diff --git a/.editorconfig b/.editorconfig new file mode 100644 index 00000000..3eb75f91 --- /dev/null +++ b/.editorconfig @@ -0,0 +1,25 @@ +# EditorConfig is awesome: http://EditorConfig.org + +# top-most EditorConfig file +root = true + +# Unix-style newlines with a newline ending every file +[*] +end_of_line = lf +insert_final_newline = true +trim_trailing_whitespace = true +# Set default charset +charset = utf-8 + +# Matches multiple files with brace expansion notation + +# 4 space indentation +[*.{py,java,r,R}] +indent_size = 4 + +# 2 space indentation +[*.{js,json,yml,html,xml}] +indent_size = 2 + +[*.{md,Rmd}] +trim_trailing_whitespace = false diff --git a/.travis.yml b/.travis.yml index 01c303c8..7db66e6f 100644 --- a/.travis.yml +++ b/.travis.yml @@ -10,7 +10,7 @@ env: global: - PREFIX=gtcg/xenon - BAM=data/test.bam - - CHROM="12 22" + - SEQIDS="12,22" matrix: - PORT=10022 IMG=gridengine TAG=6 SCH=local - PORT=10023 IMG=gridengine TAG=6 SCH=gridengine @@ -23,4 +23,4 @@ install: - docker exec -t $SCH chown -R xenon:xenon /home/xenon - docker exec -u xenon -t $SCH ./install.sh script: - - docker exec -u xenon -t $SCH ./run.sh $SCH $BAM $CHROM + - docker exec -u xenon -t $SCH ./run.sh $SCH $BAM $SEQIDS diff --git a/data/delly.bedpe b/data/delly.bedpe deleted file mode 100644 index 999eddd3..00000000 --- a/data/delly.bedpe +++ /dev/null @@ -1,5138 +0,0 @@ -12 1634 1639 12 1944 1949 DEL -12 4750 4759 12 4892 4901 DEL -12 7521 7524 12 7755 7758 DEL -12 11361 11368 12 11425 11432 DEL -12 39904 39911 12 39905 39912 INS -12 43958 43971 12 44047 44060 DEL -12 48708 48713 12 48870 48875 DEL -12 50247 50252 12 50601 50606 DEL -12 51408 51415 12 51409 51416 INS -12 52167 52172 12 52396 52401 DEL -12 53941 53948 12 54029 54036 DEL -12 56842 56851 12 56843 56852 INS -12 58103 58108 12 58104 58109 INS -12 59667 59674 12 59760 59767 DEL -12 59841 59846 12 59914 59919 DEL -12 61244 61249 12 61358 61363 DEL -12 67485 67490 12 67559 67564 DEL -12 68745 68752 12 69067 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-22 1653532 1653539 22 1653694 1653701 DEL -22 1667770 1667775 22 1667919 1667924 DEL -22 1669323 1669328 22 1669324 1669329 INS -22 1670344 1670351 22 1670478 1670485 DEL -22 1671137 1671142 22 1671381 1671386 DEL -22 1672270 1672275 22 1672762 1672767 DEL -22 1673280 1673285 22 1673551 1673556 DEL -22 1675372 1675379 22 1675639 1675646 DEL -22 1677319 1677322 22 1677524 1677527 DEL -22 1680693 1680700 22 1680813 1680820 DEL -22 1681117 1681122 22 1681478 1681483 DEL -22 1693395 1693402 22 1693537 1693544 DEL -22 1698058 1698061 22 1698158 1698161 DEL -22 1700079 1700084 22 1700441 1700446 DEL -22 1700630 1700637 22 1700788 1700795 DEL -22 1702419 1702428 22 1702700 1702709 DEL -22 1705279 1705288 22 1705454 1705463 DEL -22 1705644 1705647 22 1706036 1706039 DEL -22 1708405 1708422 22 1708406 1708423 INS -22 1708705 1708714 22 1709012 1709021 DEL -22 1710031 1710036 22 1710237 1710242 DEL -22 1716872 1716877 22 1717041 1717046 DEL -22 1718784 1718791 22 1718849 1718856 DEL -22 1720280 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INV -12 137132 137133 12 137453 137454 INV -12 138341 138342 12 138447 138448 INV -12 139051 139052 12 139303 139304 DEL -12 139775 139777 12 140146 140148 DEL -12 142360 142361 12 1477755 1477756 DEL -12 142507 142508 22 1066109 1066110 TRA -12 142932 142934 22 1280458 1280460 TRA -12 143336 143337 22 1280862 1280863 TRA -12 143478 143480 22 1801677 1801679 TRA -12 143479 143481 22 1801676 1801678 TRA -12 143613 143616 22 1801812 1801815 TRA -12 143614 143617 22 1801811 1801814 TRA -12 144118 144121 12 144524 144527 DUP -12 144903 144904 12 145240 145241 INV -12 144904 144905 12 145241 145242 INV -12 146097 146101 22 1883728 1883732 TRA -12 146096 146100 22 1883729 1883733 TRA -12 146358 146359 22 1883991 1883992 TRA -12 147046 147048 12 147422 147424 DUP -12 151198 151199 12 151404 151405 DUP -12 151562 151563 22 980718 980719 TRA -12 151979 151980 22 980717 980718 TRA -12 152241 152242 22 980981 980982 TRA -12 152369 152370 22 1732222 1732223 TRA -12 153139 153140 22 1732490 1732491 TRA -12 153402 153404 12 153896 153898 DEL -12 154494 154495 22 483918 483919 TRA -12 155205 155206 12 441776 441777 DUP -12 155618 155619 22 426434 426435 TRA -12 156050 156051 12 156541 156542 DEL -12 156887 156888 12 157380 157381 DUP -12 157537 157540 12 157818 157821 DUP -12 158331 158332 22 881782 881783 TRA -12 158432 158433 12 158564 158565 DEL -12 159038 159040 22 1623282 1623284 TRA -12 159131 159132 22 738835 738836 TRA -12 159622 159623 22 739742 739743 TRA -12 159622 159624 22 739326 739328 TRA -12 161953 161954 12 162102 162103 DUP -12 162319 162320 22 1295278 1295279 TRA -12 162539 162541 22 1295496 1295498 TRA -12 162721 162722 22 858682 858683 TRA -12 162932 162933 12 952678 952679 DUP -12 164076 164078 12 164136 164138 DEL -12 164279 164281 22 1056273 1056275 TRA -12 164280 164282 22 1056272 1056274 TRA -12 164675 164679 22 1056667 1056671 TRA -12 164674 164678 22 1056668 1056672 TRA -12 165040 165043 12 165310 165313 DUP -12 168088 168089 22 1994747 1994748 TRA -12 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1021256 22 1551366 1551367 TRA -12 1021494 1021495 12 1641539 1641540 DUP -12 1021616 1021618 22 1551729 1551731 TRA -12 1021721 1021726 12 1022156 1022161 INV -12 1022159 1022160 22 1551729 1551730 TRA -12 1022428 1022429 12 1022588 1022589 INV -12 1023464 1023466 12 1023612 1023614 INV -12 1024077 1024078 12 1024511 1024512 INV -12 1024171 1024172 12 1024512 1024513 INV -12 1025082 1025083 22 777474 777475 TRA -12 1025414 1025415 22 777804 777805 TRA -12 1025919 1025920 22 1478219 1478220 TRA -12 1026122 1026123 22 1478218 1478219 TRA -12 1026396 1026398 22 1478492 1478494 TRA -12 1026395 1026397 22 1478493 1478495 TRA -12 1026767 1026768 12 1026768 1026769 INS -12 1027683 1027684 12 1027833 1027834 INV -12 1028118 1028119 12 1028944 1028945 DUP -12 1029600 1029601 22 637078 637079 TRA -12 1029877 1029879 22 637355 637357 TRA -12 1029876 1029878 22 637356 637358 TRA -12 1030247 1030248 22 103880 103881 TRA -12 1030538 1030539 22 104173 104174 TRA -12 1030928 1030931 22 1380762 1380765 TRA -12 1031600 1031602 22 1258330 1258332 TRA -12 1031671 1031673 22 1258399 1258401 TRA -12 1033412 1033417 12 1033536 1033541 INV -12 1033413 1033418 12 1033537 1033542 INV -12 1034645 1034646 22 1176029 1176030 TRA -12 1035148 1035149 22 1176028 1176029 TRA -12 1035372 1035374 22 1176254 1176256 TRA -12 1035421 1035423 22 366170 366172 TRA -12 1035535 1035537 22 366286 366288 TRA -12 1036428 1036430 12 1036537 1036539 DUP -12 1037143 1037146 22 1534800 1534803 TRA -12 1037144 1037147 22 1534799 1534802 TRA -12 1037628 1037635 22 1535283 1535290 TRA -12 1037633 1037634 22 1535679 1535680 TRA -12 1038446 1038448 12 1038543 1038545 DUP -12 1038553 1038554 22 1172150 1172151 TRA -12 1038554 1038555 22 1172149 1172150 TRA -12 1039248 1039251 12 1039384 1039387 DEL -12 1039690 1039691 22 1293326 1293327 TRA -12 1040008 1040010 22 1293325 1293327 TRA -12 1040416 1040418 22 1293735 1293737 TRA -12 1041418 1041421 22 1179942 1179945 TRA -12 1041603 1041604 12 1514556 1514557 DEL -12 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-12 1054577 1054580 22 554439 554442 TRA -12 1054778 1054779 22 680139 680140 TRA -12 1055103 1055104 12 1055417 1055418 DUP -12 1056304 1056305 12 1056601 1056602 INV -12 1056305 1056306 12 1056602 1056603 INV -12 1058220 1058223 22 886141 886144 TRA -12 1058332 1058333 22 886255 886256 TRA -12 1058591 1058592 22 1104551 1104552 TRA -12 1058738 1058739 22 771822 771823 TRA -12 1059262 1059263 22 1920043 1920044 TRA -12 1059439 1059441 22 857175 857177 TRA -12 1059440 1059441 22 1920043 1920044 TRA -12 1060240 1060241 12 1060383 1060384 DUP -12 1060936 1060937 12 1061119 1061120 INV -12 1062083 1062085 22 818841 818843 TRA -12 1063319 1063320 12 1063421 1063422 INV -12 1063527 1063528 22 1047730 1047731 TRA -12 1063740 1063741 22 1047945 1047946 TRA -12 1063924 1063925 22 144366 144367 TRA -12 1064134 1064137 22 144652 144655 TRA -12 1065328 1065329 22 689748 689749 TRA -12 1065406 1065407 22 689824 689825 TRA -12 1065893 1065894 22 899766 899767 TRA -12 1066355 1066357 22 900204 900206 TRA -12 1066668 1066672 22 1236245 1236249 TRA -12 1067339 1067340 12 1068088 1068089 DUP -12 1068317 1068318 12 1068580 1068581 DUP -12 1069108 1069109 22 1791612 1791613 TRA -12 1069469 1069470 22 1232905 1232906 TRA -12 1070243 1070244 12 1070671 1070672 INV -12 1070244 1070245 22 1558515 1558516 TRA -12 1070937 1070941 22 1558512 1558516 TRA -12 1071348 1071349 22 1559124 1559125 TRA -12 1071349 1071350 22 1558924 1558925 TRA -12 1071447 1071449 12 1071734 1071736 DUP -12 1071772 1071775 12 1072171 1072174 DEL -12 1073803 1073804 22 321911 321912 TRA -12 1073804 1073805 22 321910 321911 TRA -12 1073982 1073983 22 322090 322091 TRA -12 1073983 1073984 22 322089 322090 TRA -12 1074154 1074155 12 1074646 1074647 DUP -12 1079493 1079495 12 1079981 1079983 DUP -12 1080866 1080867 22 852423 852424 TRA -12 1080867 1080868 22 852422 852423 TRA -12 1081172 1081174 12 1964162 1964164 DUP -12 1081406 1081408 12 1081585 1081587 DEL -12 1082046 1082048 12 1082390 1082392 DUP -12 1084046 1084047 12 1084111 1084112 DUP -12 1084292 1084294 12 1084649 1084651 DEL -12 1084970 1084971 12 1085452 1085453 DUP -12 1085591 1085592 22 325669 325670 TRA -12 1086066 1086067 22 325965 325966 TRA -12 1086067 1086068 22 325964 325965 TRA -12 1086657 1086660 12 1086757 1086760 DUP -12 1087081 1087082 22 1090327 1090328 TRA -12 1087741 1087745 12 1088116 1088120 INV -12 1088333 1088335 12 1088596 1088598 DEL -12 1090163 1090164 12 1090562 1090563 DUP -12 1090903 1090904 12 1091045 1091046 INV -12 1091295 1091296 22 354175 354176 TRA -12 1091296 1091297 22 353837 353838 TRA -12 1091452 1091453 22 354332 354333 TRA -12 1091662 1091664 12 1091789 1091791 DUP -12 1092364 1092370 12 1092804 1092810 DUP -12 1093101 1093102 12 1093519 1093520 DUP -12 1093558 1093559 12 1093671 1093672 DUP -12 1094228 1094230 22 1624098 1624100 TRA -12 1094228 1094229 22 1692497 1692498 TRA -12 1094574 1094577 22 1624446 1624449 TRA -12 1094635 1094638 22 1692850 1692853 TRA -12 1095065 1095067 12 1095506 1095508 DUP -12 1095608 1095609 12 1095676 1095677 INV -12 1096084 1096085 22 502413 502414 TRA -12 1096492 1096496 22 502823 502827 TRA -12 1097846 1097847 22 331709 331710 TRA -12 1098599 1098600 22 680972 680973 TRA -12 1099071 1099073 12 1099200 1099202 DEL -12 1100042 1100043 22 806916 806917 TRA -12 1100043 1100044 22 806668 806669 TRA -12 1100508 1100512 22 807380 807384 TRA -12 1100507 1100511 22 807381 807385 TRA -12 1100522 1100524 12 1100681 1100683 DUP -12 1101135 1101137 12 1101294 1101296 INV -12 1101136 1101138 12 1101295 1101297 INV -12 1102133 1102134 22 1930921 1930922 TRA -12 1102134 1102135 22 1930920 1930921 TRA -12 1102618 1102621 22 1931404 1931407 TRA -12 1102619 1102620 22 1931407 1931408 TRA -12 1102698 1102699 12 1102926 1102927 DUP -12 1103491 1103494 12 1103607 1103610 INV -12 1104140 1104142 12 1104288 1104290 DUP -12 1106250 1106251 12 1106387 1106388 INV -12 1107144 1107145 22 1542914 1542915 TRA -12 1107405 1107406 22 1755919 1755920 TRA -12 1107500 1107502 22 1542913 1542915 TRA -12 1107846 1107847 22 1543261 1543262 TRA -12 1107869 1107871 12 1108240 1108242 DUP -12 1108475 1108476 12 1108604 1108605 INV -12 1108671 1108673 22 423127 423129 TRA -12 1109410 1109412 12 1109571 1109573 INV -12 1109615 1109617 12 1109709 1109711 DUP -12 1110035 1110036 12 1110282 1110283 INV -12 1110036 1110037 12 1110283 1110284 INV -12 1110532 1110533 12 1110615 1110616 INV -12 1111728 1111731 22 1660950 1660953 TRA -12 1111959 1111960 22 1661268 1661269 TRA -12 1111960 1111961 22 646042 646043 TRA -12 1112101 1112102 22 1661267 1661268 TRA -12 1113856 1113859 22 968447 968450 TRA -12 1113857 1113860 22 968446 968449 TRA -12 1113943 1113944 22 968534 968535 TRA -12 1115452 1115453 22 631740 631741 TRA -12 1116386 1116387 22 1249558 1249559 TRA -12 1116452 1116453 12 1116632 1116633 DEL -12 1117646 1117647 22 1450095 1450096 TRA -12 1118094 1118096 22 1450543 1450545 TRA -12 1118095 1118097 22 1450542 1450544 TRA -12 1118558 1118560 12 1118795 1118797 DEL -12 1119106 1119107 22 1179032 1179033 TRA -12 1119107 1119108 22 1179031 1179032 TRA -12 1119313 1119314 22 24712 24713 TRA -12 1119385 1119386 22 24786 24787 TRA -12 1119386 1119387 22 24785 24786 TRA -12 1120497 1120498 12 1120785 1120786 DEL -12 1121847 1121849 22 1567740 1567742 TRA -12 1121848 1121850 22 1567739 1567741 TRA -12 1122100 1122102 22 1567991 1567993 TRA -12 1123445 1123446 12 1123689 1123690 INV -12 1123688 1123689 22 418525 418526 TRA -12 1123742 1123743 12 1124056 1124057 DUP -12 1125307 1125309 12 1125623 1125625 DUP -12 1126177 1126178 22 36521 36522 TRA -12 1126286 1126287 22 36628 36629 TRA -12 1128068 1128069 12 1128069 1128070 INS -12 1128223 1128224 22 1396380 1396381 TRA -12 1128224 1128225 22 1396379 1396380 TRA -12 1128692 1128693 12 1128919 1128920 DUP -12 1129310 1129313 22 644046 644049 TRA -12 1129311 1129314 22 644045 644048 TRA -12 1129698 1129700 22 644434 644436 TRA -12 1129699 1129701 22 644433 644435 TRA -12 1130643 1130644 12 1131038 1131039 INV -12 1130644 1130645 12 1131039 1131040 INV -12 1132078 1132080 12 1132290 1132292 DEL -12 1133374 1133375 12 1134291 1134292 INV -12 1133838 1133839 12 1134292 1134293 INV -12 1134908 1134909 12 1135190 1135191 DUP -12 1135573 1135575 22 1932657 1932659 TRA -12 1135574 1135576 22 1932656 1932658 TRA -12 1136072 1136074 22 1933156 1933158 TRA -12 1136113 1136114 22 1795316 1795317 TRA -12 1136474 1136477 22 1795677 1795680 TRA -12 1138298 1138300 22 444179 444181 TRA -12 1138299 1138301 22 444178 444180 TRA -12 1138383 1138384 22 444262 444263 TRA -12 1138592 1138594 12 1138872 1138874 DEL -12 1138978 1138979 12 1138979 1138980 INS -12 1139791 1139792 12 1140245 1140246 INV -12 1143839 1143840 22 176614 176615 TRA -12 1144131 1144135 22 176904 176908 TRA -12 1144350 1144351 12 1144419 1144420 DUP -12 1145141 1145142 12 1145458 1145459 DUP -12 1145567 1145568 22 338277 338278 TRA -12 1145568 1145569 22 338276 338277 TRA -12 1145985 1145988 22 338693 338696 TRA -12 1146715 1146716 12 1146854 1146855 INV -12 1146716 1146717 12 1146855 1146856 INV -12 1148273 1148274 22 1339890 1339891 TRA -12 1148274 1148275 22 1339889 1339890 TRA -12 1148741 1148742 22 1340358 1340359 TRA -12 1148742 1148743 22 1340357 1340358 TRA -12 1149930 1149932 12 1150037 1150039 DUP -12 1150715 1150718 12 1151013 1151016 DUP -12 1151253 1151254 22 960076 960077 TRA -12 1151317 1151319 22 1970083 1970085 TRA -12 1151373 1151374 22 561497 561498 TRA -12 1151464 1151465 22 1364795 1364796 TRA -12 1152476 1152477 12 1152935 1152936 DEL -12 1153228 1153229 12 1153557 1153558 DEL -12 1154735 1154740 12 1155112 1155117 DEL -12 1155309 1155310 12 1155762 1155763 DUP -12 1155833 1155837 22 1121364 1121368 TRA -12 1155834 1155838 22 1121363 1121367 TRA -12 1156304 1156305 22 1122336 1122337 TRA -12 1156305 1156306 22 1121834 1121835 TRA -12 1156677 1156678 12 1157169 1157170 INV -12 1156678 1156679 22 954395 954396 TRA -12 1157206 1157207 22 954394 954395 TRA -12 1157498 1157499 22 954688 954689 TRA -12 1157770 1157771 22 954687 954688 TRA -12 1159952 1159953 12 1160015 1160016 INV -12 1160805 1160806 12 1161289 1161290 INV -12 1160806 1160807 12 1161290 1161291 INV -12 1161533 1161534 22 355808 355809 TRA -12 1161534 1161535 22 355807 355808 TRA -12 1161838 1161841 22 356113 356116 TRA -12 1162061 1162062 12 1162430 1162431 DUP -12 1162779 1162780 22 798342 798343 TRA -12 1162859 1162860 12 1163281 1163282 INV -12 1164977 1164980 12 1165165 1165168 INV -12 1164978 1164981 12 1165166 1165169 INV -12 1168790 1168793 12 1169158 1169161 DUP -12 1169826 1169828 12 1170312 1170314 DUP -12 1170487 1170489 22 314998 315000 TRA -12 1170488 1170490 22 314997 314999 TRA -12 1170664 1170665 22 315079 315080 TRA -12 1171015 1171016 12 1171097 1171098 DEL -12 1171312 1171320 12 1171565 1171573 INV -12 1172920 1172924 22 555375 555379 TRA -12 1173953 1173954 22 1567332 1567333 TRA -12 1176453 1176455 22 793910 793912 TRA -12 1176454 1176456 22 793909 793911 TRA -12 1176916 1176918 22 1453270 1453272 TRA -12 1177039 1177040 12 1286250 1286251 DEL -12 1177309 1177310 22 1453663 1453664 TRA -12 1177310 1177317 22 1453662 1453669 TRA -12 1177667 1177668 12 1177736 1177737 DEL -12 1177922 1177923 12 1178355 1178356 DEL -12 1179492 1179494 12 1179670 1179672 DUP -12 1180208 1180212 12 1180665 1180669 DEL -12 1181274 1181277 12 1181391 1181394 DUP -12 1182161 1182162 12 1182374 1182375 DUP -12 1183925 1183927 22 206193 206195 TRA -12 1184459 1184462 12 1184558 1184561 DEL -12 1184990 1184991 12 1185162 1185163 DUP -12 1185313 1185318 22 640605 640610 TRA -12 1185314 1185319 22 640604 640609 TRA -12 1185750 1185753 22 641040 641043 TRA -12 1185751 1185752 22 641338 641339 TRA -12 1186246 1186247 22 1546861 1546862 TRA -12 1186952 1186960 22 1547046 1547054 TRA -12 1187308 1187309 12 1187782 1187783 DUP -12 1187845 1187846 12 1187901 1187902 INV -12 1188916 1188919 12 1189134 1189137 DUP -12 1189741 1189742 22 1112901 1112902 TRA -12 1189742 1189743 22 1112900 1112901 TRA -12 1190349 1190351 12 1190780 1190782 DEL -12 1192540 1192541 12 1192682 1192683 INV -12 1192880 1192881 12 1193039 1193040 INV -12 1194273 1194274 22 1425941 1425942 TRA -12 1194274 1194275 12 1948220 1948221 DUP -12 1194274 1194275 22 1425940 1425941 TRA -12 1194469 1194471 22 1426135 1426137 TRA -12 1195382 1195383 22 509607 509608 TRA -12 1195549 1195550 22 1343123 1343124 TRA -12 1195550 1195551 12 1418399 1418400 DUP -12 1195667 1195669 22 509892 509894 TRA -12 1195963 1195966 12 1196372 1196375 DEL -12 1197034 1197035 12 1197399 1197400 INV -12 1197035 1197036 12 1197400 1197401 INV -12 1198009 1198013 12 1198200 1198204 DUP -12 1198362 1198364 12 1198673 1198675 INV -12 1199588 1199592 12 1199658 1199662 INV -12 1199727 1199728 22 1349815 1349816 TRA -12 1200005 1200010 22 1350095 1350100 TRA -12 1200242 1200243 12 1200662 1200663 DUP -12 1200722 1200723 12 1201108 1201109 INV -12 1201201 1201203 22 755264 755266 TRA -12 1201476 1201477 22 755539 755540 TRA -12 1201673 1201676 12 1201770 1201773 DEL -12 1202423 1202424 22 1296372 1296373 TRA -12 1202424 1202425 22 1296371 1296372 TRA -12 1202567 1202570 22 1296516 1296519 TRA -12 1202568 1202571 22 1296515 1296518 TRA -12 1203966 1203967 22 303600 303601 TRA -12 1203967 1203968 22 303599 303600 TRA -12 1204047 1204052 22 303679 303684 TRA -12 1205087 1205090 12 1205386 1205389 DUP -12 1205572 1205574 22 5484 5486 TRA -12 1205713 1205716 22 5623 5626 TRA -12 1205713 1205715 22 5625 5627 TRA -12 1206387 1206388 12 1206577 1206578 DUP -12 1207297 1207298 12 1207660 1207661 DUP -12 1208435 1208436 12 1208813 1208814 DEL -12 1210333 1210334 22 548857 548858 TRA -12 1210334 1210335 22 548856 548857 TRA -12 1210742 1210743 22 549119 549120 TRA -12 1212317 1212318 22 990686 990687 TRA -12 1212318 1212319 22 990685 990686 TRA -12 1212400 1212402 22 990769 990771 TRA -12 1212401 1212403 22 990768 990770 TRA -12 1213340 1213343 12 1213498 1213501 INV -12 1213846 1213849 22 875509 875512 TRA -12 1214101 1214102 12 1946661 1946662 DEL -12 1215632 1215634 12 1215914 1215916 DEL -12 1216382 1216383 12 1216675 1216676 DEL -12 1217238 1217239 12 1217430 1217431 DEL -12 1217589 1217590 12 1218006 1218007 INV -12 1218444 1218445 22 1865941 1865942 TRA -12 1218795 1218796 12 1992450 1992451 DUP -12 1220525 1220526 22 424989 424990 TRA -12 1220526 1220527 22 823495 823496 TRA -12 1220820 1220821 22 1488998 1488999 TRA -12 1221039 1221041 22 1489217 1489219 TRA -12 1221324 1221328 12 1221572 1221576 DUP -12 1222188 1222189 12 1222327 1222328 DEL -12 1222890 1222891 22 800308 800309 TRA -12 1222891 1222892 22 800061 800062 TRA -12 1223339 1223340 22 800757 800758 TRA -12 1223340 1223341 22 800756 800757 TRA -12 1223659 1223662 12 1223938 1223941 DEL -12 1225933 1225934 22 124441 124442 TRA -12 1226145 1226149 22 826955 826959 TRA -12 1226146 1226150 22 826954 826958 TRA -12 1226474 1226476 22 827284 827286 TRA -12 1226680 1226681 12 1226794 1226795 INV -12 1226927 1226929 22 1433429 1433431 TRA -12 1227547 1227548 22 930418 930419 TRA -12 1227819 1227820 22 930800 930801 TRA -12 1229585 1229588 22 187684 187687 TRA -12 1229723 1229725 22 187824 187826 TRA -12 1229724 1229726 22 187823 187825 TRA -12 1230369 1230370 22 76943 76944 TRA -12 1230370 1230371 22 76942 76943 TRA -12 1230478 1230479 22 77052 77053 TRA -12 1230490 1230492 22 998175 998177 TRA -12 1231506 1231507 12 1231881 1231882 DUP -12 1233429 1233430 12 1233508 1233509 DEL -12 1235094 1235095 22 574296 574297 TRA -12 1235095 1235096 22 574295 574296 TRA -12 1235265 1235266 12 1785301 1785302 DEL -12 1235708 1235709 12 1235849 1235850 DEL -12 1235946 1235948 12 1236355 1236357 DEL -12 1237234 1237235 12 1237465 1237466 DEL -12 1238711 1238714 12 1239136 1239139 INV -12 1238712 1238718 12 1239134 1239140 INV -12 1241144 1241147 12 1241259 1241262 DUP -12 1241633 1241635 12 1241764 1241766 DUP -12 1242310 1242311 12 1242426 1242427 INV -12 1242311 1242312 12 1242427 1242428 INV -12 1243819 1243820 22 1226356 1226357 TRA -12 1243950 1243951 12 1244035 1244036 INV -12 1244821 1244823 12 1245202 1245204 DUP -12 1245502 1245504 22 1140200 1140202 TRA -12 1245559 1245560 22 1140257 1140258 TRA -12 1247381 1247384 12 1247575 1247578 DEL -12 1248325 1248329 12 1248682 1248686 INV -12 1248324 1248328 12 1248681 1248685 INV -12 1248974 1248976 12 1249302 1249304 DUP -12 1250281 1250282 12 1250482 1250483 INV -12 1250282 1250283 12 1250483 1250484 INV -12 1252180 1252182 12 1252629 1252631 DEL -12 1253031 1253032 12 1253099 1253100 DUP -12 1254411 1254413 22 385912 385914 TRA -12 1254642 1254643 22 385912 385913 TRA -12 1255481 1255482 12 1255773 1255774 DUP -12 1256638 1256639 22 1082080 1082081 TRA -12 1258051 1258052 12 1258757 1258758 INV -12 1260054 1260056 22 930051 930053 TRA -12 1261496 1261498 22 107479 107481 TRA -12 1261960 1261961 12 1677454 1677455 INV -12 1261960 1261962 22 107941 107943 TRA -12 1262770 1262771 12 1262863 1262864 INV -12 1263534 1263536 12 1263799 1263801 DEL -12 1264192 1264193 12 1264547 1264548 DUP -12 1264586 1264587 12 1264976 1264977 INV -12 1266391 1266392 12 1266614 1266615 DUP -12 1267241 1267242 12 1267313 1267314 INV -12 1267978 1267980 12 1268181 1268183 INV -12 1267979 1267981 12 1268182 1268184 INV -12 1268624 1268625 12 1268969 1268970 DEL -12 1270154 1270156 12 1270432 1270434 DUP -12 1271889 1271890 12 1272178 1272179 DEL -12 1272598 1272601 22 1335709 1335712 TRA -12 1272773 1272776 12 1272846 1272849 DUP -12 1273652 1273653 12 1273984 1273985 DUP -12 1274115 1274117 12 1274186 1274188 DUP -12 1274631 1274632 12 1274949 1274950 INV -12 1274632 1274633 12 1274950 1274951 INV -12 1276052 1276053 12 1276427 1276428 DUP -12 1276925 1276926 12 1276992 1276993 DUP -12 1277445 1277446 22 829895 829896 TRA -12 1277760 1277761 22 830208 830209 TRA -12 1277937 1277938 12 1278095 1278096 DUP -12 1278282 1278284 12 1278438 1278440 DEL -12 1279842 1279843 22 431221 431222 TRA -12 1281497 1281500 22 1884700 1884703 TRA -12 1281498 1281501 22 1884699 1884702 TRA -12 1282448 1282449 12 1282817 1282818 DEL -12 1283989 1283990 12 1284051 1284052 INV -12 1284358 1284359 22 1303456 1303457 TRA -12 1284357 1284360 22 1304020 1304023 TRA -12 1284708 1284709 22 1304801 1304802 TRA -12 1284840 1284841 12 1285110 1285111 DEL -12 1285363 1285364 12 1285812 1285813 DUP -12 1286720 1286721 22 1704354 1704355 TRA -12 1287182 1287183 22 1704520 1704521 TRA -12 1288402 1288404 12 1288593 1288595 INV -12 1288916 1288918 12 1289237 1289239 DUP -12 1289721 1289722 12 1290012 1290013 DUP -12 1290969 1290970 12 1291312 1291313 DUP -12 1291650 1291651 12 1291960 1291961 DEL -12 1292384 1292386 22 292447 292449 TRA -12 1292615 1292616 22 292680 292681 TRA -12 1292763 1292765 22 264098 264100 TRA -12 1292764 1292766 22 264097 264099 TRA -12 1293262 1293264 22 264597 264599 TRA -12 1293263 1293265 22 264596 264598 TRA -12 1293633 1293644 12 1294006 1294017 DEL -12 1295652 1295653 22 1887035 1887036 TRA -12 1295889 1295890 22 1887274 1887275 TRA -12 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TRA -12 1309396 1309398 22 562218 562220 TRA -12 1309397 1309399 22 562217 562219 TRA -12 1309557 1309558 22 562379 562380 TRA -12 1310206 1310207 12 1310207 1310208 INS -12 1310624 1310626 12 1311011 1311013 DUP -12 1312083 1312084 12 1312478 1312479 INV -12 1312084 1312085 12 1312479 1312480 INV -12 1313206 1313207 12 1313472 1313473 DUP -12 1314284 1314286 12 1314595 1314597 DUP -12 1315346 1315350 12 1315493 1315497 DEL -12 1315859 1315861 22 314604 314606 TRA -12 1316152 1316153 22 314872 314873 TRA -12 1316820 1316821 12 1317333 1317334 DEL -12 1318021 1318024 12 1318438 1318441 DEL -12 1318657 1318659 12 1318968 1318970 DUP -12 1319228 1319229 12 1319721 1319722 DEL -12 1319874 1319876 12 1320237 1320239 DUP -12 1321665 1321666 22 1152886 1152887 TRA -12 1321899 1321900 22 1417493 1417494 TRA -12 1322092 1322093 22 1153313 1153314 TRA -12 1322093 1322094 22 1153312 1153313 TRA -12 1322440 1322441 12 1322704 1322705 INV -12 1322441 1322442 12 1322705 1322706 INV -12 1322813 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DEL -12 1370258 1370261 12 1370525 1370528 DUP -12 1371288 1371290 22 992584 992586 TRA -12 1371441 1371442 22 992737 992738 TRA -12 1372475 1372476 22 696179 696180 TRA -12 1372476 1372477 22 696178 696179 TRA -12 1372690 1372693 22 696394 696397 TRA -12 1374149 1374150 12 1374292 1374293 INV -12 1374150 1374151 12 1374293 1374294 INV -12 1374701 1374703 12 1375181 1375183 DUP -12 1375489 1375490 22 173862 173863 TRA -12 1375737 1375738 22 174110 174111 TRA -12 1375738 1375739 22 174109 174110 TRA -12 1375843 1375844 12 1375844 1375845 INS -12 1378910 1378913 12 1379271 1379274 DUP -12 1379716 1379718 22 1502650 1502652 TRA -12 1379715 1379717 22 1502651 1502653 TRA -12 1380200 1380201 12 1380294 1380295 DUP -12 1380326 1380329 22 1421865 1421868 TRA -12 1380327 1380330 22 1421864 1421867 TRA -12 1380513 1380514 22 1422052 1422053 TRA -12 1381182 1381183 12 1381443 1381444 DEL -12 1381834 1381835 12 1382252 1382253 INV -12 1381835 1381836 12 1382253 1382254 INV -12 1382584 1382585 12 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1343124 TRA -12 1418735 1418736 22 1343280 1343281 TRA -12 1419214 1419215 22 1343280 1343281 TRA -12 1421626 1421627 12 1421806 1421807 DEL -12 1422096 1422097 22 857755 857756 TRA -12 1422228 1422229 22 153203 153204 TRA -12 1422947 1422955 12 1423124 1423132 INV -12 1426066 1426069 12 1426324 1426327 INV -12 1426686 1426687 22 149799 149800 TRA -12 1427868 1427870 22 496561 496563 TRA -12 1428165 1428171 22 496858 496864 TRA -12 1428288 1428289 12 1428578 1428579 DUP -12 1429554 1429555 22 1379859 1379860 TRA -12 1429555 1429556 22 1379858 1379859 TRA -12 1429639 1429640 22 1379944 1379945 TRA -12 1430513 1430514 12 1430833 1430834 DUP -12 1431130 1431131 22 1217462 1217463 TRA -12 1431287 1431288 22 1217619 1217620 TRA -12 1432116 1432117 22 653702 653703 TRA -12 1434338 1434339 12 1434648 1434649 INV -12 1435794 1435796 12 1436230 1436232 DUP -12 1437389 1437391 12 1437660 1437662 INV -12 1437942 1437943 12 1438024 1438025 INV -12 1438569 1438571 22 1708013 1708015 TRA -12 1438757 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-12 1476606 1476609 22 1779253 1779256 TRA -12 1476607 1476610 22 1779252 1779255 TRA -12 1476674 1476675 12 1477031 1477032 DUP -12 1477757 1477758 22 1394182 1394183 TRA -12 1478191 1478192 22 1394614 1394615 TRA -12 1479360 1479361 22 1423181 1423182 TRA -12 1479771 1479773 12 1480233 1480235 DEL -12 1480587 1480588 12 1480925 1480926 INV -12 1480588 1480589 12 1481169 1481170 INV -12 1482399 1482401 22 919763 919765 TRA -12 1482551 1482556 22 919917 919922 TRA -12 1482552 1482557 22 919916 919921 TRA -12 1482860 1482861 22 1360042 1360043 TRA -12 1482861 1482862 22 1360041 1360042 TRA -12 1482956 1482957 22 1360138 1360139 TRA -12 1484347 1484349 12 1484809 1484811 DEL -12 1486068 1486070 12 1486497 1486499 DUP -12 1487304 1487305 22 73177 73178 TRA -12 1487305 1487306 22 73176 73177 TRA -12 1487724 1487725 22 73636 73637 TRA -12 1488544 1488545 22 1034077 1034078 TRA -12 1488674 1488678 22 1040185 1040189 TRA -12 1488676 1488677 22 1039598 1039599 TRA -12 1489008 1489011 22 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-12 1534417 1534420 12 1534668 1534671 DUP -12 1534756 1534757 22 825435 825436 TRA -12 1534757 1534758 22 825434 825435 TRA -12 1534938 1534941 22 825617 825620 TRA -12 1534939 1534942 22 825616 825619 TRA -12 1536849 1536853 22 154794 154798 TRA -12 1536989 1536992 22 154936 154939 TRA -12 1536990 1536993 22 154935 154938 TRA -12 1537565 1537566 22 274426 274427 TRA -12 1537722 1537723 22 274426 274427 TRA -12 1537855 1537856 22 274597 274598 TRA -12 1537855 1537858 22 1484746 1484749 TRA -12 1537948 1537949 22 274596 274597 TRA -12 1537947 1537949 22 1484840 1484842 TRA -12 1538447 1538451 22 836715 836719 TRA -12 1538443 1538448 22 836716 836721 TRA -12 1538892 1538893 22 837160 837161 TRA -12 1539947 1539948 12 1539948 1539949 INS -12 1540396 1540397 22 1346417 1346418 TRA -12 1540407 1540408 12 1540560 1540561 DUP -12 1541663 1541664 12 1541941 1541942 DUP -12 1542546 1542547 22 1937424 1937425 TRA -12 1542547 1542548 22 1937423 1937424 TRA -12 1543138 1543140 12 1543607 1543609 INV -12 1543139 1543141 12 1543608 1543610 INV -12 1545280 1545281 12 1545281 1545282 INS -12 1546570 1546571 22 530327 530328 TRA -12 1547276 1547277 22 1962147 1962148 TRA -12 1548034 1548036 12 1548183 1548185 DUP -12 1548953 1548954 12 1549042 1549043 DUP -12 1549610 1549611 22 577573 577574 TRA -12 1549611 1549612 22 577572 577573 TRA -12 1550108 1550109 12 1550109 1550110 INS -12 1550596 1550597 12 1551295 1551296 DUP -12 1550910 1550911 22 1184338 1184339 TRA -12 1551438 1551439 22 1184337 1184338 TRA -12 1551675 1551677 22 1244371 1244373 TRA -12 1551675 1551676 22 1184787 1184788 TRA -12 1552002 1552003 22 1002923 1002924 TRA -12 1552075 1552076 12 1552909 1552910 INV -12 1552562 1552563 22 1184787 1184788 TRA -12 1554755 1554756 12 1554968 1554969 INV -12 1556791 1556792 22 93087 93088 TRA -12 1556792 1556793 22 93086 93087 TRA -12 1557164 1557167 22 93462 93465 TRA -12 1557167 1557170 22 93461 93464 TRA -12 1558373 1558374 12 1558585 1558586 INV -12 1558374 1558375 12 1558586 1558587 INV -12 1561506 1561508 12 1561732 1561734 DEL -12 1562142 1562143 12 1562611 1562612 DEL -12 1563067 1563069 12 1563152 1563154 DUP -12 1563292 1563296 22 1700923 1700927 TRA -12 1563785 1563786 22 1701414 1701415 TRA -12 1563786 1563787 22 1701413 1701414 TRA -12 1564756 1564760 12 1564923 1564927 DEL -12 1565218 1565219 12 1565664 1565665 INV -12 1565219 1565220 12 1565665 1565666 INV -12 1567239 1567240 12 1568199 1568200 INV -12 1567737 1567738 22 608048 608049 TRA -12 1568327 1568328 12 1568417 1568418 DEL -12 1568494 1568495 22 608047 608048 TRA -12 1568751 1568752 22 608306 608307 TRA -12 1569141 1569142 22 142287 142288 TRA -12 1569142 1569143 22 142286 142287 TRA -12 1569590 1569591 22 142736 142737 TRA -12 1569591 1569592 22 142735 142736 TRA -12 1571088 1571089 12 1571089 1571090 INS -12 1571359 1571360 12 1572020 1572021 DUP -12 1572640 1572644 22 1977496 1977500 TRA -12 1572829 1572830 22 1977685 1977686 TRA -12 1573134 1573136 22 1041601 1041603 TRA -12 1573374 1573375 22 1041843 1041844 TRA -12 1573779 1573780 22 1704063 1704064 TRA -12 1574351 1574352 12 1574658 1574659 INV -12 1574352 1574353 22 1681594 1681595 TRA -12 1574853 1574854 22 1681119 1681120 TRA -12 1575263 1575264 22 1682003 1682004 TRA -12 1576420 1576421 22 1828663 1828664 TRA -12 1576421 1576422 22 1827963 1827964 TRA -12 1577272 1577273 22 1829147 1829148 TRA -12 1577319 1577320 12 1577383 1577384 DUP -12 1580217 1580220 12 1580630 1580633 DUP -12 1580794 1580795 22 1441783 1441784 TRA -12 1581121 1581122 22 1442112 1442113 TRA -12 1581122 1581123 22 1442111 1442112 TRA -12 1582869 1582871 22 1403238 1403240 TRA -12 1583084 1583085 22 1189871 1189872 TRA -12 1584854 1584855 12 1585246 1585247 DEL -12 1586536 1586538 22 1252609 1252611 TRA -12 1586537 1586539 22 1252608 1252610 TRA -12 1587033 1587035 22 1253106 1253108 TRA -12 1587141 1587143 22 434030 434032 TRA -12 1587472 1587473 22 434363 434364 TRA -12 1587815 1587820 12 1587913 1587918 DUP -12 1588347 1588348 12 1588552 1588553 DUP -12 1588768 1588770 12 1589115 1589117 INV -12 1588767 1588769 12 1589114 1589116 INV -12 1589459 1589460 12 1589949 1589950 DUP -12 1590900 1590902 12 1591325 1591327 DEL -12 1591648 1591649 12 1591649 1591650 INS -12 1591806 1591808 12 1592076 1592078 DEL -12 1592261 1592262 12 1592459 1592460 INV -12 1592262 1592263 12 1592460 1592461 INV -12 1592854 1592855 12 1593103 1593104 DEL -12 1593761 1593762 12 1594169 1594170 DEL -12 1596937 1596939 12 1597380 1597382 INV -12 1597557 1597558 12 1597763 1597764 DEL -12 1598297 1598299 12 1598668 1598670 INV -12 1598298 1598300 12 1598669 1598671 INV -12 1599332 1599333 12 1599741 1599742 INV -12 1600348 1600349 22 1054751 1054752 TRA -12 1600349 1600350 22 1054750 1054751 TRA -12 1601171 1601174 12 1601261 1601264 DUP -12 1601318 1601319 12 1601573 1601574 DUP -12 1601813 1601815 12 1602098 1602100 DUP -12 1602334 1602335 12 1602450 1602451 INV -12 1604174 1604176 12 1604298 1604300 DEL -12 1604338 1604339 12 1604670 1604671 DUP -12 1605725 1605727 22 327733 327735 TRA -12 1606046 1606047 22 328056 328057 TRA -12 1606167 1606168 22 552482 552483 TRA -12 1606417 1606418 12 1606649 1606650 DEL -12 1606854 1606855 12 1607257 1607258 DUP -12 1607656 1607657 12 1608091 1608092 INV -12 1607657 1607658 12 1608092 1608093 INV -12 1608647 1608648 12 1608937 1608938 DEL -12 1608647 1608648 22 329135 329136 TRA -12 1609163 1609164 22 329134 329135 TRA -12 1610793 1610794 12 1610795 1610796 INS -12 1611053 1611057 12 1611118 1611122 DUP -12 1611340 1611341 12 1611862 1611863 DEL -12 1612656 1612658 12 1612780 1612782 DUP -12 1614523 1614526 12 1614953 1614956 DEL -12 1616808 1616810 12 1616949 1616951 INV -12 1617417 1617418 12 1617581 1617582 INV -12 1619279 1619281 22 1331325 1331327 TRA -12 1619278 1619280 22 1331326 1331328 TRA -12 1619755 1619759 22 1331803 1331807 TRA -12 1619978 1619980 12 1620271 1620273 INV -12 1620271 1620272 22 1331805 1331806 TRA -12 1622726 1622727 12 1623360 1623361 DEL -12 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169048 TRA -12 1637758 1637759 22 168657 168658 TRA -12 1638112 1638113 22 169810 169811 TRA -12 1640506 1640507 12 1640859 1640860 DUP -12 1640912 1640913 12 1641407 1641408 DEL -12 1641726 1641728 22 1551793 1551795 TRA -12 1641727 1641729 22 1551792 1551794 TRA -12 1642725 1642726 22 8695 8696 TRA -12 1642726 1642727 22 8694 8695 TRA -12 1643302 1643303 22 8995 8996 TRA -12 1643402 1643403 12 1643846 1643847 DUP -12 1644090 1644091 22 1083956 1083957 TRA -12 1644091 1644092 22 1083955 1083956 TRA -12 1644509 1644510 22 149753 149754 TRA -12 1644510 1644511 22 1084290 1084291 TRA -12 1645966 1645967 22 1911329 1911330 TRA -12 1645965 1645968 22 1377624 1377627 TRA -12 1646178 1646179 22 1377839 1377840 TRA -12 1646528 1646530 22 1911688 1911690 TRA -12 1646527 1646529 22 1911689 1911691 TRA -12 1647723 1647726 22 1566029 1566032 TRA -12 1647882 1647883 22 1566190 1566191 TRA -12 1648521 1648522 12 1648583 1648584 DUP -12 1648691 1648692 12 1648820 1648821 INV -12 1649122 1649125 12 1649360 1649363 DUP -12 1649370 1649376 12 1649464 1649470 INV -12 1649963 1649965 22 351197 351199 TRA -12 1650114 1650115 22 351350 351351 TRA -12 1650115 1650116 22 1682619 1682620 TRA -12 1650587 1650590 22 387679 387682 TRA -12 1650586 1650589 22 387680 387683 TRA -12 1650918 1650919 22 388108 388109 TRA -12 1652037 1652038 22 450567 450568 TRA -12 1652094 1652097 22 450624 450627 TRA -12 1652290 1652291 12 1652778 1652779 DUP -12 1653404 1653406 22 1116599 1116601 TRA -12 1653498 1653499 22 1116693 1116694 TRA -12 1653782 1653783 22 1814661 1814662 TRA -12 1654073 1654075 22 1814952 1814954 TRA -12 1654074 1654076 22 1814951 1814953 TRA -12 1654973 1654975 12 1655355 1655357 DEL -12 1657006 1657008 22 164644 164646 TRA -12 1657356 1657358 22 164996 164998 TRA -12 1658453 1658454 12 1658712 1658713 INV -12 1659014 1659017 12 1659236 1659239 DEL -12 1660797 1660800 12 1660880 1660883 DUP -12 1660985 1660986 22 1896777 1896778 TRA -12 1662457 1662458 12 1662650 1662651 INV -12 1663139 1663140 22 91291 91292 TRA -12 1663140 1663141 22 91290 91291 TRA -12 1663628 1663629 22 91780 91781 TRA -12 1663629 1663630 22 91779 91780 TRA -12 1666175 1666176 12 1666523 1666524 DUP -12 1666652 1666653 22 918081 918082 TRA -12 1666653 1666654 22 918080 918081 TRA -12 1666934 1666935 22 918361 918362 TRA -12 1667958 1667960 12 1668275 1668277 DEL -12 1668564 1668566 12 1668965 1668967 INV -12 1668563 1668565 12 1668964 1668966 INV -12 1672379 1672380 22 80046 80047 TRA -12 1672380 1672381 22 80045 80046 TRA -12 1672870 1672875 22 80541 80546 TRA -12 1672875 1672880 22 80540 80545 TRA -12 1673570 1673576 12 1673988 1673994 INV -12 1673569 1673575 12 1673987 1673993 INV -12 1675393 1675394 12 1675473 1675474 DEL -12 1675976 1675977 12 1676281 1676282 DEL -12 1676990 1676992 22 108076 108078 TRA -12 1676990 1676991 22 108130 108131 TRA -12 1677098 1677099 12 1677901 1677902 DUP -12 1677513 1677515 12 1677901 1677903 INV -12 1678052 1678053 22 176265 176266 TRA -12 1678187 1678188 22 176400 176401 TRA -12 1679092 1679093 12 1679254 1679255 DUP -12 1679658 1679659 22 811311 811312 TRA -12 1679997 1679998 22 812497 812498 TRA -12 1679997 1679999 22 812045 812047 TRA -12 1680311 1680312 22 1048973 1048974 TRA -12 1680508 1680509 22 1049172 1049173 TRA -12 1680956 1680958 22 297250 297252 TRA -12 1681078 1681079 12 1681257 1681258 DEL -12 1682867 1682868 22 1430852 1430853 TRA -12 1683340 1683341 22 1718443 1718444 TRA -12 1683405 1683406 12 1683477 1683478 INV -12 1684673 1684675 12 1685052 1685054 DUP -12 1686036 1686037 12 1686507 1686508 INV -12 1686037 1686038 12 1686654 1686655 INV -12 1688700 1688702 12 1688822 1688824 DEL -12 1689218 1689219 12 1689427 1689428 DEL -12 1690167 1690168 22 442530 442531 TRA -12 1690168 1690169 22 442529 442530 TRA -12 1690576 1690578 12 1690682 1690684 INV -12 1690937 1690938 12 1691204 1691205 DUP -12 1692063 1692065 12 1692123 1692125 DUP -12 1692439 1692440 22 328578 328579 TRA -12 1692790 1692791 22 328958 328959 TRA -12 1692791 1692792 22 328930 328931 TRA -12 1693375 1693376 22 1335518 1335519 TRA -12 1693824 1693827 22 1079833 1079836 TRA -12 1693851 1693852 22 1833655 1833656 TRA -12 1695016 1695017 12 1695369 1695370 DEL -12 1696122 1696125 22 391688 391691 TRA -12 1696489 1696490 22 1356064 1356065 TRA -12 1697883 1697884 12 1698166 1698167 DUP -12 1699252 1699253 22 217660 217661 TRA -12 1699502 1699504 22 217910 217912 TRA -12 1699946 1699947 12 1700311 1700312 INV -12 1700716 1700717 22 1274104 1274105 TRA -12 1700717 1700718 22 1274103 1274104 TRA -12 1701145 1701149 22 1274531 1274535 TRA -12 1701147 1701148 22 1274786 1274787 TRA -12 1702776 1702777 12 1702914 1702915 DUP -12 1703228 1703229 22 874831 874832 TRA -12 1703229 1703230 12 1985229 1985230 INV -12 1703540 1703543 22 875141 875144 TRA -12 1704520 1704522 22 381849 381851 TRA -12 1704521 1704523 22 381848 381850 TRA -12 1704655 1704656 22 381984 381985 TRA -12 1704656 1704657 22 381983 381984 TRA -12 1704702 1704705 12 1704848 1704851 DUP -12 1705007 1705010 12 1705256 1705259 DEL -12 1705549 1705550 12 1706041 1706042 INV -12 1705550 1705551 12 1706269 1706270 INV -12 1707848 1707855 12 1707974 1707981 DUP -12 1709375 1709376 12 1709829 1709830 DUP -12 1711436 1711439 22 1258589 1258592 TRA -12 1711639 1711640 22 1258790 1258791 TRA -12 1712907 1712908 22 437734 437735 TRA -12 1713222 1713223 12 1723358 1723359 DEL -12 1713223 1713224 12 1723357 1723358 DUP -12 1713962 1713963 22 210179 210180 TRA -12 1714260 1714262 22 210479 210481 TRA -12 1714261 1714263 22 210478 210480 TRA -12 1715149 1715150 22 342128 342129 TRA -12 1715272 1715273 22 342251 342252 TRA -12 1715773 1715775 12 1716187 1716189 DUP -12 1717501 1717503 12 1717785 1717787 DUP -12 1718239 1718241 12 1718404 1718406 DUP -12 1719686 1719688 12 1720088 1720090 DEL -12 1721034 1721037 12 1721276 1721279 INV -12 1721723 1721729 12 1722198 1722204 DEL -12 1723768 1723769 22 438462 438463 TRA -12 1724073 1724074 22 1739982 1739983 TRA -12 1724208 1724209 22 1854760 1854761 TRA -12 1724209 1724210 22 1854759 1854760 TRA -12 1724913 1724914 22 1710643 1710644 TRA -12 1724914 1724915 22 1710642 1710643 TRA -12 1725021 1725022 22 1710751 1710752 TRA -12 1725022 1725023 22 1710750 1710751 TRA -12 1727193 1727196 12 1727545 1727548 DUP -12 1729512 1729513 22 1357143 1357144 TRA -12 1729959 1729965 12 1730127 1730133 DUP -12 1731588 1731592 12 1732079 1732083 DEL -12 1732678 1732679 22 1220227 1220228 TRA -12 1732679 1732680 22 1220226 1220227 TRA -12 1733015 1733018 22 1220562 1220565 TRA -12 1733014 1733017 22 1220563 1220566 TRA -12 1733235 1733236 12 1733623 1733624 DUP -12 1734088 1734091 12 1734212 1734215 DEL -12 1735115 1735116 22 244263 244264 TRA -12 1735586 1735587 22 245097 245098 TRA -12 1735639 1735640 22 182005 182006 TRA -12 1736108 1736109 12 1736683 1736684 DEL -12 1737277 1737278 12 1737471 1737472 DUP -12 1738814 1738815 22 711294 711295 TRA -12 1738864 1738865 22 1433294 1433295 TRA -12 1739216 1739217 22 1411733 1411734 TRA -12 1739393 1739394 22 711712 711713 TRA -12 1740101 1740102 12 1740309 1740310 INV -12 1740782 1740784 22 1633955 1633957 TRA -12 1740781 1740783 22 1633956 1633958 TRA -12 1741113 1741114 22 1634288 1634289 TRA -12 1741114 1741115 22 1634287 1634288 TRA -12 1743008 1743011 12 1743253 1743256 INV -12 1743007 1743010 12 1743252 1743255 INV -12 1743802 1743804 12 1744096 1744098 DEL -12 1744300 1744302 22 1302512 1302514 TRA -12 1744579 1744580 22 1302791 1302792 TRA -12 1744996 1744998 22 1783895 1783897 TRA -12 1745097 1745100 22 1783998 1784001 TRA -12 1745098 1745101 22 1783997 1784000 TRA -12 1745457 1745459 22 274426 274428 TRA -12 1745472 1745473 22 484773 484774 TRA -12 1745633 1745634 22 485094 485095 TRA -12 1745634 1745635 22 485093 485094 TRA -12 1747186 1747187 22 156947 156948 TRA -12 1747853 1747856 12 1748250 1748253 DEL -12 1748607 1748608 22 1341311 1341312 TRA -12 1748784 1748785 22 1341490 1341491 TRA -12 1751259 1751261 12 1751578 1751580 DUP -12 1752093 1752095 22 577932 577934 TRA -12 1752094 1752096 22 577931 577933 TRA -12 1752588 1752589 22 798452 798453 TRA -12 1753333 1753334 12 1753752 1753753 INV -12 1754116 1754117 22 1254321 1254322 TRA -12 1754117 1754118 22 1254320 1254321 TRA -12 1754286 1754287 22 1254491 1254492 TRA -12 1754693 1754694 12 1754924 1754925 DEL -12 1755613 1755614 22 1061489 1061490 TRA -12 1755646 1755648 22 978004 978006 TRA -12 1756881 1756883 12 1756966 1756968 DUP -12 1757815 1757816 22 1386854 1386855 TRA -12 1757816 1757817 22 1386853 1386854 TRA -12 1758291 1758292 22 1387330 1387331 TRA -12 1758292 1758293 22 1387329 1387330 TRA -12 1758336 1758340 12 1758611 1758615 DUP -12 1759106 1759109 12 1759177 1759180 INV -12 1759418 1759419 22 1150831 1150832 TRA -12 1759760 1759761 12 1760007 1760008 DUP -12 1760125 1760126 12 1760292 1760293 INV -12 1760126 1760127 12 1760297 1760298 INV -12 1760496 1760497 12 1760882 1760883 DUP -12 1761081 1761084 22 1800734 1800737 TRA -12 1761311 1761313 22 1800964 1800966 TRA -12 1761312 1761314 22 1800963 1800965 TRA -12 1761920 1761922 12 1762290 1762292 DEL -12 1762970 1762972 12 1763275 1763277 INV -12 1764277 1764278 22 1478707 1478708 TRA -12 1764633 1764634 22 1667707 1667708 TRA -12 1766120 1766121 12 1766466 1766467 INV -12 1767984 1767985 22 1143090 1143091 TRA -12 1768728 1768729 22 75645 75646 TRA -12 1773893 1773894 12 1774258 1774259 INV -12 1773894 1773895 12 1774259 1774260 INV -12 1777238 1777239 12 1777569 1777570 DEL -12 1779500 1779501 22 1225274 1225275 TRA -12 1779844 1779847 22 1225616 1225619 TRA -12 1783146 1783150 12 1783547 1783551 DEL -12 1783882 1783883 22 1825292 1825293 TRA -12 1784337 1784338 22 1825291 1825292 TRA -12 1784625 1784626 22 1825581 1825582 TRA -12 1785300 1785303 22 574615 574618 TRA -12 1785794 1785795 22 575109 575110 TRA -12 1785795 1785796 22 575108 575109 TRA -12 1787015 1787016 12 1862401 1862402 DUP -12 1787347 1787348 22 900205 900206 TRA -12 1788149 1788152 22 277924 277927 TRA -12 1788315 1788317 22 278088 278090 TRA -12 1789690 1789691 12 1789775 1789776 DUP -12 1789841 1789844 12 1789977 1789980 DEL -12 1790680 1790681 22 834040 834041 TRA -12 1791252 1791253 22 333519 333520 TRA -12 1791542 1791544 22 333811 333813 TRA -12 1792569 1792570 22 594742 594743 TRA -12 1792908 1792909 12 1894541 1894542 DEL -12 1792908 1792910 22 595079 595081 TRA -12 1793934 1793935 22 591484 591485 TRA -12 1794035 1794036 22 1324025 1324026 TRA -12 1794036 1794037 22 1324024 1324025 TRA -12 1794996 1794998 12 1795278 1795280 DEL -12 1796351 1796354 12 1796558 1796561 DUP -12 1797914 1797915 22 1845662 1845663 TRA -12 1798363 1798365 22 1845661 1845663 TRA -12 1798857 1798858 22 1846157 1846158 TRA -12 1798858 1798859 22 1846156 1846157 TRA -12 1799232 1799233 12 1799991 1799992 DUP -12 1800056 1800058 12 1800192 1800194 DUP -12 1800733 1800734 12 1801120 1801121 INV -12 1800734 1800735 12 1801121 1801122 INV -12 1801439 1801442 22 1509655 1509658 TRA -12 1801645 1801646 22 1510394 1510395 TRA -12 1802324 1802325 22 1591694 1591695 TRA -12 1802750 1802751 22 1592530 1592531 TRA -12 1802750 1802752 22 1592118 1592120 TRA -12 1802838 1802841 12 1803206 1803209 DUP -12 1803342 1803344 12 1803678 1803680 DUP -12 1803734 1803738 22 995656 995660 TRA -12 1803985 1803986 22 995905 995906 TRA -12 1804142 1804144 12 1804498 1804500 DUP -12 1804579 1804580 12 1804909 1804910 INV -12 1805217 1805219 12 1805698 1805700 DEL -12 1805750 1805752 12 1806141 1806143 DUP -12 1806427 1806428 12 1806839 1806840 DUP -12 1807325 1807326 12 1807612 1807613 DEL -12 1809170 1809171 22 58022 58023 TRA -12 1809389 1809392 22 58239 58242 TRA -12 1810145 1810146 12 1810417 1810418 DEL -12 1811127 1811128 22 776133 776134 TRA -12 1811382 1811383 22 110849 110850 TRA -12 1811790 1811792 22 111563 111565 TRA -12 1811791 1811793 22 111562 111564 TRA -12 1812103 1812105 22 1093188 1093190 TRA -12 1812102 1812104 22 1093189 1093191 TRA -12 1812551 1812553 22 1093638 1093640 TRA -12 1812639 1812640 12 1813036 1813037 DUP -12 1814051 1814055 22 1004537 1004541 TRA -12 1814320 1814321 22 1004806 1004807 TRA -12 1814669 1814670 12 1814949 1814950 INV -12 1817767 1817769 12 1817833 1817835 INV -12 1818036 1818037 12 1818527 1818528 DEL -12 1819347 1819352 22 1959814 1959819 TRA -12 1819348 1819353 22 1959813 1959818 TRA -12 1819892 1819893 12 1820072 1820073 DUP -12 1820744 1820746 22 898312 898314 TRA -12 1820746 1820747 22 898312 898313 TRA -12 1821704 1821705 12 1821898 1821899 INV -12 1821705 1821708 12 1821897 1821900 INV -12 1823224 1823225 12 1823225 1823226 INS -12 1824208 1824210 12 1824342 1824344 DEL -12 1825353 1825354 12 1825578 1825579 INV -12 1825354 1825355 12 1825579 1825580 INV -12 1825727 1825728 22 1148253 1148254 TRA -12 1826184 1826187 22 1148712 1148715 TRA -12 1826185 1826188 22 1148711 1148714 TRA -12 1827551 1827552 12 1827850 1827851 DUP -12 1828174 1828176 12 1828343 1828345 DEL -12 1829840 1829841 22 226902 226903 TRA -12 1830531 1830535 22 227173 227177 TRA -12 1832521 1832522 22 1737646 1737647 TRA -12 1832522 1832523 22 1737645 1737646 TRA -12 1833023 1833025 22 1315489 1315491 TRA -12 1833551 1833552 12 1833797 1833798 DEL -12 1834829 1834830 12 1835469 1835470 DEL -12 1835609 1835610 12 1835672 1835673 DUP -12 1836034 1836035 12 1836459 1836460 DUP -12 1837443 1837448 12 1837691 1837696 DUP -12 1839052 1839053 12 1839410 1839411 DEL -12 1839941 1839942 22 702703 702704 TRA -12 1840212 1840213 22 702703 702704 TRA -12 1840622 1840623 22 949033 949034 TRA -12 1841294 1841297 12 1841399 1841402 DUP -12 1841535 1841536 12 1841680 1841681 DEL -12 1843355 1843359 12 1843788 1843792 DEL -12 1844665 1844666 22 1192814 1192815 TRA -12 1844733 1844734 22 1192884 1192885 TRA -12 1845189 1845190 12 1846340 1846341 INV -12 1847021 1847025 12 1847364 1847368 DUP -12 1848153 1848154 22 1756825 1756826 TRA -12 1848154 1848155 22 1756824 1756825 TRA -12 1849168 1849169 12 1849623 1849624 INV -12 1849169 1849170 12 1849624 1849625 INV -12 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1888713 1888715 22 1206808 1206810 TRA -12 1888712 1888714 22 1206809 1206811 TRA -12 1888858 1888859 22 1207283 1207284 TRA -12 1889258 1889259 22 1207121 1207122 TRA -12 1890059 1890062 12 1890303 1890306 DEL -12 1891238 1891242 12 1891648 1891652 INV -12 1892395 1892396 22 1097876 1097877 TRA -12 1892488 1892489 22 1097969 1097970 TRA -12 1892489 1892490 22 1097968 1097969 TRA -12 1893410 1893411 12 1893606 1893607 DEL -12 1894190 1894193 22 638406 638409 TRA -12 1894189 1894192 22 638407 638410 TRA -12 1894540 1894542 22 595272 595274 TRA -12 1896400 1896401 22 1250346 1250347 TRA -12 1896910 1896911 12 1897120 1897121 INV -12 1898376 1898377 12 1899342 1899343 INV -12 1898952 1898955 12 1899341 1899344 INV -12 1900761 1900762 22 264436 264437 TRA -12 1900760 1900763 22 20211 20214 TRA -12 1901068 1901069 22 20517 20518 TRA -12 1903014 1903017 12 1903134 1903137 DEL -12 1903582 1903584 12 1903912 1903914 DUP -12 1906705 1906708 22 1009140 1009143 TRA -12 1906850 1906852 22 1009283 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1915770 22 1984295 1984296 TRA -12 1915770 1915771 22 1984294 1984295 TRA -12 1916409 1916412 12 1916640 1916643 INV -12 1916410 1916413 12 1916641 1916644 INV -12 1917337 1917339 22 1884302 1884304 TRA -12 1918074 1918075 12 1918310 1918311 DEL -12 1918561 1918564 22 859105 859108 TRA -12 1919400 1919401 22 582284 582285 TRA -12 1919401 1919402 22 1867303 1867304 TRA -12 1919569 1919570 22 442791 442792 TRA -12 1919871 1919872 12 1919986 1919987 DEL -12 1920382 1920383 22 1177947 1177948 TRA -12 1920721 1920724 22 1177946 1177949 TRA -12 1921529 1921533 22 225200 225204 TRA -12 1921890 1921891 22 225277 225278 TRA -12 1923369 1923370 12 1923477 1923478 DUP -12 1924884 1924889 22 1277395 1277400 TRA -12 1924985 1924986 22 1277494 1277495 TRA -12 1925662 1925663 12 1925842 1925843 INV -12 1925663 1925664 12 1925843 1925844 INV -12 1926584 1926585 12 1926768 1926769 DEL -12 1927133 1927135 22 1695053 1695055 TRA -12 1927134 1927136 22 1695052 1695054 TRA -12 1927583 1927585 22 1695503 1695505 TRA -12 1927584 1927586 22 1695502 1695504 TRA -12 1928712 1928713 22 1459349 1459350 TRA -12 1928779 1928781 22 1459414 1459416 TRA -12 1930072 1930074 12 1930303 1930305 INV -12 1931048 1931049 12 1931365 1931366 DUP -12 1931471 1931473 12 1931843 1931845 DEL -12 1932240 1932241 22 1833301 1833302 TRA -12 1932501 1932502 22 582221 582222 TRA -12 1932502 1932503 22 582220 582221 TRA -12 1932876 1932877 22 1867666 1867667 TRA -12 1932876 1932877 22 582726 582727 TRA -12 1934850 1934851 12 1934917 1934918 DEL -12 1935235 1935236 12 1935583 1935584 INV -12 1936392 1936393 22 1620623 1620624 TRA -12 1936831 1936832 22 1392973 1392974 TRA -12 1936832 1936833 22 1392972 1392973 TRA -12 1936900 1936903 22 1393040 1393043 TRA -12 1937451 1937452 22 208418 208419 TRA -12 1937663 1937664 22 208628 208629 TRA -12 1938307 1938308 22 1218850 1218851 TRA -12 1938505 1938506 22 1219048 1219049 TRA -12 1939137 1939139 22 1119870 1119872 TRA -12 1940430 1940431 12 1940562 1940563 INV -12 1941620 1941621 22 268079 268080 TRA -12 1942590 1942592 12 1942779 1942781 INV -12 1942960 1942961 22 239525 239526 TRA -12 1943091 1943093 22 239654 239656 TRA -12 1943971 1943972 12 1944046 1944047 DEL -12 1946071 1946072 22 1547394 1547395 TRA -12 1946660 1946661 22 875800 875801 TRA -12 1947257 1947258 12 1947406 1947407 DEL -12 1947654 1947655 22 1425273 1425274 TRA -12 1948012 1948013 22 1142434 1142435 TRA -12 1948363 1948364 22 1124112 1124113 TRA -12 1950280 1950282 12 1950545 1950547 INV -12 1950883 1950884 12 1951349 1951350 INV -12 1950884 1950885 12 1951350 1951351 INV -12 1951923 1951926 12 1952025 1952028 DEL -12 1952723 1952724 22 1057262 1057263 TRA -12 1952979 1952984 22 1057520 1057525 TRA -12 1952980 1952985 22 1057519 1057524 TRA -12 1953659 1953660 12 1953811 1953812 DEL -12 1955693 1955695 12 1955921 1955923 DEL -12 1958005 1958006 22 1938290 1938291 TRA -12 1958133 1958135 22 1938255 1938257 TRA -12 1958482 1958485 22 1938604 1938607 TRA -12 1959518 1959519 12 1959783 1959784 DUP -12 1960306 1960307 22 741318 741319 TRA -12 1960604 1960605 22 741616 741617 TRA -12 1961456 1961458 22 1627474 1627476 TRA -12 1962038 1962039 22 1451272 1451273 TRA -12 1962376 1962380 22 1451612 1451616 TRA -12 1963255 1963256 12 1963605 1963606 DUP -12 1963733 1963734 22 1631903 1631904 TRA -12 1963794 1963796 22 1631964 1631966 TRA -12 1964110 1964111 22 701443 701444 TRA -12 1964593 1964594 12 1964973 1964974 INV -12 1964594 1964595 12 1964974 1964975 INV -12 1965341 1965344 12 1965394 1965397 DUP -12 1965949 1965951 22 1898050 1898052 TRA -12 1965950 1965952 22 1898049 1898051 TRA -12 1966312 1966313 22 1898413 1898414 TRA -12 1966711 1966712 22 1079452 1079453 TRA -12 1967635 1967636 22 1833844 1833845 TRA -12 1968133 1968135 12 1968458 1968460 DUP -12 1969187 1969189 22 832922 832924 TRA -12 1969259 1969260 22 832994 832995 TRA -12 1969731 1969733 22 365508 365510 TRA -12 1969732 1969734 22 365507 365509 TRA -12 1970149 1970150 22 365926 365927 TRA -12 1970150 1970151 22 365925 365926 TRA -12 1971149 1971150 12 1971331 1971332 INV -12 1971150 1971151 12 1971332 1971333 INV -12 1971457 1971458 12 1971746 1971747 DUP -12 1972176 1972177 12 1972440 1972441 INV -12 1972177 1972178 12 1972441 1972442 INV -12 1973158 1973160 12 1973284 1973286 DUP -12 1973854 1973855 12 1974096 1974097 DUP -12 1974106 1974108 22 1604870 1604872 TRA -12 1974107 1974108 12 1995901 1995902 DUP -12 1974396 1974397 12 1975367 1975368 DUP -12 1974730 1974731 22 1605141 1605142 TRA -12 1974938 1974939 12 1975368 1975369 INV -12 1976829 1976830 12 1977054 1977055 DEL -12 1977488 1977492 12 1977586 1977590 DUP -12 1977922 1977924 12 1978267 1978269 INV -12 1978701 1978702 12 1979076 1979077 INV -12 1978702 1978703 12 1979077 1979078 INV -12 1982307 1982308 22 1329308 1329309 TRA -12 1982308 1982309 22 1329307 1329308 TRA -12 1982430 1982431 22 1329431 1329432 TRA -12 1982431 1982432 22 1329430 1329431 TRA -12 1983529 1983530 12 1983983 1983984 DUP -12 1985062 1985063 12 1985442 1985443 DUP -12 1985511 1985512 22 874394 874395 TRA -12 1985781 1985782 22 875142 875143 TRA -12 1986299 1986301 12 1986704 1986706 DUP -12 1988176 1988178 12 1988322 1988324 INV -12 1988910 1988911 12 1989084 1989085 DUP -12 1989961 1989964 12 1990409 1990412 DUP -12 1990811 1990813 22 1686601 1686603 TRA -12 1991021 1991025 22 1686811 1686815 TRA -12 1991022 1991026 22 1686807 1686811 TRA -12 1991299 1991300 12 1991487 1991488 INV -12 1992067 1992068 22 715057 715058 TRA -12 1992450 1992452 22 715438 715440 TRA -12 1993543 1993544 12 1993935 1993936 DEL -12 1995451 1995452 22 1769062 1769063 TRA -12 1995735 1995736 22 1769496 1769497 TRA -12 1995901 1995903 22 1604794 1604796 TRA -12 1996030 1996031 22 709915 709916 TRA -12 1996304 1996305 22 710191 710192 TRA -12 1997880 1997881 22 1459230 1459231 TRA -12 1999345 1999346 12 1999781 1999782 DUP -22 995 998 22 1405 1408 DEL -22 4086 4089 22 4544 4547 DEL -22 4956 4957 22 5283 5284 DEL -22 7531 7532 22 8019 8020 DEL -22 11450 11452 22 1155662 1155664 DEL -22 12145 12146 22 12432 12433 INV -22 12146 12147 22 12433 12434 INV -22 12631 12633 22 12868 12870 DUP -22 13867 13868 22 14487 14488 INV -22 14110 14111 22 14932 14933 DUP -22 15538 15539 22 911991 911992 DUP -22 16477 16479 22 16834 16836 INV -22 17241 17242 22 1752036 1752037 DUP -22 18114 18115 22 1087569 1087570 DUP -22 19924 19930 22 264252 264258 DUP -22 20844 20846 22 21222 21224 DUP -22 21430 21431 22 21557 21558 DEL -22 21979 21981 22 22090 22092 DEL -22 22590 22592 22 22815 22817 DEL -22 22914 22917 22 23310 23313 DEL -22 27777 27778 22 27921 27922 INV -22 29769 29770 22 30233 30234 INV -22 31538 31541 22 31627 31630 INV -22 31744 31746 22 32046 32048 DEL -22 32262 32264 22 32504 32506 DUP -22 37456 37457 22 37697 37698 DUP -22 39440 39441 22 39769 39770 DUP -22 40811 40812 22 40951 40952 DUP -22 44281 44284 22 44706 44709 INV -22 44280 44283 22 44705 44708 INV -22 46330 46331 22 46701 46702 DUP -22 48576 48578 22 48916 48918 DUP -22 50722 50723 22 50952 50953 DUP -22 51300 51301 22 51301 51302 INS -22 51535 51536 22 51972 51973 DEL -22 52703 52704 22 53008 53009 DUP -22 53962 53963 22 54429 54430 DEL -22 55777 55779 22 56069 56071 DUP -22 56557 56559 22 56755 56757 INV -22 57003 57005 22 57266 57268 DEL -22 57441 57442 22 57867 57868 INV -22 57442 57443 22 57868 57869 INV -22 59045 59046 22 59359 59360 INV -22 59046 59047 22 59360 59361 INV -22 60476 60477 22 60901 60902 INV -22 60477 60478 22 60902 60903 INV -22 66690 66693 22 66990 66993 DUP -22 68900 68901 22 69310 69311 DUP -22 69524 69525 22 69690 69691 DUP -22 69874 69875 22 70357 70358 INV -22 72726 72730 22 72786 72790 DEL -22 77051 77052 22 998176 998177 DEL -22 79018 79020 22 79207 79209 INV -22 79017 79019 22 79206 79208 INV -22 83409 83410 22 83410 83411 INS -22 84138 84146 22 84430 84438 DEL -22 85124 85125 22 85209 85210 DUP -22 87095 87097 22 87585 87587 INV -22 87096 87098 22 87586 87588 INV -22 88045 88046 22 88890 88891 DUP -22 88044 88047 22 88379 88382 INV -22 88397 88398 22 88891 88892 INV -22 90279 90281 22 90466 90468 INV -22 90280 90282 22 90467 90469 INV -22 94880 94884 22 95320 95324 INV -22 94879 94883 22 95319 95323 INV -22 98383 98384 22 1345487 1345488 DEL -22 98384 98385 22 1345486 1345487 DUP -22 98489 98491 22 1345591 1345593 DUP -22 98488 98490 22 1345592 1345594 DEL -22 98707 98708 22 98841 98842 DUP -22 99352 99354 22 99698 99700 DUP -22 99932 99933 22 100256 100257 DUP -22 103204 103205 22 103600 103601 DUP -22 104172 104173 22 1380499 1380500 DEL -22 105469 105470 22 105834 105835 DUP -22 108816 108819 22 109205 109208 DUP -22 110848 110849 22 111133 111134 INV -22 112693 112696 22 112929 112932 DUP -22 113657 113658 22 113772 113773 INV -22 115548 115550 22 115644 115646 DUP -22 116932 116933 22 117423 117424 INV -22 120707 120708 22 120934 120935 DEL -22 121867 121868 22 668727 668728 DEL -22 122155 122156 22 668938 668939 DUP -22 126324 126325 22 126541 126542 DUP -22 126922 126923 22 127380 127381 DEL -22 128023 128024 22 128147 128148 DEL -22 128734 128735 22 128903 128904 DEL -22 129447 129448 22 129811 129812 INV -22 130865 130866 22 131361 131362 INV -22 130866 130867 22 131362 131363 INV -22 132664 132667 22 133040 133043 INV -22 132667 132670 22 133041 133044 INV -22 134700 134702 22 135192 135194 DEL -22 135507 135508 22 135981 135982 INV -22 135508 135509 22 135982 135983 INV -22 137022 137024 22 137148 137150 DUP -22 137197 137199 22 137637 137639 DEL -22 139014 139015 22 139469 139470 INV -22 139880 139881 22 562218 562219 DEL -22 140202 140203 22 1647850 1647851 DUP -22 140301 140302 22 1647958 1647959 DEL -22 141237 141238 22 141486 141487 DUP -22 143022 143023 22 143504 143505 DUP -22 144705 144707 22 144867 144869 DUP -22 145364 145365 22 145442 145443 DUP -22 146233 146234 22 146393 146394 DUP -22 146592 146593 22 1141430 1141431 DUP -22 148224 148226 22 148592 148594 DEL -22 151385 151386 22 151443 151444 INV -22 151602 151603 22 151762 151763 INV -22 157987 157988 22 158400 158401 DEL -22 159292 159293 22 159389 159390 DEL -22 160242 160243 22 160310 160311 DUP -22 160407 160409 22 160550 160552 DEL -22 161823 161824 22 161922 161923 INV -22 161824 161825 22 161923 161924 INV -22 163752 163753 22 1203726 1203727 INV -22 163952 163953 22 164311 164312 DUP -22 167460 167463 22 167929 167932 INV -22 167461 167464 22 167930 167933 INV -22 169486 169488 22 169810 169812 INV -22 170299 170300 22 170606 170607 INV -22 170300 170301 22 170607 170608 INV -22 172071 172072 22 172444 172445 DUP -22 177407 177408 22 177744 177745 DUP -22 178233 178235 22 178614 178616 DUP -22 178853 178856 22 178943 178946 INV -22 178854 178857 22 178944 178947 INV -22 179474 179475 22 1551262 1551263 DUP -22 182006 182007 22 1549427 1549428 DUP -22 187043 187044 22 363490 363491 DEL -22 189657 189659 22 189908 189910 DUP -22 191177 191178 22 191500 191501 DEL -22 191924 191927 22 192011 192014 INV -22 192254 192256 22 192713 192715 DUP -22 192796 192798 22 192935 192937 INV -22 192797 192799 22 192936 192938 INV -22 194340 194341 22 194798 194799 DEL -22 200660 200661 22 200853 200854 DEL -22 204377 204382 22 204634 204639 DEL -22 205359 205360 22 205440 205441 INV -22 205360 205361 22 205441 205442 INV -22 206459 206460 22 746181 746182 INV -22 212740 212741 22 212909 212910 INV -22 215408 215412 22 215476 215480 DUP -22 216963 216964 22 217392 217393 DEL -22 220956 220957 22 220957 220958 INS -22 227543 227544 22 228387 228388 INV -22 228040 228041 22 228388 228389 INV -22 228791 228794 22 228948 228951 DUP -22 231291 231292 22 231649 231650 DEL -22 232042 232043 22 455130 455131 DEL -22 232543 232544 22 455472 455473 DUP -22 233069 233070 22 233380 233381 DUP -22 234163 234164 22 234414 234415 INV -22 234164 234165 22 234415 234416 INV -22 238306 238307 22 239026 239027 DEL -22 241143 241145 22 241298 241300 INV -22 242906 242908 22 243090 243092 DEL -22 245628 245630 22 245694 245696 DUP -22 246773 246775 22 247253 247255 DUP -22 249866 249867 22 250242 250243 INV -22 249867 249868 22 250243 250244 INV -22 251450 251452 22 251723 251725 DUP -22 251823 251824 22 252217 252218 DUP -22 253079 253080 22 254054 254055 INV -22 253619 253620 22 254055 254056 INV -22 254669 254674 22 255142 255147 INV -22 254670 254671 22 255672 255673 INV -22 256950 256951 22 257564 257565 DUP -22 258291 258292 22 258592 258593 INV -22 258292 258293 22 258593 258594 INV -22 259983 259984 22 1311813 1311814 DEL -22 261847 261848 22 261941 261942 DEL -22 264098 264099 22 292679 292680 DUP -22 265284 265285 22 265452 265453 INV -22 265285 265286 22 265453 265454 INV -22 268875 268876 22 269897 269898 INV -22 269278 269282 22 269454 269458 DEL -22 270329 270330 22 270725 270726 INV -22 270330 270331 22 270726 270727 INV -22 271834 271835 22 272563 272564 DUP -22 272207 272208 22 272950 272951 DUP -22 275763 275764 22 275764 275765 INS -22 276911 276915 22 277032 277036 DEL -22 281169 281170 22 281463 281464 DUP -22 284732 284733 22 285091 285092 INV -22 285692 285693 22 286108 286109 DEL -22 286236 286237 22 286381 286382 DUP -22 287979 287980 22 288234 288235 INV -22 290894 290896 22 291175 291177 INV -22 293584 293585 22 294022 294023 DUP -22 295780 295783 22 296275 296278 DUP -22 296495 296496 22 296651 296652 DEL -22 296796 296800 22 296993 296997 DEL -22 297811 297812 22 1780898 1780899 DUP -22 297960 297961 22 298139 298140 INV -22 298260 298262 22 298517 298519 DUP -22 298666 298671 22 299000 299005 INV -22 301275 301276 22 1090501 1090502 DUP -22 301393 301397 22 301719 301723 DEL -22 302851 302854 22 303214 303217 DEL -22 305908 305909 22 306365 306366 DEL -22 308244 308247 22 308708 308711 DEL -22 308803 308804 22 1103462 1103463 DUP -22 308993 308994 22 771586 771587 DEL -22 309819 309820 22 310261 310262 DUP -22 310987 310988 22 311214 311215 INV -22 310988 310989 22 311215 311216 INV -22 311732 311733 22 312066 312067 DUP -22 312267 312269 22 312667 312669 INV -22 312268 312270 22 312668 312670 INV -22 314044 314045 22 1024917 1024918 DUP -22 314390 314393 22 1025337 1025340 DEL -22 315962 315964 22 316271 316273 DEL -22 317020 317021 22 1035126 1035127 DEL -22 319446 319447 22 319865 319866 DUP -22 320935 320936 22 321405 321406 DUP -22 322842 322843 22 323262 323263 DEL -22 328579 328580 22 1114844 1114845 DUP -22 331459 331465 22 331643 331649 DEL -22 332530 332531 22 681353 681354 DUP -22 332717 332719 22 333213 333215 DUP -22 334718 334719 22 334843 334844 INV -22 334719 334720 22 334844 334845 INV -22 336600 336603 22 337020 337023 DEL -22 343188 343190 22 343483 343485 INV -22 345245 345248 22 345359 345362 DUP -22 349094 349096 22 349274 349276 INV -22 349095 349097 22 349275 349277 INV -22 352078 352079 22 352367 352368 DEL -22 353537 353538 22 353604 353605 DUP -22 356201 356202 22 356448 356449 DUP -22 356554 356555 22 356751 356752 INV -22 357595 357602 22 357710 357717 DEL -22 358259 358261 22 358343 358345 DUP -22 358891 358892 22 359229 359230 DUP -22 359592 359593 22 359654 359655 DUP -22 360873 360874 22 360986 360987 DEL -22 361628 361630 22 362028 362030 INV -22 363065 363069 22 363304 363308 DUP -22 364192 364194 22 364255 364257 DEL -22 364438 364439 22 364809 364810 DUP -22 366476 366478 22 366726 366728 INV -22 367373 367375 22 367486 367488 INV -22 367374 367376 22 367487 367489 INV -22 369969 369973 22 370196 370200 INV -22 370625 370628 22 370866 370869 INV -22 371112 371115 22 371412 371415 DUP -22 371813 371814 22 372303 372304 INV -22 371814 371815 22 372304 372305 INV -22 373688 373689 22 374177 374178 DEL -22 376989 376990 22 377281 377282 DUP -22 378119 378120 22 1206809 1206810 DUP -22 379021 379025 22 379118 379122 DUP -22 379489 379490 22 380235 380236 INV -22 389265 389266 22 389760 389761 DUP -22 389881 389884 22 390033 390036 INV -22 389884 389887 22 390034 390037 INV -22 390533 390534 22 390969 390970 INV -22 391844 391845 22 392270 392271 INV -22 393395 393397 22 393472 393474 INV -22 393396 393398 22 393473 393475 INV -22 393937 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-22 534336 534337 22 1985301 1985302 DUP -22 536019 536021 22 536288 536290 DUP -22 536643 536650 22 536849 536856 DUP -22 538936 538937 22 539014 539015 INV -22 538937 538938 22 539015 539016 INV -22 539876 539879 22 540165 540168 DEL -22 540201 540202 22 540527 540528 DUP -22 541049 541050 22 541168 541169 DEL -22 541599 541600 22 542372 542373 INV -22 542759 542760 22 543195 543196 DEL -22 544356 544357 22 544753 544754 DUP -22 544763 544764 22 545048 545049 INV -22 546389 546390 22 546666 546667 DEL -22 547285 547286 22 547575 547576 INV -22 547286 547287 22 547576 547577 INV -22 548551 548552 22 1024917 1024918 DEL -22 551720 551721 22 551829 551830 INV -22 556994 556995 22 557280 557281 INV -22 558531 558532 22 558718 558719 DEL -22 560932 560933 22 561111 561112 INV -22 561358 561361 22 1970001 1970004 DEL -22 561359 561362 22 1970000 1970003 DUP -22 563052 563054 22 563208 563210 DEL -22 564165 564166 22 564166 564167 INS -22 564341 564344 22 564557 564560 DUP -22 564607 564608 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-22 618611 618612 22 620139 620140 DUP -22 625456 625457 22 625758 625759 DUP -22 627274 627275 22 627369 627370 DUP -22 627554 627555 22 627843 627844 DUP -22 628422 628423 22 628829 628830 INV -22 628423 628424 22 628830 628831 INV -22 629139 629140 22 629488 629489 DUP -22 631074 631075 22 631551 631552 DEL -22 632177 632178 22 1248828 1248829 DEL -22 632425 632427 22 632876 632878 DEL -22 633537 633538 22 633899 633900 DUP -22 634197 634199 22 634528 634530 DEL -22 637622 637623 22 637883 637884 DEL -22 638690 638691 22 639119 639120 INV -22 640092 640094 22 640396 640398 INV -22 641063 641066 22 641337 641340 INV -22 642379 642382 22 642712 642715 INV -22 642380 642383 22 642713 642716 INV -22 645111 645112 22 645181 645182 DEL -22 645319 645320 22 645498 645499 DUP -22 645646 645647 22 645765 645766 INV -22 647643 647644 22 648063 648064 DUP -22 648927 648931 22 649275 649279 DUP -22 649295 649296 22 649350 649351 DUP -22 650140 650141 22 650335 650336 INV -22 651944 651945 22 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INV -22 766209 766211 22 766567 766569 INV -22 766857 766858 22 766938 766939 DEL -22 771821 771822 22 1104762 1104763 DEL -22 773040 773043 22 773355 773358 DEL -22 776449 776454 22 776792 776797 DUP -22 777891 777892 22 956539 956540 DUP -22 778876 778877 22 779366 779367 INV -22 779000 779001 22 779367 779368 INV -22 781385 781388 22 781522 781525 DUP -22 781870 781871 22 782127 782128 INV -22 781871 781872 22 782128 782129 INV -22 784282 784284 22 784424 784426 DEL -22 784488 784490 22 784983 784985 DUP -22 785392 785393 22 785644 785645 DEL -22 786467 786469 22 786820 786822 INV -22 786468 786470 22 786821 786823 INV -22 787852 787853 22 787945 787946 INV -22 787853 787854 22 787946 787947 INV -22 788335 788339 22 788603 788607 DUP -22 790941 790942 22 791199 791200 INV -22 794084 794085 22 794330 794331 DUP -22 794617 794620 22 794917 794920 DUP -22 794982 794983 22 795355 795356 INV -22 798019 798022 22 1341818 1341821 DUP -22 798098 798100 22 1341897 1341899 DUP -22 799202 799203 22 799468 799469 DUP -22 799831 799832 22 800299 800300 DUP -22 801894 801895 22 801980 801981 INV -22 801895 801896 22 801981 801982 INV -22 802633 802635 22 802872 802874 INV -22 802634 802636 22 802873 802875 INV -22 805895 805896 22 1112448 1112449 DEL -22 810000 810002 22 810165 810167 DUP -22 810810 810811 22 811100 811101 DUP -22 812162 812163 22 812919 812920 INV -22 813917 813918 22 815012 815013 DEL -22 815414 815415 22 815668 815669 DEL -22 817204 817207 22 817504 817507 DEL -22 819502 819503 22 819992 819993 DEL -22 821176 821177 22 821426 821427 DUP -22 821638 821639 22 821978 821979 INV -22 821639 821640 22 821979 821980 INV -22 823771 823772 22 1974077 1974078 DEL -22 824768 824770 22 824863 824865 DEL -22 827468 827469 22 827833 827834 DUP -22 828581 828582 22 829071 829072 INV -22 831017 831018 22 831415 831416 DEL -22 832137 832138 22 832324 832325 DEL -22 832458 832459 22 832658 832659 INV -22 833780 833781 22 833910 833911 INV -22 834595 834597 22 834913 834915 DEL -22 838875 838877 22 839183 839185 INV -22 838876 838878 22 839184 839186 INV -22 840102 840103 22 840554 840555 DEL -22 845418 845419 22 845870 845871 INV -22 847500 847502 22 847666 847668 DUP -22 849688 849689 22 849781 849782 INV -22 851060 851061 22 851512 851513 DUP -22 851702 851704 22 851781 851783 DEL -22 852955 852956 22 921976 921977 DUP -22 854451 854454 22 854593 854596 DUP -22 855181 855182 22 855399 855400 DUP -22 857433 857434 22 1641754 1641755 DUP -22 858196 858197 22 1295278 1295279 DEL -22 858683 858684 22 1295497 1295498 DUP -22 859904 859907 22 859994 859997 DEL -22 860038 860039 22 860107 860108 DUP -22 861998 862001 22 862360 862363 DEL -22 863106 863107 22 863249 863250 DUP -22 863389 863392 22 863879 863882 DUP -22 864133 864134 22 864186 864187 DUP -22 864267 864269 22 864585 864587 DEL -22 865401 865402 22 865677 865678 INV -22 870750 870752 22 870831 870833 DEL -22 872245 872247 22 872518 872520 DEL -22 877209 877210 22 877653 877654 DUP -22 881781 881782 22 1622901 1622902 DEL -22 882299 882300 22 882300 882301 INS -22 883713 883715 22 884206 884208 DEL -22 885803 885804 22 885990 885991 DUP -22 886820 886821 22 1766972 1766973 DUP -22 888049 888050 22 1280157 1280158 DEL -22 888647 888648 22 888716 888717 DEL -22 889308 889310 22 889500 889502 DEL -22 891189 891190 22 1177310 1177311 DEL -22 891688 891689 22 1177649 1177650 DUP -22 891792 891796 22 891860 891864 DUP -22 892606 892608 22 892861 892863 INV -22 896685 896688 22 897098 897101 DEL -22 899402 899403 22 1448865 1448866 DUP -22 901083 901084 22 901388 901389 DUP -22 901807 901808 22 902009 902010 INV -22 901808 901809 22 902010 902011 INV -22 903573 903575 22 903684 903686 DUP -22 904130 904132 22 904616 904618 DUP -22 904973 904974 22 905428 905429 DEL -22 906096 906099 22 906280 906283 DEL -22 908590 908592 22 908965 908967 DEL -22 909658 909660 22 909971 909973 DEL -22 910286 910288 22 910505 910507 DEL -22 910549 910550 22 910649 910650 DUP -22 910849 910856 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-22 962391 962392 22 962546 962547 DUP -22 962580 962584 22 962633 962637 DUP -22 966013 966015 22 966357 966359 INV -22 966014 966016 22 966358 966360 INV -22 968665 968667 22 968762 968764 DUP -22 970017 970020 22 970411 970414 INV -22 973138 973139 22 973214 973215 INV -22 973139 973140 22 973215 973216 INV -22 973479 973481 22 973585 973587 DEL -22 974543 974544 22 974760 974761 DUP -22 975330 975332 22 975511 975513 DEL -22 975815 975816 22 975991 975992 DEL -22 978699 978700 22 979144 979145 INV -22 978700 978701 22 979505 979506 INV -22 980980 980981 22 1732223 1732224 DEL -22 982834 982836 22 983282 983284 DEL -22 983774 983776 22 983982 983984 DUP -22 983994 983995 22 984102 984103 DUP -22 984289 984292 22 984638 984641 INV -22 984288 984291 22 984637 984640 INV -22 986342 986343 22 986431 986432 INV -22 986343 986344 22 986432 986433 INV -22 986639 986640 22 986820 986821 DUP -22 989050 989051 22 989198 989199 DUP -22 993547 993548 22 994043 994044 DUP -22 999622 999624 22 999712 999714 DEL -22 1001853 1001854 22 1093425 1093426 DUP -22 1002066 1002067 22 1093638 1093639 DUP -22 1002739 1002743 22 1244515 1244519 DEL -22 1002740 1002744 22 1244514 1244518 DUP -22 1002809 1002811 22 1184786 1184788 DUP -22 1006020 1006021 22 1006475 1006476 INV -22 1006021 1006022 22 1006476 1006477 INV -22 1008165 1008166 22 1008315 1008316 DUP -22 1009584 1009587 22 1009766 1009769 INV -22 1010973 1010974 22 1011175 1011176 INV -22 1010974 1010975 22 1011176 1011177 INV -22 1013193 1013194 22 1013284 1013285 DEL -22 1013984 1013985 22 1014336 1014337 DUP -22 1015061 1015062 22 1015241 1015242 INV -22 1015761 1015762 22 1015982 1015983 INV -22 1015762 1015763 22 1015983 1015984 INV -22 1016641 1016642 22 1017059 1017060 DUP -22 1017206 1017207 22 1017325 1017326 INV -22 1017207 1017208 22 1017326 1017327 INV -22 1017729 1017731 22 1018033 1018035 DUP -22 1018386 1018387 22 1018727 1018728 DUP -22 1018941 1018942 22 1019236 1019237 DEL -22 1019312 1019314 22 1019546 1019548 DEL -22 1020355 1020356 22 1020567 1020568 DUP -22 1020650 1020652 22 1021105 1021107 INV -22 1022516 1022519 22 1022640 1022643 DEL -22 1023189 1023190 22 1023415 1023416 DUP -22 1023798 1023799 22 1024184 1024185 DEL -22 1025852 1025855 22 1025948 1025951 INV -22 1026857 1026858 22 1027560 1027561 DEL -22 1027765 1027766 22 1027766 1027767 INS -22 1028392 1028396 22 1028881 1028885 DUP -22 1029125 1029126 22 1029334 1029335 INV -22 1029126 1029127 22 1029335 1029336 INV -22 1029707 1029708 22 1029708 1029709 INS -22 1030433 1030434 22 1030817 1030818 DEL -22 1031742 1031743 22 1032170 1032171 INV -22 1034076 1034077 22 1040188 1040189 DEL -22 1036249 1036253 22 1036420 1036424 DUP -22 1037145 1037146 22 1037514 1037515 DEL -22 1040702 1040706 22 1040774 1040778 DUP -22 1041842 1041843 22 1703796 1703797 DEL -22 1043528 1043531 22 1043686 1043689 INV -22 1045154 1045155 22 1045155 1045156 INS -22 1045562 1045564 22 1045682 1045684 DUP -22 1047089 1047090 22 1047325 1047326 DUP -22 1049757 1049758 22 1050023 1050024 INV -22 1050466 1050468 22 1050877 1050879 DUP -22 1051188 1051193 22 1051587 1051592 DUP -22 1051678 1051680 22 1052088 1052090 DUP -22 1052742 1052743 22 1985189 1985190 DEL -22 1054231 1054233 22 1054452 1054454 INV -22 1060502 1060504 22 1060781 1060783 DEL -22 1061775 1061777 22 1062059 1062061 DUP -22 1064861 1064862 22 1065259 1065260 DUP -22 1067793 1067795 22 1068203 1068205 INV -22 1069250 1069251 22 1069351 1069352 DEL -22 1070557 1070558 22 1070642 1070643 DUP -22 1070940 1070942 22 1071272 1071274 INV -22 1071545 1071547 22 1071868 1071870 DUP -22 1072480 1072481 22 1072943 1072944 INV -22 1072481 1072482 22 1072944 1072945 INV -22 1073510 1073514 22 1073810 1073814 INV -22 1074136 1074138 22 1074226 1074228 INV -22 1075231 1075232 22 1075355 1075356 DUP -22 1080576 1080578 22 1080633 1080635 INV -22 1081560 1081561 22 1430167 1430168 DUP -22 1081807 1081810 22 1718270 1718273 DUP -22 1085814 1085816 22 1086308 1086310 DEL -22 1089733 1089735 22 1090074 1090076 INV -22 1092277 1092279 22 1092744 1092746 INV -22 1094754 1094755 22 1095417 1095418 DEL -22 1097100 1097101 22 1097325 1097326 DUP -22 1098139 1098144 22 1098572 1098577 INV -22 1099114 1099116 22 1099244 1099246 DUP -22 1101614 1101615 22 1101870 1101871 DUP -22 1102103 1102104 22 1102203 1102204 INV -22 1102385 1102386 22 1102809 1102810 DEL -22 1104761 1104762 22 1919707 1919708 DEL -22 1105699 1105700 22 1106630 1106631 INV -22 1107279 1107280 22 1107507 1107508 DEL -22 1111795 1111796 22 1111852 1111853 DUP -22 1113608 1113609 22 1114607 1114608 DEL -22 1116058 1116060 22 1116133 1116135 INV -22 1117808 1117809 22 1118031 1118032 DEL -22 1118173 1118174 22 1118290 1118291 DUP -22 1123937 1123938 22 1124040 1124041 DUP -22 1124113 1124114 22 1425734 1425735 DUP -22 1126606 1126609 22 1126659 1126662 DEL -22 1128650 1128652 22 1129070 1129072 DUP -22 1130381 1130382 22 1130511 1130512 DUP -22 1132281 1132282 22 1132538 1132539 DEL -22 1134671 1134672 22 1135625 1135626 DUP -22 1135146 1135147 22 1135626 1135627 INV -22 1136392 1136393 22 1136863 1136864 DUP -22 1137496 1137497 22 1481434 1481435 DUP -22 1139675 1139676 22 1463721 1463722 DEL -22 1142182 1142183 22 1425272 1425273 DUP -22 1145564 1145565 22 1145836 1145837 DEL -22 1146182 1146184 22 1146377 1146379 DEL -22 1148329 1148332 22 1364171 1364174 DUP -22 1149950 1149951 22 1150402 1150403 INV -22 1149951 1149952 22 1150403 1150404 INV -22 1151528 1151530 22 1151593 1151595 DEL -22 1153585 1153586 22 1154062 1154063 DUP -22 1154165 1154166 22 1154509 1154510 DEL -22 1155896 1155897 22 1897031 1897032 DEL -22 1156270 1156271 22 1156764 1156765 DUP -22 1158334 1158335 22 1158490 1158491 DEL -22 1159126 1159128 22 1682086 1682088 DEL -22 1159718 1159719 22 1160208 1160209 DEL -22 1160994 1160996 22 1161456 1161458 DEL -22 1161566 1161567 22 1162031 1162032 DUP -22 1162375 1162376 22 1162529 1162530 DUP -22 1165477 1165478 22 1165607 1165608 DUP -22 1166023 1166024 22 1166024 1166025 INS -22 1166422 1166423 22 1166682 1166683 INV -22 1166423 1166424 22 1166683 1166684 INV -22 1166702 1166703 22 1167125 1167126 DUP -22 1167734 1167735 22 1168021 1168022 INV -22 1167735 1167736 22 1168022 1168023 INV -22 1169517 1169518 22 1170475 1170476 DEL -22 1170923 1170924 22 1171265 1171266 INV -22 1170924 1170925 22 1171266 1171267 INV -22 1171552 1171554 22 1171974 1171976 DUP -22 1173476 1173478 22 1173795 1173797 INV -22 1173477 1173479 22 1173796 1173798 INV -22 1175360 1175361 22 1175490 1175491 DUP -22 1177728 1177729 22 1177836 1177837 DUP -22 1185047 1185048 22 1185121 1185122 DUP -22 1187204 1187205 22 1187423 1187424 INV -22 1187205 1187206 22 1187424 1187425 INV -22 1187959 1187960 22 1188269 1188270 DUP -22 1188733 1188734 22 1189166 1189167 DUP -22 1191163 1191164 22 1191586 1191587 DEL -22 1194097 1194098 22 1194559 1194560 DUP -22 1195956 1195957 22 1196301 1196302 INV -22 1195957 1195958 22 1196302 1196303 INV -22 1200807 1200808 22 1201084 1201085 DUP -22 1201357 1201360 22 1201694 1201697 DUP -22 1203270 1203272 22 1203726 1203728 INV -22 1204400 1204402 22 1204839 1204841 DUP -22 1204951 1204952 22 1205170 1205171 DUP -22 1205694 1205695 22 1205917 1205918 DEL -22 1206141 1206142 22 1206540 1206541 INV -22 1209250 1209251 22 1209708 1209709 DEL -22 1210395 1210397 22 1210728 1210730 INV -22 1212886 1212888 22 1213148 1213150 DEL -22 1213153 1213155 22 1213298 1213300 DUP -22 1213823 1213825 22 1214033 1214035 DUP -22 1214722 1214723 22 1902383 1902384 DEL -22 1214923 1214924 22 1902573 1902574 DEL -22 1214924 1214925 22 1902572 1902573 DUP -22 1216421 1216422 22 1216911 1216912 DUP -22 1218377 1218378 22 1218583 1218584 INV -22 1219259 1219260 22 1219473 1219474 DUP -22 1222682 1222683 22 1223535 1223536 DUP -22 1223117 1223119 22 1223535 1223537 INV -22 1223857 1223859 22 1224019 1224021 DEL -22 1224549 1224550 22 1224633 1224634 DUP -22 1226636 1226637 22 1226901 1226902 DEL -22 1228330 1228332 22 1228697 1228699 DUP -22 1232083 1232084 22 1232569 1232570 INV -22 1232084 1232085 22 1232570 1232571 INV -22 1233473 1233474 22 1552011 1552012 DEL -22 1233643 1233644 22 1552102 1552103 DUP -22 1235448 1235449 22 1235973 1235974 INV -22 1237229 1237232 22 1237373 1237376 DUP -22 1239460 1239461 22 1239814 1239815 INV -22 1239461 1239462 22 1239815 1239816 INV -22 1241734 1241735 22 1604303 1604304 DUP -22 1242504 1242505 22 1242599 1242600 INV -22 1242505 1242506 22 1242600 1242601 INV -22 1243259 1243260 22 1243534 1243535 DUP -22 1244104 1244105 22 1769283 1769284 DUP -22 1246428 1246429 22 1246772 1246773 DUP -22 1247682 1247684 22 1248070 1248072 DEL -22 1250199 1250200 22 1250201 1250202 INS -22 1253363 1253366 22 1253459 1253462 DUP -22 1254042 1254043 22 1254240 1254241 DUP -22 1255077 1255079 22 1255357 1255359 DUP -22 1256247 1256248 22 1256793 1256794 DEL -22 1260147 1260148 22 1260228 1260229 DEL -22 1262841 1262842 22 1262919 1262920 INV -22 1262842 1262843 22 1262920 1262921 INV -22 1263476 1263477 22 1263966 1263967 INV -22 1263477 1263478 22 1263967 1263968 INV -22 1265629 1265631 22 1265773 1265775 DEL -22 1266127 1266128 22 1266375 1266376 DUP -22 1267049 1267050 22 1267149 1267150 DUP -22 1267696 1267697 22 1267975 1267976 DEL -22 1268413 1268414 22 1268621 1268622 DEL -22 1270863 1270866 22 1271178 1271181 DEL -22 1272198 1272201 22 1272413 1272416 DEL -22 1272861 1272862 22 1273022 1273023 DUP -22 1273728 1273729 22 1273896 1273897 INV -22 1275349 1275350 22 1275774 1275775 INV -22 1275350 1275352 22 1275774 1275776 INV -22 1278133 1278135 22 1278188 1278190 DEL -22 1281450 1281452 22 1281768 1281770 INV -22 1281451 1281453 22 1281769 1281771 INV -22 1284909 1284910 22 1285263 1285264 DUP -22 1287711 1287713 22 1287857 1287859 DEL -22 1289066 1289069 22 1289536 1289539 DUP -22 1291173 1291174 22 1291497 1291498 DUP -22 1292110 1292112 22 1292204 1292206 INV -22 1294295 1294296 22 1295164 1295165 DUP -22 1296667 1296669 22 1296767 1296769 INV -22 1301267 1301269 22 1301662 1301664 DUP -22 1303455 1303457 22 1303942 1303944 INV -22 1305151 1305152 22 1305339 1305340 DUP -22 1308231 1308232 22 1308311 1308312 DUP -22 1308616 1308617 22 1308982 1308983 INV -22 1310548 1310550 22 1310608 1310610 DUP -22 1314275 1314279 22 1314352 1314356 INV -22 1315984 1315985 22 1316238 1316239 DUP -22 1319194 1319195 22 1319325 1319326 DUP -22 1322714 1322718 22 1322861 1322865 INV -22 1322897 1322898 22 1323319 1323320 DUP -22 1324952 1324953 22 1325144 1325145 DEL -22 1325186 1325189 22 1325628 1325631 DUP -22 1328698 1328699 22 1328852 1328853 INV -22 1332257 1332262 22 1332441 1332446 INV -22 1336570 1336571 22 1336985 1336986 DUP -22 1337496 1337497 22 1337617 1337618 DUP -22 1339563 1339564 22 1339708 1339709 DUP -22 1340568 1340569 22 1340667 1340668 DEL -22 1342055 1342056 22 1342423 1342424 DUP -22 1342706 1342709 22 1342903 1342906 INV -22 1343123 1343124 22 1524230 1524231 DUP -22 1343568 1343570 22 1344044 1344046 DUP -22 1344597 1344600 22 1344807 1344810 INV -22 1344596 1344599 22 1344806 1344809 INV -22 1348598 1348601 22 1348892 1348895 DEL -22 1354082 1354083 22 1354339 1354340 INV -22 1356647 1356650 22 1356772 1356775 DEL -22 1357411 1357413 22 1357763 1357765 INV -22 1357410 1357418 22 1357756 1357764 INV -22 1368421 1368423 22 1368745 1368747 INV -22 1371474 1371477 22 1371577 1371580 DUP -22 1372049 1372050 22 1372232 1372233 DUP -22 1372636 1372637 22 1372866 1372867 DEL -22 1374696 1374700 22 1374824 1374828 DEL -22 1378696 1378699 22 1378788 1378791 DEL -22 1379548 1379550 22 1379705 1379707 DUP -22 1383085 1383086 22 1383397 1383398 INV -22 1383086 1383087 22 1383398 1383399 INV -22 1384401 1384402 22 1384856 1384857 INV -22 1384402 1384403 22 1384857 1384858 INV -22 1385966 1385968 22 1386148 1386150 DUP -22 1386186 1386187 22 1386605 1386606 INV -22 1386187 1386188 22 1386606 1386607 INV -22 1387992 1387996 22 1388481 1388485 DUP -22 1390031 1390032 22 1390385 1390386 INV -22 1392261 1392262 22 1392477 1392478 INV -22 1392262 1392263 22 1392478 1392479 INV -22 1393405 1393406 22 1393497 1393498 DEL -22 1394684 1394687 22 1394810 1394813 DEL -22 1398624 1398625 22 1399068 1399069 INV -22 1398625 1398626 22 1399069 1399070 INV -22 1399981 1399982 22 1400381 1400382 DEL -22 1401216 1401217 22 1401267 1401268 INV -22 1401995 1401998 22 1402343 1402346 INV -22 1403402 1403404 22 1403593 1403595 INV -22 1403787 1403792 22 1403983 1403988 DEL -22 1404567 1404568 22 1404815 1404816 DUP -22 1405165 1405166 22 1405166 1405167 INS -22 1405647 1405648 22 1405759 1405760 DUP -22 1407220 1407221 22 1407438 1407439 INV -22 1407477 1407478 22 1407705 1407706 DUP -22 1410212 1410214 22 1410506 1410508 DEL -22 1415789 1415791 22 1416137 1416139 DEL -22 1418868 1418870 22 1419128 1419130 INV -22 1419435 1419436 22 1419510 1419511 INV -22 1420076 1420078 22 1420242 1420244 DEL -22 1420573 1420575 22 1420946 1420948 DUP -22 1421216 1421220 22 1421562 1421566 DUP -22 1422577 1422578 22 1422952 1422953 DUP -22 1423448 1423452 22 1423582 1423586 INV -22 1426743 1426744 22 1426916 1426917 DUP -22 1428646 1428649 22 1429053 1429056 INV -22 1429068 1429070 22 1429549 1429551 DUP -22 1430637 1430639 22 1430767 1430769 INV -22 1431107 1431110 22 1718279 1718282 DEL -22 1434440 1434442 22 1434930 1434932 DUP -22 1436149 1436150 22 1436150 1436151 INS -22 1436297 1436300 22 1436391 1436394 DEL -22 1438088 1438090 22 1438302 1438304 DEL -22 1441143 1441144 22 1441552 1441553 DUP -22 1444203 1444205 22 1444566 1444568 DEL -22 1447854 1447855 22 1448212 1448213 DUP -22 1449313 1449314 22 1449585 1449586 INV -22 1449314 1449315 22 1449586 1449587 INV -22 1454718 1454721 22 1454863 1454866 DUP -22 1457484 1457488 22 1457956 1457960 DUP -22 1458532 1458534 22 1458976 1458978 INV -22 1458533 1458535 22 1458977 1458979 INV -22 1459418 1459420 22 1459591 1459593 DUP -22 1460314 1460315 22 1460729 1460730 DEL -22 1461867 1461869 22 1462185 1462187 DEL -22 1466984 1466985 22 1467244 1467245 DEL -22 1470338 1470339 22 1470785 1470786 DEL -22 1474658 1474661 22 1474765 1474768 DEL -22 1476551 1476552 22 1476652 1476653 DEL -22 1477590 1477592 22 1477920 1477922 DEL -22 1482265 1482268 22 1482451 1482454 DEL -22 1482583 1482586 22 1483068 1483071 DEL -22 1485576 1485577 22 1485887 1485888 DUP -22 1486014 1486015 22 1486067 1486068 INV -22 1486747 1486750 22 1486858 1486861 DEL -22 1487642 1487644 22 1487906 1487908 INV -22 1487643 1487645 22 1487907 1487909 INV -22 1490034 1490038 22 1490139 1490143 DEL -22 1491927 1491928 22 1492300 1492301 DEL -22 1496069 1496071 22 1496312 1496314 DUP -22 1497248 1497249 22 1497668 1497669 DUP -22 1497986 1497989 22 1498282 1498285 DEL -22 1498627 1498628 22 1499006 1499007 INV -22 1498628 1498629 22 1499007 1499008 INV -22 1501214 1501216 22 1501620 1501622 DUP -22 1505305 1505306 22 1506230 1506231 INV -22 1506976 1506977 22 1507223 1507224 DEL -22 1508177 1508178 22 1508355 1508356 DUP -22 1508538 1508539 22 1508871 1508872 INV -22 1508539 1508540 22 1508872 1508873 INV -22 1509315 1509316 22 1509369 1509370 DEL -22 1512089 1512090 22 1512803 1512804 DUP -22 1514121 1514122 22 1514577 1514578 DUP -22 1515926 1515927 22 1650636 1650637 DUP -22 1517126 1517128 22 1517276 1517278 INV -22 1517961 1517962 22 1518457 1518458 DEL -22 1520471 1520473 22 1520558 1520560 INV -22 1521241 1521244 22 1521619 1521622 INV -22 1522980 1522981 22 1523050 1523051 INV -22 1523748 1523749 22 1523853 1523854 INV -22 1525175 1525179 22 1525517 1525521 DEL -22 1526778 1526779 22 1527159 1527160 DUP -22 1527273 1527274 22 1527572 1527573 INV -22 1527274 1527275 22 1527573 1527574 INV -22 1529654 1529656 22 1529784 1529786 DEL -22 1530019 1530020 22 1530237 1530238 DEL -22 1530338 1530339 22 1530613 1530614 DUP -22 1531380 1531381 22 1531642 1531643 DEL -22 1531845 1531846 22 1532082 1532083 DUP -22 1532401 1532404 22 1532750 1532753 INV -22 1532402 1532405 22 1532751 1532754 INV -22 1536511 1536514 22 1536678 1536681 DEL -22 1536807 1536809 22 1537268 1537270 DUP -22 1538919 1538920 22 1539056 1539057 DUP -22 1539619 1539621 22 1539911 1539913 DUP -22 1545633 1545636 22 1546026 1546029 DEL -22 1546776 1546777 22 1727310 1727311 DUP -22 1546860 1546865 22 1727409 1727414 DEL -22 1546861 1546866 22 1727408 1727413 DUP -22 1548214 1548215 22 1548417 1548418 DUP -22 1548522 1548524 22 1548662 1548664 DUP -22 1550117 1550119 22 1550268 1550270 DUP -22 1552474 1552478 22 1552782 1552786 DUP -22 1556566 1556567 22 1557191 1557192 DEL -22 1559243 1559244 22 1559319 1559320 DEL -22 1559676 1559677 22 1559960 1559961 DEL -22 1561627 1561630 22 1561710 1561713 DEL -22 1564530 1564534 22 1564953 1564957 DUP -22 1565768 1565769 22 1565861 1565862 DUP -22 1569537 1569539 22 1569793 1569795 DEL -22 1571782 1571785 22 1572197 1572200 DUP -22 1572577 1572578 22 1573048 1573049 INV -22 1572578 1572579 22 1573049 1573050 INV -22 1575577 1575578 22 1575708 1575709 INV -22 1576032 1576033 22 1576293 1576294 INV -22 1577037 1577038 22 1577359 1577360 DUP -22 1578913 1578914 22 1579188 1579189 DEL -22 1580312 1580313 22 1580467 1580468 DUP -22 1581323 1581324 22 1581668 1581669 DUP -22 1581828 1581829 22 1582079 1582080 INV -22 1581829 1581830 22 1582080 1582081 INV -22 1583530 1583531 22 1583629 1583630 DUP -22 1584094 1584095 22 1584188 1584189 INV -22 1585803 1585804 22 1586205 1586206 INV -22 1587646 1587649 22 1587905 1587908 INV -22 1589095 1589099 22 1589335 1589339 DEL -22 1590155 1590156 22 1590331 1590332 DUP -22 1590904 1590906 22 1591319 1591321 INV -22 1590905 1590907 22 1591320 1591322 INV -22 1596104 1596105 22 1596555 1596556 INV -22 1596105 1596106 22 1596556 1596557 INV -22 1598912 1598916 22 1599142 1599146 DEL -22 1600640 1600642 22 1601110 1601112 DEL -22 1601316 1601319 22 1601447 1601450 INV -22 1601805 1601806 22 1602392 1602393 INV -22 1601998 1601999 22 1602393 1602394 INV -22 1603393 1603396 22 1603538 1603541 DUP -22 1603650 1603652 22 1603750 1603752 DEL -22 1604373 1604375 22 1935881 1935883 DEL -22 1604374 1604376 22 1935880 1935882 DUP -22 1605167 1605168 22 1605650 1605651 DUP -22 1606056 1606059 22 1606123 1606126 INV -22 1608258 1608260 22 1608717 1608719 DEL -22 1610251 1610253 22 1610330 1610332 DUP -22 1610795 1610796 22 1611187 1611188 DUP -22 1611522 1611523 22 1611769 1611770 DEL -22 1612019 1612021 22 1612389 1612391 DUP -22 1612857 1612859 22 1612981 1612983 INV -22 1612858 1612860 22 1612982 1612984 INV -22 1613866 1613868 22 1614345 1614347 DUP -22 1614469 1614470 22 1614787 1614788 INV -22 1614470 1614471 22 1614788 1614789 INV -22 1615531 1615533 22 1615971 1615973 DEL -22 1616387 1616389 22 1616515 1616517 INV -22 1618540 1618542 22 1618718 1618720 INV -22 1618919 1618923 22 1619161 1619165 INV -22 1621816 1621817 22 1622309 1622310 DUP -22 1622421 1622423 22 1622610 1622612 DUP -22 1624152 1624153 22 1692496 1692497 DUP -22 1624447 1624448 22 1692851 1692852 DEL -22 1624957 1624959 22 1625199 1625201 INV -22 1626857 1626858 22 1626934 1626935 DUP -22 1628202 1628203 22 1628504 1628505 DUP -22 1628518 1628519 22 1628923 1628924 DEL -22 1629391 1629393 22 1629480 1629482 DUP -22 1631329 1631330 22 1631713 1631714 INV -22 1631330 1631331 22 1631714 1631715 INV -22 1633545 1633548 22 1633666 1633669 DEL -22 1638207 1638208 22 1638567 1638568 DEL -22 1639737 1639738 22 1639981 1639982 DUP -22 1643649 1643650 22 1643804 1643805 DUP -22 1644044 1644047 22 1644487 1644490 DUP -22 1644867 1644868 22 1645202 1645203 DEL -22 1647990 1647991 22 1648332 1648333 DUP -22 1650641 1650643 22 1651113 1651115 INV -22 1651461 1651462 22 1651590 1651591 DUP -22 1651795 1651797 22 1651866 1651868 INV -22 1651940 1651942 22 1652350 1652352 DEL -22 1653535 1653537 22 1653697 1653699 DEL -22 1656208 1656209 22 1656367 1656368 INV -22 1658834 1658835 22 1659166 1659167 DEL -22 1662019 1662024 22 1662437 1662442 DUP -22 1664572 1664573 22 1664872 1664873 DUP -22 1665225 1665226 22 1665711 1665712 INV -22 1665226 1665227 22 1665712 1665713 INV -22 1669325 1669326 22 1669326 1669327 INS -22 1671139 1671140 22 1671383 1671384 DEL -22 1672171 1672177 22 1672471 1672477 INV -22 1672763 1672764 22 1673026 1673027 INV -22 1672764 1672765 22 1673027 1673028 INV -22 1673141 1673142 22 1673264 1673265 DUP -22 1673282 1673284 22 1673553 1673555 DEL -22 1673955 1673956 22 1674281 1674282 INV -22 1673956 1673957 22 1674730 1674731 INV -22 1675375 1675378 22 1675642 1675645 DEL -22 1676479 1676482 22 1676669 1676672 DUP -22 1677320 1677321 22 1677525 1677526 DEL -22 1679683 1679684 22 1679985 1679986 DUP -22 1680696 1680697 22 1680816 1680817 DEL -22 1685186 1685187 22 1685682 1685683 DUP -22 1687267 1687268 22 1687758 1687759 INV -22 1687268 1687269 22 1687759 1687760 INV -22 1688487 1688490 22 1688704 1688707 DUP -22 1688873 1688874 22 1689135 1689136 INV -22 1688874 1688875 22 1689211 1689212 DUP -22 1694577 1694578 22 1695040 1695041 DUP -22 1696834 1696836 22 1697220 1697222 DEL -22 1698059 1698060 22 1698159 1698160 DEL -22 1698264 1698266 22 1698366 1698368 INV -22 1699299 1699304 22 1699499 1699504 DUP -22 1699751 1699752 22 1699955 1699956 DUP -22 1700081 1700082 22 1700443 1700444 DEL -22 1701929 1701931 22 1702001 1702003 INV -22 1701930 1701932 22 1702002 1702004 INV -22 1702423 1702425 22 1702704 1702706 DEL -22 1705283 1705285 22 1705458 1705460 DEL -22 1708709 1708710 22 1709016 1709017 DEL -22 1711092 1711094 22 1711449 1711451 DUP -22 1712671 1712672 22 1712845 1712846 INV -22 1712672 1712673 22 1712846 1712847 INV -22 1715587 1715588 22 1716043 1716044 DEL -22 1717242 1717244 22 1717524 1717526 INV -22 1719708 1719710 22 1719812 1719814 INV -22 1720282 1720283 22 1720479 1720480 DEL -22 1721171 1721173 22 1721231 1721233 INV -22 1721488 1721490 22 1721765 1721767 INV -22 1721489 1721491 22 1721766 1721768 INV -22 1722773 1722774 22 1723552 1723553 INV -22 1723370 1723372 22 1723551 1723553 INV -22 1724194 1724198 22 1724395 1724399 INV -22 1726313 1726314 22 1726798 1726799 DUP -22 1728410 1728411 22 1728563 1728564 INV -22 1729188 1729189 22 1729482 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DEL -12 1790675 1790685 12 1791148 1791158 DEL -12 988756 988774 12 1792898 1792918 DUP -12 882133 882145 12 1793876 1793889 DUP -12 882131 882151 12 1793872 1793892 DEL -12 882186 882197 12 1793930 1793940 DEL -12 1794987 1795003 12 1795268 1795288 DEL -12 1796340 1796360 12 1796550 1796570 DUP -12 1798848 1799237 12 1799980 1800583 DUP -12 1802827 1802847 12 1803198 1803218 DUP -12 1803331 1803351 12 1803669 1803689 DUP -12 277514 278214 12 1803097 1803745 DEL -12 1804131 1804151 12 1804489 1804509 DUP -12 1805208 1805228 12 1805692 1805708 DEL -12 1805747 1805751 12 1806140 1806144 DUP -12 1806424 1806428 12 1806837 1806841 DUP -12 1807315 1807335 12 1807602 1807622 DEL -12 1629659 1629670 12 1809188 1809196 DEL -12 1629723 1629735 12 1809250 1809262 DUP -12 1629726 1629732 12 1809254 1809260 DEL -12 1810135 1810155 12 1810407 1810427 DEL -12 1012432 1012877 12 1810277 1810682 DEL -12 1012882 1012902 12 1810951 1810971 DUP -12 1012882 1012902 12 1810959 1810965 DEL -12 1812628 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1847377 DUP -12 1852763 1852783 12 1852945 1852965 DUP -12 1549264 1549940 12 1854237 1854896 DUP -12 1855271 1855291 12 1855567 1855587 DEL -12 1856251 1856271 12 1856529 1856549 DEL -12 1856579 1856599 12 1856796 1856816 DUP -12 260798 260806 12 1862172 1862180 DUP -12 260792 260812 12 1862167 1862187 DEL -12 260862 260880 12 1862237 1862255 DUP -12 260863 260883 12 1862237 1862257 DEL -12 597337 597805 12 1861657 1862092 DEL -12 1065504 1066211 12 1862392 1863098 DUP -12 1787012 1787015 12 1862399 1862403 DUP -12 1862724 1862744 12 1862832 1862852 DEL -12 441765 442413 12 1864013 1864678 DEL -12 155368 155990 12 1864387 1864689 DEL -12 1868038 1868058 12 1868386 1868406 DUP -12 1868648 1868668 12 1868904 1868924 DEL -12 1869040 1869044 12 1869308 1869312 DUP -12 1869753 1869769 12 1870145 1870165 DEL -12 1870671 1870691 12 1870942 1870962 DUP -12 1875921 1875940 12 1876282 1876288 DEL -12 929313 929333 12 1877942 1877962 DUP -12 255664 256302 12 1877184 1877801 DEL -12 1630240 1630903 12 1877642 1878281 DEL -12 1879356 1879373 12 1879726 1879746 DEL -12 1880055 1880075 12 1880329 1880335 DEL -12 1881756 1881771 12 1882213 1882233 DUP -12 1882713 1882732 12 1882984 1883003 DEL -12 1886923 1886931 12 1887053 1887061 DEL -12 784975 785607 12 1887327 1887937 DUP -12 1887917 1887929 12 1888126 1888142 DUP -12 1890051 1890071 12 1890293 1890313 DEL -12 1893400 1893420 12 1893596 1893616 DEL -12 1792897 1793469 12 1893966 1894551 DEL -12 988900 988908 12 1894531 1894551 DEL -12 1895844 1895864 12 1896107 1896124 DUP -12 1898374 1898377 12 1898778 1898781 DEL -12 242411 242999 12 1899151 1899550 DEL -12 1903013 1903019 12 1903131 1903137 DEL -12 1903571 1903591 12 1903903 1903923 DUP -12 1557159 1557747 12 1906460 1907071 DEL -12 727788 728351 12 1907241 1907643 DUP -12 1908133 1908139 12 1908330 1908345 DUP -12 1469755 1469762 12 1909331 1909337 DUP -12 1469758 1469762 12 1909332 1909336 DEL -12 1470114 1470732 12 1909269 1909800 DEL -12 1911189 1911193 12 1911372 1911376 DEL -12 1354438 1354443 12 1912306 1912326 DUP -12 1912816 1912836 12 1913059 1913079 DUP -12 1396686 1396706 12 1916125 1916145 DEL -12 650498 650508 12 1917365 1917375 DUP -12 650500 650505 12 1917369 1917372 DEL -12 650640 650644 12 1917508 1917512 DUP -12 1918064 1918081 12 1918308 1918312 DEL -12 570683 571016 12 1918555 1918783 DUP -12 571279 571299 12 1918978 1918983 DEL -12 26636 26642 12 1919102 1919109 DUP -12 683678 684370 12 1919394 1919843 DUP -12 26636 26642 12 1919104 1919110 DEL -12 1690391 1690410 12 1919560 1919580 DEL -12 324494 325133 12 1919732 1920386 DUP -12 1025915 1025921 12 1919733 1919753 DUP -12 1919861 1919881 12 1919976 1919996 DEL -12 1920378 1920386 12 1920598 1920606 DEL -12 1926574 1926594 12 1926758 1926778 DEL -12 1929399 1929408 12 1929801 1929810 DUP -12 1931041 1931057 12 1931362 1931367 DUP -12 1931469 1931475 12 1931839 1931847 DEL -12 684466 684486 12 1932866 1932886 DUP -12 1021390 1021410 12 1934070 1934090 DUP -12 1934444 1934454 12 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-22 1340558 1340578 22 1340657 1340677 DEL -22 798013 798022 22 1341810 1341830 DUP -22 798017 798023 22 1341816 1341822 DEL -22 798093 798101 22 1341894 1341902 DUP -22 798095 798101 22 1341888 1341908 DEL -22 1342044 1342064 22 1342413 1342433 DUP -22 1343562 1343577 22 1344043 1344047 DUP -22 98381 98385 22 1345484 1345488 DUP -22 98373 98393 22 1345477 1345497 DEL -22 98480 98498 22 1345583 1345602 DUP -22 98483 98494 22 1345586 1345597 DEL -22 1348590 1348610 22 1348886 1348902 DEL -22 1356648 1356650 22 1356771 1356773 DEL -22 1148255 1148275 22 1364098 1364118 DUP -22 1148324 1148332 22 1364169 1364177 DUP -22 1148327 1148333 22 1364162 1364182 DEL -22 723755 724297 22 1365666 1366160 DUP -22 1372038 1372058 22 1372222 1372242 DUP -22 1372626 1372646 22 1372856 1372876 DEL -22 1374689 1374709 22 1374814 1374834 DEL -22 1378688 1378707 22 1378785 1378794 DEL -22 1379537 1379557 22 1379696 1379716 DUP -22 104166 104472 22 1379924 1380505 DEL -22 1042443 1043148 22 1380676 1381379 DEL -22 1042647 1042958 22 1381490 1382161 DUP -22 1385955 1385975 22 1386139 1386159 DUP -22 1387986 1387996 22 1388479 1388489 DUP -22 1393395 1393415 22 1393487 1393507 DEL -22 1065956 1065971 22 1394015 1394022 DUP -22 1394676 1394696 22 1394800 1394820 DEL -22 163898 163903 22 1399507 1399527 DEL -22 1203091 1203678 22 1398849 1399524 DEL -22 1399971 1399991 22 1400374 1400391 DEL -22 1189205 1189880 22 1403231 1403639 DUP -22 1403781 1403797 22 1403974 1403993 DEL -22 1404556 1404576 22 1404805 1404825 DUP -22 1404919 1404939 22 1404993 1405013 DEL -22 479284 479640 22 1405697 1406035 DEL -22 898020 898659 22 1406339 1406774 DUP -22 1316542 1317169 22 1406927 1407279 DUP -22 1316820 1316838 22 1406688 1406706 DEL -22 1407466 1407486 22 1407695 1407715 DUP -22 1410203 1410223 22 1410496 1410516 DEL -22 711283 711303 22 1411091 1411111 DEL -22 711283 711303 22 1411090 1411110 DUP -22 68376 68990 22 1413708 1414390 DEL -22 1415780 1415800 22 1416127 1416147 DEL -22 1420067 1420087 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DUP -22 312804 313415 22 1454515 1455110 DUP -22 437247 437741 22 1456965 1457435 DUP -22 1459407 1459427 22 1459582 1459602 DUP -22 1460305 1460322 22 1460720 1460735 DEL -22 1460972 1460985 22 1461195 1461207 DUP -22 1461464 1461472 22 1461654 1461662 DUP -22 1461859 1461874 22 1462175 1462195 DEL -22 1139664 1140270 22 1463326 1463726 DEL -22 1466980 1466988 22 1467238 1467254 DEL -22 1468490 1468507 22 1468986 1468993 DEL -22 731142 731723 22 1468802 1469444 DEL -22 1470328 1470348 22 1470782 1470787 DEL -22 1474650 1474670 22 1474755 1474775 DEL -22 404793 404813 22 1475531 1475538 DUP -22 1476074 1476084 22 1476386 1476397 DUP -22 1476541 1476561 22 1476642 1476662 DEL -22 1477589 1477593 22 1477918 1477922 DEL -22 443139 443159 22 1478209 1478229 DEL -22 425796 426283 22 1479802 1480268 DUP -22 1216367 1216780 22 1479509 1479878 DEL -22 1137166 1137499 22 1481423 1481960 DUP -22 1482263 1482270 22 1482448 1482454 DEL -22 1482583 1482587 22 1483066 1483070 DEL -22 274590 274602 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1518467 DEL -22 1519057 1519077 22 1519385 1519405 DUP -22 1521756 1521776 22 1521915 1521935 DEL -22 1343119 1343125 22 1524227 1524233 DUP -22 1525175 1525180 22 1525507 1525527 DEL -22 1212201 1212634 22 1526274 1526686 DUP -22 1212711 1213306 22 1526221 1526719 DEL -22 1526767 1526787 22 1527149 1527169 DUP -22 1529645 1529665 22 1529774 1529794 DEL -22 1530009 1530029 22 1530227 1530247 DEL -22 1530327 1530347 22 1530603 1530623 DUP -22 1531370 1531390 22 1531632 1531652 DEL -22 1531834 1531854 22 1532072 1532092 DUP -22 1536503 1536523 22 1536669 1536689 DEL -22 1536803 1536808 22 1537267 1537271 DUP -22 1539612 1539628 22 1539902 1539922 DUP -22 1545625 1545645 22 1546023 1546030 DEL -22 1548211 1548215 22 1548415 1548419 DUP -22 244712 244725 22 1549144 1549164 DUP -22 244719 244724 22 1549145 1549165 DEL -22 181436 181692 22 1548500 1549165 DEL -22 182002 182007 22 1549424 1549431 DUP -22 181995 182015 22 1549418 1549438 DEL -22 245088 245377 22 1549170 1549591 DEL -22 1550111 1550126 22 1550259 1550279 DUP -22 178944 179500 22 1551251 1551637 DUP -22 1233381 1233735 22 1551324 1552021 DEL -22 1233140 1233652 22 1552095 1552512 DUP -22 1233611 1233631 22 1552092 1552112 DEL -22 1552859 1552869 22 1553229 1553239 DUP -22 495804 495814 22 1556028 1556038 DUP -22 495800 495820 22 1556030 1556035 DEL -22 495966 495970 22 1556179 1556199 DUP -22 1556562 1556569 22 1556675 1556681 DEL -22 1556757 1556763 22 1557181 1557201 DEL -22 1557540 1557550 22 1557799 1557809 DEL -22 1559233 1559253 22 1559309 1559329 DEL -22 1559666 1559686 22 1559958 1559962 DEL -22 1561619 1561639 22 1561700 1561720 DEL -22 1564519 1564539 22 1564946 1564966 DUP -22 1569528 1569548 22 1569785 1569803 DEL -22 1571778 1571784 22 1572189 1572208 DUP -22 1577034 1577038 22 1577357 1577361 DUP -22 1578903 1578923 22 1579181 1579198 DEL -22 1580308 1580314 22 1580464 1580470 DUP -22 1581312 1581332 22 1581658 1581678 DUP -22 1417428 1417774 22 1582622 1583291 DEL -22 1152641 1153322 22 1583392 1583981 DUP -22 1585136 1585156 22 1585501 1585521 DEL -22 1589095 1589101 22 1589325 1589333 DEL -22 1590152 1590156 22 1590329 1590333 DUP -22 1598913 1598917 22 1599136 1599152 DEL -22 1600640 1600644 22 1601107 1601112 DEL -22 656514 656518 22 1601795 1601815 DUP -22 656512 656519 22 1601803 1601809 DEL -22 1603641 1603656 22 1603740 1603760 DEL -22 1241343 1241799 22 1603511 1603996 DEL -22 1241730 1241735 22 1604299 1604306 DUP -22 1241723 1241743 22 1604294 1604314 DEL -22 709384 709925 22 1604789 1605195 DUP -22 1608254 1608264 22 1608707 1608727 DEL -22 1610791 1610797 22 1611183 1611190 DUP -22 1611512 1611532 22 1611763 1611779 DEL -22 1612012 1612028 22 1612380 1612400 DUP -22 1613859 1613870 22 1614336 1614356 DUP -22 1615529 1615535 22 1615968 1615974 DEL -22 1618103 1618543 22 1619153 1619732 DUP -22 1621812 1621817 22 1622299 1622315 DUP -22 1622417 1622423 22 1622608 1622614 DUP -22 881772 882246 22 1622433 1622911 DEL -22 738307 738845 22 1623273 1623621 DUP -22 1249236 1249699 22 1627060 1627517 DUP -22 1450669 1451278 22 1627816 1628389 DUP -22 1628199 1628203 22 1628494 1628513 DUP -22 1628516 1628520 22 1628921 1628925 DEL -22 1633537 1633557 22 1633656 1633676 DEL -22 239678 240052 22 1636963 1637348 DUP -22 240225 240240 22 1637344 1637364 DEL -22 1638203 1638210 22 1638557 1638577 DEL -22 1639726 1639746 22 1639977 1639984 DUP -22 1408897 1409457 22 1641171 1641531 DEL -22 857070 857437 22 1641744 1642320 DUP -22 1643643 1643658 22 1643794 1643814 DUP -22 1644039 1644046 22 1644479 1644499 DUP -22 1644865 1644869 22 1645192 1645212 DEL -22 140191 140211 22 1647840 1647860 DUP -22 140291 140311 22 1647947 1647967 DUP -22 140299 140303 22 1647956 1647960 DEL -22 1647979 1647999 22 1648322 1648342 DUP -22 1515551 1515935 22 1650629 1651027 DUP -22 1651931 1651951 22 1652340 1652360 DEL -22 1652611 1652631 22 1652935 1652955 DEL -22 1653526 1653546 22 1653687 1653707 DEL -22 1658831 1658836 22 1659156 1659176 DEL -22 646121 646133 22 1661257 1661273 DUP -22 646125 646129 22 1661266 1661270 DEL -22 1662008 1662028 22 1662431 1662451 DUP -22 1664561 1664581 22 1664862 1664878 DUP -22 1478818 1478838 22 1667470 1667474 DEL -22 1667763 1667783 22 1667911 1667931 DEL -22 1670337 1670357 22 1670471 1670491 DEL -22 1671129 1671149 22 1671373 1671393 DEL -22 1671561 1672180 22 1673019 1673614 DUP -22 1673273 1673293 22 1673543 1673563 DEL -22 1675373 1675380 22 1675639 1675645 DEL -22 1675793 1675804 22 1676288 1676308 DEL -22 1676470 1676490 22 1676661 1676681 DUP -22 1677315 1677325 22 1677520 1677530 DEL -22 1679672 1679692 22 1679975 1679995 DUP -22 1680686 1680705 22 1680806 1680826 DEL -22 1681109 1681129 22 1681470 1681490 DEL -22 1159121 1159131 22 1682081 1682091 DUP -22 1159117 1159137 22 1682076 1682096 DEL -22 351286 351302 22 1682609 1682629 DUP -22 351290 351294 22 1682615 1682630 DEL -22 1685180 1685189 22 1685677 1685687 DUP -22 1688476 1688496 22 1688696 1688716 DUP -22 1688868 1688878 22 1689206 1689216 DUP -22 1624144 1624161 22 1692486 1692506 DUP -22 1624141 1624161 22 1692487 1692507 DEL -22 1624441 1624717 22 1692573 1692854 DEL -22 1693393 1693405 22 1693534 1693546 DEL -22 1694566 1694586 22 1695030 1695050 DUP -22 1696832 1696838 22 1697217 1697224 DEL -22 1698056 1698062 22 1698156 1698162 DEL -22 1699289 1699308 22 1699500 1699505 DUP -22 1699741 1699760 22 1699946 1699965 DUP -22 1700078 1700083 22 1700433 1700453 DEL -22 1702414 1702433 22 1702695 1702714 DEL -22 1041832 1042175 22 1703441 1703802 DEL -22 1704990 1705008 22 1705227 1705246 DUP -22 1705282 1705286 22 1705456 1705460 DEL -22 1705641 1705649 22 1706033 1706041 DEL -22 1708706 1708712 22 1709013 1709019 DEL -22 1710027 1710039 22 1710233 1710245 DEL -22 1711087 1711095 22 1711446 1711454 DUP -22 1715585 1715589 22 1716033 1716053 DEL -22 1716865 1716885 22 1717033 1717053 DEL -22 1081803 1081809 22 1718262 1718282 DUP -22 1081548 1081932 22 1717740 1718127 DEL -22 1431099 1431119 22 1718269 1718289 DEL -22 1718785 1718790 22 1718842 1718861 DEL -22 1495730 1496325 22 1718292 1718923 DEL -22 1720272 1720292 22 1720472 1720489 DEL -22 1722763 1722783 22 1723260 1723280 DEL -22 1726308 1726315 22 1726794 1726802 DUP -22 1546765 1546785 22 1727300 1727320 DUP -22 1546859 1546861 22 1727411 1727413 DUP -22 1546854 1546874 22 1727399 1727419 DEL -22 1729183 1729192 22 1729472 1729492 DUP -22 1729755 1729766 22 1730188 1730198 DEL -22 1731413 1731433 22 1731795 1731815 DUP -22 1556898 1557361 22 1732211 1732612 DUP -22 980970 981373 22 1731828 1732229 DEL -22 1733779 1733794 22 1734020 1734040 DUP -22 1734921 1734937 22 1735241 1735261 DUP -22 1737766 1737785 22 1737844 1737851 DEL -22 411003 411592 22 1739675 1740275 DEL -22 1740541 1740546 22 1740764 1740769 DUP -22 1743959 1743979 22 1744304 1744324 DUP -22 1748150 1748156 22 1748263 1748269 DEL -22 1749223 1749242 22 1749384 1749391 DUP -22 16836 17244 22 1752025 1752562 DUP -22 495661 496130 22 1756285 1756761 DUP -22 1757394 1757414 22 1757751 1757766 DEL -22 1757883 1757903 22 1758107 1758127 DUP -22 1758486 1758504 22 1758679 1758697 DEL -22 1758977 1758987 22 1759075 1759085 DEL -22 1759277 1759297 22 1759492 1759512 DUP -22 1760983 1761003 22 1761363 1761383 DUP -22 1764958 1764963 22 1765431 1765451 DUP -22 886401 886822 22 1766961 1767620 DUP -22 1243733 1244047 22 1768515 1769073 DEL -22 1769781 1769801 22 1769927 1769947 DEL -22 1770394 1770414 22 1770617 1770637 DUP -22 1772299 1772305 22 1772492 1772498 DUP -22 1773321 1773341 22 1773766 1773779 DUP -22 653340 653769 22 1777360 1778063 DEL -22 653700 653702 22 1778144 1778161 DUP -22 545437 546107 22 1779567 1780255 DEL -22 297485 298104 22 1780117 1780794 DEL -22 297800 297820 22 1780888 1780908 DUP -22 1781893 1781913 22 1782230 1782250 DEL -22 1782356 1782365 22 1782609 1782617 DEL -22 1782851 1782857 22 1783039 1783045 DEL -22 484172 484781 22 1783989 1784693 DUP -22 1785075 1785081 22 1785299 1785305 DUP -22 1465034 1465040 22 1788546 1788566 DUP -22 1465029 1465047 22 1788548 1788566 DEL -22 1232415 1232913 22 1792301 1792797 DUP -22 1793388 1793731 22 1794631 1794966 DUP -22 1280856 1281258 22 1801279 1801682 DEL -22 1802434 1802437 22 1802805 1802825 DEL -22 1804862 1804865 22 1805309 1805313 DUP -22 1751306 1752008 22 1809104 1809811 DEL -22 1752026 1752046 22 1810151 1810156 DUP -22 1752026 1752042 22 1810150 1810160 DEL -22 16880 17323 22 1810241 1810728 DUP -22 1810421 1810441 22 1810665 1810685 DUP -22 1812877 1812897 22 1813165 1813185 DUP -22 1116684 1117287 22 1814046 1814667 DEL -22 1817513 1817525 22 1817790 1817802 DEL -22 1818833 1818852 22 1819011 1819017 DEL -22 1821652 1821665 22 1822028 1822048 DEL -22 963378 964016 22 1824634 1825300 DUP -22 1827960 1827965 22 1828438 1828441 DEL -22 1829893 1829902 22 1830357 1830365 DUP -22 1831131 1831149 22 1831610 1831618 DEL -22 1831637 1831657 22 1831940 1831960 DUP -22 1832513 1832518 22 1832595 1832615 DEL -22 1832770 1832774 22 1833018 1833022 DUP -22 1079830 1079837 22 1833841 1833847 DEL -22 1080100 1080116 22 1834074 1834093 DUP -22 1836506 1836526 22 1836939 1836948 DUP -22 1837650 1837670 22 1838018 1838038 DUP -22 1839276 1839296 22 1839590 1839610 DUP -22 1839763 1839783 22 1840045 1840065 DUP -22 1840639 1840641 22 1841103 1841105 DEL -22 1841582 1841602 22 1841919 1841939 DUP -22 1843924 1843930 22 1844413 1844418 DEL -22 1844666 1844686 22 1844892 1844912 DUP -22 1850625 1850645 22 1850960 1850980 DUP -22 506589 506601 22 1851435 1851447 DUP -22 1851787 1851807 22 1852269 1852289 DEL -22 1739275 1739294 22 1854418 1854425 DUP -22 1739275 1739293 22 1854414 1854432 DEL -22 438455 438789 22 1854121 1854429 DEL -22 1739480 1739844 22 1854432 1854767 DEL -22 1855160 1855180 22 1855501 1855521 DUP -22 1857566 1857571 22 1857656 1857676 DEL -22 1858632 1858639 22 1859104 1859112 DUP -22 1859727 1859747 22 1859937 1859944 DUP -22 1861105 1861125 22 1861453 1861473 DUP -22 1862668 1862673 22 1863074 1863080 DEL -22 1863263 1863265 22 1863574 1863592 DEL -22 715219 715576 22 1866191 1866512 DUP -22 582274 582292 22 1867351 1867369 DUP -22 1833606 1833626 22 1867358 1867363 DEL -22 582469 583021 22 1867081 1867685 DEL -22 1868623 1868627 22 1869104 1869124 DEL -22 1870205 1870213 22 1870580 1870588 DEL -22 1871590 1871610 22 1872046 1872066 DUP -22 1874572 1874581 22 1875062 1875082 DUP -22 1879995 1880005 22 1880481 1880501 DUP -22 1880514 1880522 22 1880946 1880954 DUP -22 1881902 1881922 22 1882108 1882128 DEL -22 1885418 1885438 22 1885716 1885736 DUP -22 1889472 1889474 22 1889753 1889755 DEL -22 1860482 1860489 22 1889837 1889857 DUP -22 1860476 1860496 22 1889845 1889850 DEL -22 1893199 1893219 22 1893314 1893334 DEL -22 1893582 1893588 22 1893722 1893737 DEL -22 1893787 1893795 22 1893990 1893998 DUP -22 1894060 1894080 22 1894241 1894261 DEL -22 1895134 1895154 22 1895246 1895266 DEL -22 1155886 1155906 22 1897022 1897040 DEL -22 11440 12119 22 1896385 1897041 DEL -22 1897609 1897629 22 1897771 1897791 DUP -22 223029 223736 22 1898039 1898686 DUP -22 1901414 1901420 22 1901614 1901620 DUP -22 1214712 1214732 22 1902380 1902385 DEL -22 1214913 1214933 22 1902570 1902573 DUP -22 445833 446539 22 1901873 1902580 DEL -22 1214920 1214926 22 1902570 1902576 DEL -22 1903699 1903708 22 1904193 1904213 DUP -22 1904589 1904596 22 1904678 1904698 DEL -22 1282921 1283556 22 1906187 1906650 DUP -22 1283619 1283638 22 1906377 1906384 DUP -22 1283625 1283634 22 1906371 1906391 DEL -22 1906868 1906888 22 1907041 1907061 DUP -22 1907331 1907349 22 1907613 1907620 DUP -22 1377825 1377828 22 1911327 1911331 DEL -22 1912361 1912381 22 1912799 1912802 DEL -22 1913583 1913591 22 1913823 1913831 DEL -22 1916972 1916992 22 1917174 1917194 DEL -22 1918016 1918024 22 1918500 1918507 DEL -22 856613 857000 22 1919882 1920157 DUP -22 1104753 1105182 22 1919107 1919717 DEL -22 857064 857084 22 1920033 1920053 DUP -22 857071 857079 22 1920039 1920047 DEL -22 1920969 1920989 22 1921385 1921405 DEL -22 1922118 1922138 22 1922432 1922452 DUP -22 1922790 1922803 22 1922962 1922982 DEL -22 1923044 1923056 22 1923528 1923548 DEL -22 1926104 1926108 22 1926361 1926378 DUP -22 1927127 1927147 22 1927431 1927451 DUP -22 1931836 1931842 22 1932304 1932324 DEL -22 1933816 1933836 22 1934049 1934069 DEL -22 1934775 1934781 22 1935045 1935051 DUP -22 1603978 1604093 22 1935272 1935539 DEL -22 1604363 1604383 22 1935871 1935891 DUP -22 1604369 1604379 22 1935876 1935886 DEL -22 1241683 1242196 22 1935175 1935891 DEL -22 1207278 1207286 22 1938285 1938293 DUP -22 1207280 1207284 22 1938288 1938292 DEL -22 1942325 1942331 22 1942552 1942567 DUP -22 184009 184587 22 1942114 1942790 DEL -22 870245 870265 22 1942770 1942790 DEL -22 1943413 1943433 22 1943615 1943635 DEL -22 282442 282462 22 1945968 1945988 DEL -22 1952862 1952882 22 1952954 1952970 DEL -22 1953274 1953280 22 1953648 1953654 DUP -22 1953678 1953684 22 1954021 1954027 DEL -22 987559 988139 22 1959808 1960411 DUP -22 1959897 1959917 22 1959980 1960000 DEL -22 887195 887215 22 1961043 1961047 DUP -22 886914 887247 22 1960335 1960895 DEL -22 887196 887216 22 1961035 1961055 DEL -22 78242 78903 22 1960690 1961359 DEL -22 529950 530335 22 1961701 1962309 DUP -22 530613 530633 22 1962142 1962157 DEL -22 1963992 1964012 22 1964117 1964125 DEL -22 1965549 1966269 22 1966583 1967212 DUP -22 959298 960005 22 1969824 1970407 DUP -22 1969027 1969308 22 1969182 1969443 DEL -22 1364268 1364918 22 1969995 1970651 DUP -22 561348 561368 22 1969992 1970012 DUP -22 561350 561370 22 1969991 1970011 DEL -22 960070 960510 22 1969779 1970206 DEL -22 1971087 1971103 22 1971544 1971564 DEL -22 1971599 1971619 22 1972013 1972033 DUP -22 823765 824128 22 1973519 1974087 DEL -22 1974792 1974812 22 1975044 1975064 DEL -22 1975180 1975200 22 1975368 1975388 DUP -22 1976323 1976343 22 1976423 1976429 DEL -22 1977023 1977043 22 1977154 1977174 DEL -22 1040906 1041610 22 1977676 1978330 DUP -22 1978514 1978517 22 1978980 1979000 DUP -22 1381491 1381511 22 1979109 1979114 DUP -22 1381496 1381506 22 1979108 1979118 DEL -22 1042906 1042914 22 1979153 1979173 DUP -22 1042904 1042915 22 1979154 1979174 DEL -22 1381592 1382298 22 1978786 1979424 DEL -22 1979581 1979586 22 1979960 1979980 DEL -22 1052732 1052752 22 1985179 1985199 DEL -22 533662 534345 22 1985296 1985713 DUP -22 278633 278653 22 1985291 1985311 DUP -22 1987311 1987331 22 1987402 1987422 DEL -22 1989158 1989168 22 1989527 1989537 DUP -22 1209911 1209923 22 1990267 1990279 DEL -22 1989648 1989668 22 1990135 1990140 DEL -22 1990589 1990609 22 1991049 1991055 DUP -22 1993023 1993043 22 1993309 1993329 DUP -22 1995161 1995181 22 1995460 1995480 DUP -22 1804588 1805249 22 1996770 1997438 DEL -22 1997873 1997879 22 1998237 1998243 DUP -12 622442 622461 12 622574 622593 INV -12 933522 933524 12 933880 933882 INV -12 13803 13807 12 13952 13968 INV -12 15230 15250 12 15677 15697 INV -12 15900 15902 12 16049 16051 INV -12 27763 27769 12 27939 27945 INV -12 35399 35414 12 35673 35679 INV -12 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INV -12 382183 382201 12 382463 382481 INV -12 382521 382535 12 382603 382620 INV -12 386557 386562 12 386693 386698 INV -12 386741 386745 12 386938 386942 INV -12 394571 394585 12 394881 394896 INV -12 396157 396168 12 396221 396236 INV -12 400917 400921 12 401269 401273 INV -12 409220 409226 12 409659 409664 INV -12 419950 419951 12 420385 420386 INV -12 422963 422983 12 423361 423365 INV -12 424937 424955 12 425023 425032 INV -12 433298 433310 12 433387 433394 INV -12 436335 436353 12 436698 436702 INV -12 437366 437368 12 437449 437451 INV -12 437950 437969 12 438457 438459 INV -12 442781 442783 12 443227 443229 INV -12 443422 443441 12 443715 443717 INV -12 444129 444133 12 444390 444407 INV -12 444624 444628 12 444725 444729 INV -12 449290 449310 12 449544 449561 INV -12 452183 452199 12 452246 452253 INV -12 453543 453553 12 453615 453626 INV -12 456441 456446 12 456793 456810 INV -12 472798 472808 12 472902 472921 INV -12 473697 473699 12 473805 473807 INV -12 498269 498271 12 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05944c11..2dc4f365 100644 --- a/environment.yaml +++ b/environment.yaml @@ -1,22 +1,28 @@ +--- channels: - conda-forge - bioconda - nlesc dependencies: - - intervaltree - - pysam - - numpy - - pandas - - bz2file - - plotnine - - pybigwig - - twobitreader - - bcolz - - scikit-learn + - intervaltree=3.1.0 + - pysam=0.16.0 + - pandas=1.1.4 + - bz2file=0.98 + - plotnine=0.7.1 + - pybigwig=0.3.17 + - twobitreader=3.1.7 + - bcolz=1.2.1 + - scikit-learn=0.23.2 - tensorflow>=2.0.0 - - r-argparser - - bioconductor-structuralvariantannotation + - r-base=4.0.3 + - r-argparser=0.6 + - bioconductor-structuralvariantannotation=1.6 - xenon-cli=3.0.5 - - jq - - cigar - - coreutils + - jq=1.6 + - cigar=0.1.3 + - coreutils=8.32 + - samtools=1.11 + - genmap=1.3.0 + - ucsc-bedgraphtobigwig=377 + - ucsc-fatotwobit=377 + - seqkit=0.14.0 diff --git a/genmap.sh b/genmap.sh new file mode 100755 index 00000000..4cf8ea7b --- /dev/null +++ b/genmap.sh @@ -0,0 +1,25 @@ +#!/usr/bin/env bash + +set -xe + +# check input arg(s) +if [ $# -ne "4" ]; then + echo "Usage: $0 [FASTA] [BIGWIG] [k-mers] [# mismatches]" + exit 1 +fi + +FASTA=$1 +BIGWIG=$2 +KMERS=$3 +MAX_MISMATCH=$4 +BASE_DIR="$(dirname "$FASTA")" +FAI="$FASTA.fai" +INDEX="$BASE_DIR/genmap_index" +MAP="$BASE_DIR/genmap_K${KMERS}_E${MAX_MISMATCH}" + +rm -fr "$INDEX" "$MAP*" "$FAI*" +samtools faidx -o "$FAI" "$FASTA" +cut -f 1,2 "$FAI" > "$FAI.sizes" +genmap index -F "$FASTA" -I "$INDEX" +genmap map -K "$KMERS" -E "$MAX_MISMATCH" -I "$INDEX" -O "$MAP" -bg +bedGraphToBigWig "$MAP.bedgraph" "$FAI.sizes" "$BIGWIG" diff --git a/notebooks/Flowchart.ipynb b/notebooks/Flowchart.ipynb index a27aaea6..2b6f0ead 100644 --- a/notebooks/Flowchart.ipynb +++ b/notebooks/Flowchart.ipynb @@ -1,971 +1,390 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "#!conda install -y -c anaconda pydot \n", - "#!conda install -y -c anaconda networkx\n", - "\n", - "import networkx as nx\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.image as mpimg\n", - "from io import StringIO\n", - "from IPython.display import SVG\n", - "import pydot" - ] + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.10" + }, + "colab": { + "name": "Flowchart.ipynb", + "provenance": [], + "collapsed_sections": [] + } }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ + "cells": [ + { + "cell_type": "code", + "metadata": { + "id": "7uvMRvcz37KB" + }, + "source": [ + "#!conda install -y -c anaconda pydot \n", + "#!conda install -y -c anaconda networkx\n", + "\n", + "import networkx as nx\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as mpimg\n", + "from io import StringIO\n", + "from IPython.display import SVG\n", + "import pydot" + ], + "execution_count": 1, + "outputs": [] + }, { - "data": { - "image/svg+xml": [ - "<svg height=\"949pt\" viewBox=\"0.00 0.00 1444.14 949.20\" width=\"1444pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", - "<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 945.2)\">\n", - "<title>G</title>\n", - "<polygon fill=\"#ffffff\" points=\"-4,4 -4,-945.2 1440.141,-945.2 1440.141,4 -4,4\" stroke=\"transparent\"/>\n", - "<!-- by chromosome -->\n", - "<g class=\"node\" id=\"node1\">\n", - "<title>by chromosome</title>\n", - "<ellipse cx=\"457\" cy=\"-833.4\" fill=\"none\" rx=\"72.9793\" ry=\"18\" stroke=\"#000000\"/>\n", - "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"457\" y=\"-829.2\">by chromosome</text>\n", - 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"source": [ + "dot_graph = pydot.Dot(graph_type='digraph')\n", + "\n", + "test_bam_node = pydot.Node('Read alignment\\n(BAM)')\n", + "test_bam_node.set_shape('box3d')\n", + "dot_graph.add_node(test_bam_node)\n", + "\n", + "test_gem_node = pydot.Node('GEM mappability track\\n(BigWig)')\n", + "test_gem_node.set_shape('box3d')\n", + "dot_graph.add_node(test_gem_node)\n", + "\n", + "test_2bit_node = pydot.Node('Reference genome\\n(2BIT)')\n", + "test_2bit_node.set_shape('box3d')\n", + "dot_graph.add_node(test_2bit_node)\n", + "\n", + "test_truth_set_vcf_node = pydot.Node('Truth set\\n(VCF)')\n", + "test_truth_set_vcf_node.set_shape('box3d')\n", + "dot_graph.add_node(test_truth_set_vcf_node)\n", + "\n", + "test_truth_set_bedpe_node = pydot.Node('Truth set\\n(BEDPE)')\n", + "test_truth_set_bedpe_node.set_shape('box3d')\n", + "dot_graph.add_node(test_truth_set_bedpe_node)\n", + "\n", + "iedge = pydot.Edge(test_truth_set_vcf_node, test_truth_set_bedpe_node)\n", + "iedge.set_label('vcf2bedpe.R')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "clipped_reads_py_node = pydot.Node('clipped_reads.py')\n", + "clipped_reads_py_node.set_shape('box')\n", + "dot_graph.add_node(clipped_reads_py_node)\n", + "\n", + "clipped_reads_node = pydot.Node('clipped_reads\\n(JSON.GZ)')\n", + "clipped_reads_node.set_shape('box3d')\n", + "dot_graph.add_node(clipped_reads_node)\n", + "\n", + "iedge = pydot.Edge(test_bam_node, clipped_reads_py_node)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "iedge = pydot.Edge(clipped_reads_py_node, clipped_reads_node)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "clipped_read_pos_py_node = pydot.Node('clipped_read_pos.py')\n", + "clipped_read_pos_py_node.set_shape('box')\n", + "dot_graph.add_node(clipped_read_pos_py_node)\n", + "\n", + "clipped_read_pos_node = pydot.Node('clipped_read_pos\\n(JSON.GZ)')\n", + "clipped_read_pos_node.set_shape('box3d')\n", + "dot_graph.add_node(clipped_read_pos_node)\n", + "\n", + "iedge = pydot.Edge(test_bam_node, clipped_read_pos_py_node)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "iedge = pydot.Edge(clipped_read_pos_py_node, clipped_read_pos_node)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "split_reads_py_node = pydot.Node('split_reads.py')\n", + "split_reads_py_node.set_shape('box')\n", + "dot_graph.add_node(split_reads_py_node)\n", + "\n", + "split_reads_node = pydot.Node('split_reads\\n(JSON.GZ)')\n", + "split_reads_node.set_shape('box3d')\n", + "dot_graph.add_node(split_reads_node)\n", + "\n", + "iedge = pydot.Edge(test_bam_node, split_reads_py_node)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "iedge = pydot.Edge(split_reads_py_node, split_reads_node)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "clipped_read_distance_py_node = pydot.Node('clipped_read_distance.py')\n", + "clipped_read_distance_py_node.set_shape('box')\n", + "dot_graph.add_node(clipped_read_distance_py_node)\n", + "\n", + "clipped_read_distance_node = pydot.Node('clipped_read_distance\\n(JSON.GZ)')\n", + "clipped_read_distance_node.set_shape('box3d')\n", + "dot_graph.add_node(clipped_read_distance_node)\n", + "\n", + "iedge = pydot.Edge(test_bam_node, clipped_read_distance_node)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "iedge = pydot.Edge(clipped_read_distance_py_node, clipped_read_distance_node)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "coverage_py_node = pydot.Node('coverage.py')\n", + "coverage_py_node.set_shape('box')\n", + "dot_graph.add_node(coverage_py_node)\n", + "\n", + "coverage_node = pydot.Node('coverage\\n(NPY.GZ)')\n", + "coverage_node.set_shape('box3d')\n", + "dot_graph.add_node(coverage_node)\n", + "\n", + "iedge = pydot.Edge(coverage_py_node, coverage_node)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "snv_py_node = pydot.Node('snv.py')\n", + "snv_py_node.set_shape('box')\n", + "dot_graph.add_node(snv_py_node)\n", + "\n", + "snv_node = pydot.Node('snv\\n(NPY.GZ)')\n", + "snv_node.set_shape('box3d')\n", + "dot_graph.add_node(snv_node)\n", + "\n", + "iedge = pydot.Edge(snv_py_node, snv_node)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "chr_array_py_node = pydot.Node('chr_array.py')\n", + "chr_array_py_node.set_shape('box')\n", + "dot_graph.add_node(chr_array_py_node)\n", + "\n", + "chr_array_node = pydot.Node('chr_array\\n(BCOLZ)')\n", + "chr_array_node.set_shape('box3d')\n", + "dot_graph.add_node(chr_array_node)\n", + "\n", + "iedge = pydot.Edge(clipped_reads_node, chr_array_py_node)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "iedge = pydot.Edge(clipped_read_pos_node, chr_array_py_node)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "iedge = pydot.Edge(split_reads_node, chr_array_py_node)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "iedge = pydot.Edge(clipped_read_distance_node, chr_array_py_node)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "iedge = pydot.Edge(coverage_node, chr_array_py_node)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "iedge = pydot.Edge(snv_node, chr_array_py_node)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "iedge = pydot.Edge(test_gem_node, chr_array_py_node)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "iedge = pydot.Edge(test_2bit_node, chr_array_py_node)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "iedge = pydot.Edge(chr_array_py_node, chr_array_node)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "label_sr_py_node = pydot.Node('label_windows.py')\n", + "label_sr_py_node.set_shape('box')\n", + "dot_graph.add_node(label_sr_py_node)\n", + "\n", + "iedge = pydot.Edge(test_bam_node, label_sr_py_node)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "iedge = pydot.Edge(split_reads_node, label_sr_py_node)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "iedge = pydot.Edge(test_truth_set_bedpe_node, label_sr_py_node)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "label_sr_node = pydot.Node('labels\\n(JSON.GZ)')\n", + "label_sr_node.set_shape('box3d')\n", + "dot_graph.add_node(label_sr_node)\n", + "\n", + "iedge = pydot.Edge(label_sr_py_node, label_sr_node)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "create_window_pairs_py_sr_node = pydot.Node('create_window_pairs.py')\n", + "create_window_pairs_py_sr_node.set_shape('box')\n", + "dot_graph.add_node(create_window_pairs_py_sr_node)\n", + "\n", + "add_win_channels = pydot.Node('add_win_channels.py')\n", + "add_win_channels.set_shape('box')\n", + "dot_graph.add_node(add_win_channels)\n", + "\n", + "train_py_sr_node = pydot.Node('train.py\\n')\n", + "train_py_sr_node.set_shape('box')\n", + "dot_graph.add_node(train_py_sr_node)\n", + "\n", + "predict_py_sr_node = pydot.Node('predict.py\\n')\n", + "predict_py_sr_node.set_shape('box')\n", + "dot_graph.add_node(predict_py_sr_node)\n", + "\n", + "iedge = pydot.Edge(test_bam_node, label_sr_py_node)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "iedge = pydot.Edge(test_truth_set_bedpe_node, label_sr_py_node)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "iedge = pydot.Edge(label_sr_node, create_window_pairs_py_sr_node)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "iedge = pydot.Edge(chr_array_node, create_window_pairs_py_sr_node)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "windows_sr_node = pydot.Node('windows\\n(NPZ)')\n", + "windows_sr_node.set_shape('box3d')\n", + "dot_graph.add_node(windows_sr_node)\n", + "\n", + "iedge = pydot.Edge(create_window_pairs_py_sr_node, windows_sr_node)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "windows_en_node = pydot.Node('windows_en\\n(NPZ)')\n", + "windows_en_node.set_shape('box3d')\n", + "dot_graph.add_node(windows_en_node)\n", + "\n", + "iedge = pydot.Edge(test_bam_node, add_win_channels)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "iedge = pydot.Edge(windows_sr_node, add_win_channels)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "iedge = pydot.Edge(add_win_channels, windows_en_node)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "windows_train_node = pydot.Node('Training set windows_en\\n(NPZ)')\n", + "windows_train_node.set_shape('box3d')\n", + "dot_graph.add_node(windows_train_node)\n", + "\n", + "windows_test_node = pydot.Node('Test set windows_en\\n(NPZ)')\n", + "windows_test_node.set_shape('box3d')\n", + "dot_graph.add_node(windows_test_node)\n", + "\n", + "model = pydot.Node('Model\\n(HDF5)')\n", + "model.set_shape('box3d')\n", + "dot_graph.add_node(model)\n", + "\n", + "iedge = pydot.Edge(windows_train_node, train_py_sr_node)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "iedge = pydot.Edge(train_py_sr_node, model)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "iedge = pydot.Edge(windows_test_node, predict_py_sr_node)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "iedge = pydot.Edge(model, predict_py_sr_node)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "predictions_sr_node = pydot.Node('SV calls\\n(BEDPE)')\n", + "predictions_sr_node.set_shape('box3d')\n", + "dot_graph.add_node(predictions_sr_node)\n", + "\n", + "merge_sv_calls = pydot.Node('merge_sv_calls.R')\n", + "merge_sv_calls.set_shape('box')\n", + "dot_graph.add_node(merge_sv_calls)\n", + "\n", + "bedpe_to_vcf = pydot.Node('bedpe_to_vcf.py')\n", + "bedpe_to_vcf.set_shape('box')\n", + "dot_graph.add_node(bedpe_to_vcf)\n", + "\n", + "predictions_vcf_node = pydot.Node('SV calls\\n(VCF)')\n", + "predictions_vcf_node.set_shape('box3d')\n", + "dot_graph.add_node(predictions_vcf_node)\n", + "\n", + "iedge = pydot.Edge(predict_py_sr_node, predictions_sr_node)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "iedge = pydot.Edge(predictions_sr_node, merge_sv_calls)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "iedge = pydot.Edge(merge_sv_calls, bedpe_to_vcf)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "iedge = pydot.Edge(bedpe_to_vcf, predictions_vcf_node)\n", + "iedge.set_label('')\n", + "dot_graph.add_edge(iedge)\n", + "\n", + "dot_graph.write_svg('cnn.svg')\n", + "# dot_graph.write_ps2('cnn.ps2')\n", + "SVG('cnn.svg')" ], - "text/plain": [ - 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id=\"edge40\">\n<title>bedpe_to_vcf.py->SV calls\\n(VCF)</title>\n<path d=\"M1272,-150.8446C1272,-142.529 1272,-132.4659 1272,-123.1317\" fill=\"none\" stroke=\"#000000\"/>\n<polygon fill=\"#000000\" points=\"1275.5001,-123.0347 1272,-113.0348 1268.5001,-123.0348 1275.5001,-123.0347\" stroke=\"#000000\"/>\n</g>\n</g>\n</svg>" + }, + "metadata": { + "tags": [] + }, + "execution_count": 11 + } ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" + }, + { + "cell_type": "code", + "metadata": { + "id": "GUrBkX0N37KK" + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] } - ], - "source": [ - "dot_graph = pydot.Dot(graph_type='digraph')\n", - "\n", - "chr_node = pydot.Node('by chromosome')\n", - "chr_node.set_shape('oval')\n", - "dot_graph.add_node(chr_node)\n", - "\n", - "test_bam_node = pydot.Node('Read alignment\\n(BAM)')\n", - "test_bam_node.set_shape('box3d')\n", - "dot_graph.add_node(test_bam_node)\n", - "\n", - "iedge = pydot.Edge(test_bam_node, chr_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "test_gem_node = pydot.Node('GEM mappability track\\n(BigWig)')\n", - "test_gem_node.set_shape('box3d')\n", - "dot_graph.add_node(test_gem_node)\n", - "\n", - "test_2bit_node = pydot.Node('Reference\\n(2BIT)')\n", - "test_2bit_node.set_shape('box3d')\n", - "dot_graph.add_node(test_2bit_node)\n", - "\n", - "test_svcallset_vcf_node = pydot.Node('SV caller\\ncallset\\n(VCF)')\n", - "test_svcallset_vcf_node.set_shape('box3d')\n", - "dot_graph.add_node(test_svcallset_vcf_node)\n", - "\n", - "test_svcallset_bedpe_node = pydot.Node('SV caller\\ncallset\\n(BEDPE)')\n", - "test_svcallset_bedpe_node.set_shape('box3d')\n", - "dot_graph.add_node(test_svcallset_bedpe_node)\n", - "\n", - "test_truthset_vcf_node = pydot.Node('Truth set\\n(VCF)')\n", - "test_truthset_vcf_node.set_shape('box3d')\n", - "dot_graph.add_node(test_truthset_vcf_node)\n", - "\n", - "test_truthset_bedpe_node = pydot.Node('Truth set\\n(BEDPE)')\n", - "test_truthset_bedpe_node.set_shape('box3d')\n", - "dot_graph.add_node(test_truthset_bedpe_node)\n", - "\n", - "iedge = pydot.Edge(test_svcallset_vcf_node, test_svcallset_bedpe_node)\n", - "iedge.set_label('vcf2bedpe.R')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "iedge = pydot.Edge(test_truthset_vcf_node, test_truthset_bedpe_node)\n", - "iedge.set_label('vcf2bedpe.R')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "clipped_reads_py_node = pydot.Node('clipped_reads.py')\n", - "clipped_reads_py_node.set_shape('box')\n", - "dot_graph.add_node(clipped_reads_py_node)\n", - "\n", - "clipped_reads_node = pydot.Node('clipped_reads\\n(JSON.GZ)')\n", - "clipped_reads_node.set_shape('box3d')\n", - "dot_graph.add_node(clipped_reads_node)\n", - "\n", - "iedge = pydot.Edge(test_bam_node, clipped_reads_py_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "iedge = pydot.Edge(clipped_reads_py_node, clipped_reads_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "clipped_read_pos_py_node = pydot.Node('clipped_read_pos.py')\n", - "clipped_read_pos_py_node.set_shape('box')\n", - "dot_graph.add_node(clipped_read_pos_py_node)\n", - "\n", - "clipped_read_pos_node = pydot.Node('clipped_read_pos\\n(JSON.GZ)')\n", - "clipped_read_pos_node.set_shape('box3d')\n", - "dot_graph.add_node(clipped_read_pos_node)\n", - "\n", - "iedge = pydot.Edge(test_bam_node, clipped_read_pos_py_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "iedge = pydot.Edge(clipped_read_pos_py_node, clipped_read_pos_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "split_reads_py_node = pydot.Node('split_reads.py')\n", - "split_reads_py_node.set_shape('box')\n", - "dot_graph.add_node(split_reads_py_node)\n", - "\n", - "split_reads_node = pydot.Node('split_reads\\n(JSON.GZ)')\n", - "split_reads_node.set_shape('box3d')\n", - "dot_graph.add_node(split_reads_node)\n", - "\n", - "iedge = pydot.Edge(test_bam_node, split_reads_py_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "iedge = pydot.Edge(split_reads_py_node, split_reads_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "clipped_read_distance_py_node = pydot.Node('clipped_read_distance.py')\n", - "clipped_read_distance_py_node.set_shape('box')\n", - "dot_graph.add_node(clipped_read_distance_py_node)\n", - "\n", - "clipped_read_distance_node = pydot.Node('chr_clipped_read_distance\\n(JSON.GZ)')\n", - "clipped_read_distance_node.set_shape('box3d')\n", - "dot_graph.add_node(clipped_read_distance_node)\n", - "\n", - "iedge = pydot.Edge(chr_node, clipped_read_distance_py_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "iedge = pydot.Edge(clipped_read_distance_py_node, clipped_read_distance_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "coverage_py_node = pydot.Node('coverage.py')\n", - "coverage_py_node.set_shape('box')\n", - "dot_graph.add_node(coverage_py_node)\n", - "\n", - "coverage_node = pydot.Node('chr_coverage\\n(NPY)')\n", - "coverage_node.set_shape('box3d')\n", - "dot_graph.add_node(coverage_node)\n", - "\n", - "iedge = pydot.Edge(chr_node, coverage_py_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "iedge = pydot.Edge(coverage_py_node, coverage_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "snv_py_node = pydot.Node('snv.py')\n", - "snv_py_node.set_shape('box')\n", - "dot_graph.add_node(snv_py_node)\n", - "\n", - "snv_node = pydot.Node('chr_snv\\n(NPY)')\n", - "snv_node.set_shape('box3d')\n", - "dot_graph.add_node(snv_node)\n", - "\n", - "iedge = pydot.Edge(chr_node, snv_py_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "iedge = pydot.Edge(snv_py_node, snv_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "chr_array_py_node = pydot.Node('chr_array.py')\n", - "chr_array_py_node.set_shape('box')\n", - "dot_graph.add_node(chr_array_py_node)\n", - "\n", - "chr_array_node = pydot.Node('chr_array\\n(BCOLZ)')\n", - "chr_array_node.set_shape('box3d')\n", - "dot_graph.add_node(chr_array_node)\n", - "\n", - "iedge = pydot.Edge(chr_node, chr_array_py_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "iedge = pydot.Edge(clipped_reads_node, chr_array_py_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "iedge = pydot.Edge(clipped_read_pos_node, chr_array_py_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "iedge = pydot.Edge(split_reads_node, chr_array_py_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "iedge = pydot.Edge(clipped_read_distance_node, chr_array_py_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "iedge = pydot.Edge(coverage_node, chr_array_py_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "iedge = pydot.Edge(snv_node, chr_array_py_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "iedge = pydot.Edge(test_gem_node, chr_array_py_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "iedge = pydot.Edge(test_2bit_node, chr_array_py_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "iedge = pydot.Edge(chr_array_py_node, chr_array_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "label_sr_py_node = pydot.Node('label_window_pairs_on_split_read_positions.py')\n", - "label_sr_py_node.set_shape('box')\n", - "dot_graph.add_node(label_sr_py_node)\n", - "\n", - "iedge = pydot.Edge(test_bam_node, label_sr_py_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "iedge = pydot.Edge(split_reads_node, label_sr_py_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "iedge = pydot.Edge(test_truthset_bedpe_node, label_sr_py_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "iedge = pydot.Edge(label_sr_py_node, label_sr_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "label_sr_node = pydot.Node('labels\\nsplit_reads\\n(JSON.GZ)')\n", - "label_sr_node.set_shape('box3d')\n", - "dot_graph.add_node(label_sr_node)\n", - "\n", - "label_sv_py_node = pydot.Node('label_window_pairs_on_svcallset.py')\n", - "label_sv_py_node.set_shape('box')\n", - "dot_graph.add_node(label_sv_py_node)\n", - "\n", - "create_window_pairs_py_sr_node = pydot.Node('create_window_pairs.py\\nsplit_reads')\n", - "create_window_pairs_py_sr_node.set_shape('box')\n", - "dot_graph.add_node(create_window_pairs_py_sr_node)\n", - "\n", - "create_window_pairs_py_sv_node = pydot.Node('create_window_pairs.py\\nSV_callset')\n", - "create_window_pairs_py_sv_node.set_shape('box')\n", - "dot_graph.add_node(create_window_pairs_py_sv_node)\n", - "\n", - "train_model_with_fit_py_sr_node = pydot.Node('train_model_with_fit.py\\ncross-validation\\nsplit_reads')\n", - "train_model_with_fit_py_sr_node.set_shape('box')\n", - "dot_graph.add_node(train_model_with_fit_py_sr_node)\n", - "\n", - "train_model_with_fit_py_sv_node = pydot.Node('train_model_with_fit.py\\ncross-validation\\nSV_callset')\n", - "train_model_with_fit_py_sv_node.set_shape('box')\n", - "dot_graph.add_node(train_model_with_fit_py_sv_node)\n", - "\n", - "label_sv_py_node = pydot.Node('label_window_pairs_on_svcallset.py')\n", - "label_sv_py_node.set_shape('box')\n", - "dot_graph.add_node(label_sv_py_node)\n", - "\n", - "label_sv_node = pydot.Node('labels\\nSV callset\\n(JSON.GZ)')\n", - "label_sv_node.set_shape('box3d')\n", - "dot_graph.add_node(label_sv_node)\n", - "\n", - "iedge = pydot.Edge(test_bam_node, label_sv_py_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "iedge = pydot.Edge(test_svcallset_bedpe_node, label_sv_py_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "iedge = pydot.Edge(test_truthset_bedpe_node, label_sv_py_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "iedge = pydot.Edge(label_sv_py_node, label_sv_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "iedge = pydot.Edge(test_bam_node, create_window_pairs_py_sr_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "iedge = pydot.Edge(test_bam_node, create_window_pairs_py_sv_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "iedge = pydot.Edge(label_sv_node, create_window_pairs_py_sv_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "iedge = pydot.Edge(label_sr_node, create_window_pairs_py_sr_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "iedge = pydot.Edge(chr_array_node, create_window_pairs_py_sr_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "iedge = pydot.Edge(chr_array_node, create_window_pairs_py_sv_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "windows_sr_node = pydot.Node('windows\\nsplit_reads\\n(JSON.GZ)')\n", - "windows_sr_node.set_shape('box3d')\n", - "dot_graph.add_node(windows_sr_node)\n", - "\n", - "windows_sv_node = pydot.Node('windows\\nSV_callset\\n(JSON.GZ)')\n", - "windows_sv_node.set_shape('box3d')\n", - "dot_graph.add_node(windows_sv_node)\n", - "\n", - "iedge = pydot.Edge(create_window_pairs_py_sv_node, windows_sv_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "iedge = pydot.Edge(create_window_pairs_py_sr_node, windows_sr_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "iedge = pydot.Edge(windows_sr_node, train_model_with_fit_py_sr_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "iedge = pydot.Edge(windows_sv_node, train_model_with_fit_py_sv_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "predictions_sr_node = pydot.Node('SV calls\\nsplit_reads\\n(BEDPE)')\n", - "predictions_sr_node.set_shape('box3d')\n", - "dot_graph.add_node(predictions_sr_node)\n", - "\n", - "predictions_sv_node = pydot.Node('SV calls\\nSV_callset\\n(BEDPE)')\n", - "predictions_sv_node.set_shape('box3d')\n", - "dot_graph.add_node(predictions_sv_node)\n", - "\n", - "iedge = pydot.Edge(train_model_with_fit_py_sr_node, predictions_sr_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "iedge = pydot.Edge(train_model_with_fit_py_sv_node, predictions_sv_node)\n", - "iedge.set_label('')\n", - "dot_graph.add_edge(iedge)\n", - "\n", - "dot_graph.write_svg('cnn.svg')\n", - "# dot_graph.write_ps2('cnn.ps2')\n", - "SVG('cnn.svg')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} + ] +} \ No newline at end of file diff --git a/run.sh b/run.sh index 7615add7..4a21dee5 100755 --- a/run.sh +++ b/run.sh @@ -3,36 +3,38 @@ set -xe # check input arg(s) -if [ $# -lt "3" ]; then - echo "Usage: $0 [SCHEDULER {local,gridengine,slurm}] [BAM file] [SEQID...]" +if [ $# -ne "3" ]; then + echo "Usage: $0 [SCHEDULER {local,gridengine,slurm}] [BAM file] [SEQID1,2,...]" exit 1 fi # set variables SCH=$1 # scheduler type -BAM=$(realpath -s "$2") -BASE_DIR=$(dirname "$BAM") -SAMPLE=$(basename "$BAM" .bam) -SEQ_IDS=(${@:3}) -SEQ_IDS_CSV=$(IFS=, ; echo "${SEQ_IDS[*]}") # stringify -SV_TYPES=(DEL) # INS INV DUP TRA) -SV_CALLS=(gridss) # manta delly lumpy) +BAM="$(realpath -s "$2")" +BASE_DIR="$(dirname "$BAM")" +SAMPLE="$(basename "$BAM" .bam)" +SEQ_IDS=$3 +SV_TYPES=(DEL) # INS INV DUP CTX) +SV_CALLS=(split_reads gridss) # manta delly lumpy) +CV_MODES=(kfold chrom) # cross validation modes +KMERS=19 +MAX_MISMATCH=2 KFOLD=2 # k-fold cross validation -WIN_SZ=200 # window size in bp -PREFIX="${BASE_DIR}/${SAMPLE}" -TWOBIT="${PREFIX}.2bit" -BIGWIG="${PREFIX}.bw" -BEDPE="${PREFIX}.bedpe" -BED="${PREFIX}.bed" -VCF="${PREFIX}.vcf" -STARTTIME=$(date +%s) +EPOCHS=1 # epochs +WIN_SZ=25 # window size in bp +PREFIX="$BASE_DIR/$SAMPLE" +FASTA="$PREFIX.fasta" +TWOBIT="$PREFIX.2bit" +BIGWIG="$PREFIX.bw" +BEDPE="$PREFIX.bedpe" +EXCL_LIST="$BASE_DIR/ENCFF001TDO.bed" +REF_REG="$BASE_DIR/reference_N_regions.bed" JOBS=() # array of job IDs JOBS_LOG=jobs.json # job accounting log RTIME=20 # runtime in minutes -STIME=1 # sleep X minutes -MY_ENV=sv-channels # conda env in gtcg/xenon-* docker images -#MAXMEM=48000 # mem in MB; use with xenon --max-memory - +SLTIME=1 # sleep X minutes +STIME=$(date +%s) +CONDA_ENV="sv-channels" # conda env in gtcg/xenon-* docker images # define functions submit () { # submit a job via Xenon CLI @@ -68,7 +70,7 @@ waiting () { # wait until all jobs are done for j in "${JOBS[@]}"; do while true; do [[ $(monitor $j | grep -v "WARN" | jq '.statuses | .[] | select(.done==true)') ]] && \ - break || sleep ${STIME}m + break || sleep ${SLTIME}m done done } @@ -76,46 +78,104 @@ waiting () { # wait until all jobs are done # activate conda env eval "$(conda shell.bash hook)" -conda activate $MY_ENV +conda activate $CONDA_ENV conda list +# extract N's from sequence into BED +p=seqkit +cmd="$p locate -i -P -r -p "N+" --bed \"$FASTA\" -o \"$REF_REG\"" +JOB_ID=$(submit "$cmd" "$p") +JOBS+=($JOB_ID) + +# convert input FASTA to 2bit +p=faToTwoBit +cmd="$p \"$FASTA\" \"$TWOBIT\"" +JOB_ID=$(submit "$cmd" "$p") +JOBS+=($JOB_ID) + +# compute genome mappability +p=genmap +cmd="./$p.sh \"$FASTA\" \"$BIGWIG\" $KMERS $MAX_MISMATCH" +JOB_ID=$(submit "$cmd" "$p") +JOBS+=($JOB_ID) + +waiting + # convert SV calls (i.e. truth set and sv-callers output) in VCF to BEDPE files cd scripts/R -cmd="./vcf2bedpe.R -i \"$VCF\" -o \"$BEDPE\"" -JOB_ID=$(submit "$cmd" vcf2bedpe) -JOBS+=($JOB_ID) +p=vcf2bedpe +for vcf in $(find "$BASE_DIR" -mindepth 3 -name "*.vcf"); do + prefix="$(basename "$vcf" .vcf)" + bedpe="$BASE_DIR/$prefix.bedpe" + cmd="./$p.R -i \"$vcf\" -o \"$bedpe\"" + JOB_ID=$(submit "$cmd" "$p-$prefix") + JOBS+=($JOB_ID) +done + +waiting # submit jobs to output "channel" files (*.json.gz and *.npy.gz) cd ../genome_wide p=clipped_reads -cmd="python $p.py -b \"$BAM\" -c \"${SEQ_IDS_CSV}\" -o $p.json.gz -p . -l $p.log" -JOB_ID=$(submit "$cmd" $p) +cmd="python $p.py \ + -b \"$BAM\" \ + -c \"$SEQ_IDS\" \ + -o $p.json.gz \ + -p . \ + -l $p.log" +JOB_ID=$(submit "$cmd" "$p") JOBS+=($JOB_ID) p=clipped_read_pos -cmd="python $p.py -b \"$BAM\" -c \"$SEQ_IDS_CSV\" -o $p.json.gz -p . -l $p.log" -JOB_ID=$(submit "$cmd" $p) +cmd="python $p.py \ + -b \"$BAM\" \ + -c \"$SEQ_IDS\" \ + -o $p.json.gz \ + -p . \ + -l $p.log" +JOB_ID=$(submit "$cmd" "$p") JOBS+=($JOB_ID) p=split_reads -cmd="python $p.py -b \"$BAM\" -c \"$SEQ_IDS_CSV\" -o $p.json.gz -ob $p.bedpe.gz \ - -p . -l $p.log" -JOB_ID=$(submit "$cmd" $p) +cmd="python $p.py \ + -b \"$BAM\" \ + -c \"$SEQ_IDS\" \ + -o $p.json.gz \ + -ob $p.bedpe.gz \ + -p . \ + -l $p.log" +JOB_ID=$(submit "$cmd" "$p") JOBS+=($JOB_ID) -for s in "${SEQ_IDS[@]}"; do # per chromosome +for s in $(echo "$SEQ_IDS" | tr ',' ' '); do # per chromosome p=clipped_read_distance - cmd="python $p.py -b \"$BAM\" -c $s -o $p.json.gz -p . -l $p.log" + cmd="python $p.py \ + -b \"$BAM\" \ + -c $s \ + -o $p.json.gz \ + -p . \ + -l $p.log" JOB_ID=$(submit "$cmd" "$p-$s") JOBS+=($JOB_ID) p=snv - cmd="python $p.py -b \"$BAM\" -c $s -t \"$TWOBIT\" -o $p.npy -p . -l $p.log" + cmd="python $p.py \ + -b \"$BAM\" \ + -c $s \ + -t \"$TWOBIT\" \ + -o $p.npy \ + -p . \ + -l $p.log" JOB_ID=$(submit "$cmd" "$p-$s") JOBS+=($JOB_ID) p=coverage - cmd="python $p.py -b \"$BAM\" -c $s -o $p.npy -p . -l $p.log" + cmd="python $p.py \ + -b \"$BAM\" \ + -c $s \ + -o $p.npy \ + -p . \ + -l $p.log" JOB_ID=$(submit "$cmd" "$p-$s") JOBS+=($JOB_ID) done @@ -123,10 +183,16 @@ done waiting # generate chromosome arrays from the channels as well as label window pairs -for s in "${SEQ_IDS[@]}"; do +for s in $(echo "$SEQ_IDS" | tr ',' ' '); do p=chr_array - cmd="python $p.py -b \"$BAM\" -c $s -t \"$TWOBIT\" -m \"$BIGWIG\" -o $p.npy \ - -p . -l $p.log" + cmd="python $p.py \ + -b \"$BAM\" \ + -c $s \ + -t \"$TWOBIT\" \ + -m \"$BIGWIG\" \ + -o $p.npy \ + -p . \ + -l $p.log" JOB_ID=$(submit "$cmd" "$p-$s") JOBS+=($JOB_ID) done @@ -134,13 +200,20 @@ done waiting # Create labels -for sv in "${SV_TYPES[@]}"; do - for c in "${SV_CALLS[@]}"; do +for c in "${SV_CALLS[@]}"; do + for sv in "${SV_TYPES[@]}"; do p=label_windows - cmd="python $p.py -b \"$BED\" -c \"$SEQ_IDS_CSV\" -w $WIN_SZ \ - -gt \"$BEDPE\" -s $sv -sv \"$BASE_DIR/$c\" -o labels.json.gz \ - -p . -l $p.log" - JOB_ID=$(submit "$cmd" "$p-$c") + cmd="python $p.py \ + -f \"$FASTA\" \ + -c \"$SEQ_IDS\" \ + -w $WIN_SZ \ + -gt \"$BEDPE\" \ + -s $sv \ + -sv \"$c\" \ + -o labels.json.gz \ + -p . \ + -l $p.log" + JOB_ID=$(submit "$cmd" "$p-$c-$sv") JOBS+=($JOB_ID) done done @@ -148,14 +221,19 @@ done waiting # Create windows -for sv in "${SV_TYPES[@]}"; do - for c in "${SV_CALLS[@]}"; do +for c in "${SV_CALLS[@]}"; do + for sv in "${SV_TYPES[@]}"; do p=create_window_pairs - out="labels/win$WIN_SZ/$sv/$c" + out="cnn/win$WIN_SZ/$c/windows/$sv" lb="$out/labels.json.gz" - cmd="python $p.py -b \"$BAM\" -c \"$SEQ_IDS_CSV\" -lb \"$lb\" -ca . \ - -w $WIN_SZ -p \"$out\" -l $p.log" - JOB_ID=$(submit "$cmd" $p) + cmd="python $p.py \ + -b \"$BAM\" \ + -c \"$SEQ_IDS\" \ + -lb \"$lb\" -ca . \ + -w $WIN_SZ \ + -p \"$out\" \ + -l $p.log" + JOB_ID=$(submit "$cmd" "$p-$c-$sv") JOBS+=($JOB_ID) done done @@ -163,47 +241,98 @@ done waiting # Add window channels -for sv in "${SV_TYPES[@]}"; do - for c in "${SV_CALLS[@]}"; do +for c in "${SV_CALLS[@]}"; do + for sv in "${SV_TYPES[@]}"; do p=add_win_channels - out="labels/win$WIN_SZ/$sv/$c" - prefix="$out/windows/windows" + out="cnn/win$WIN_SZ/$c/windows/$sv" + prefix="$out/windows" infile="$prefix.npz" outfile="${prefix}_en.npz" log="${prefix}_en.log" - cmd="python $p.py -b \"$BAM\" -w $WIN_SZ -i \"$infile\" -o \"$outfile\" \ + cmd="python $p.py \ + -b \"$BAM\" \ + -w $WIN_SZ \ + -i \"$infile\" \ + -o \"$outfile\" \ -l \"$log\"" - JOB_ID=$(submit "$cmd" "$p-$c") - JOBS+=($JOB_ID) - - waiting - - p=mv - cmd="mv -b --suffix=.bck \"$outfile\" \"$infile\"" - JOB_ID=$(submit "$cmd" "$p-$c") + JOB_ID=$(submit "$cmd" "$p-$c-$sv") JOBS+=($JOB_ID) done done waiting +exit 0 # Train and test model for sv in "${SV_TYPES[@]}"; do for c in "${SV_CALLS[@]}"; do - p=train_model_with_fit - out="labels/win$WIN_SZ/$sv/$c" - cmd="python $p.py --training_sample_name \"$SAMPLE\" \ - --training_sample_folder . --test_sample_name \"$SAMPLE\" \ - --test_sample_folder . -k $KFOLD -p \"$out\" -s $sv -l $p.log" - JOB_ID=$(submit "$cmd" "$p-$c") + for cv in "${CV_MODES[@]}"; do + p=train + out_dir="cnn/win$WIN_SZ/$c" + train_dir="$out_dir/windows/$sv/windows_en.npz" + cmd="python $p.py \ + --training_sample_name \"$SAMPLE\" \ + --training_windows \"$train_dir\" \ + --test_sample_name \"$SAMPLE\" \ + --test_windows \"$train_dir\" \ + -k $KFOLD \ + -e $EPOCHS \ + -p \"$out_dir\" \ + -s $sv \ + -cv $cv \ + -l $p.log" + JOB_ID=$(submit "$cmd" "$p-$sv-$c") + JOBS+=($JOB_ID) + done + done +done + +waiting + +# merge SV calls +cd ../R +p=merge_sv_calls +for c in "${SV_CALLS[@]}"; do + for m in "${CV_MODES[@]}"; do + win_dir="../genome_wide/cnn/win$WIN_SZ" + sv_dir="$win_dir/$c/$m" + bedpe="$win_dir/sv-channels.$c.$m.$SAMPLE" + cmd="$p.R \ + -i \"$sv_dir\" \ + -f \"$EXCL_LIST\" \ + -n \"$REF_REG\" \ + -m $c \ + -o \"$bedpe\"" + JOB_ID=$(submit "$cmd" "$p-$c-$m") + JOBS+=($JOB_ID) + done +done + +waiting + +# convert BEDPE to VCF +cd ../utils +for c in "${SV_CALLS[@]}"; do + for m in "${CV_MODES[@]}"; do + p=bedpe_to_vcf + win_dir="../genome_wide/cnn/win$WIN_SZ" + prefix="$win_dir/sv-channels.$c.$m.$SAMPLE" + bedpe="$prefix.bedpe" + vcf="$prefix.vcf" + cmd="python $p.py \ + -i \"$bedpe\" \ + -b \"$TWOBIT\" \ + -o \"$vcf\" \ + -s \"$SAMPLE\"" + JOB_ID=$(submit "$cmd" "$p-$c-$m") JOBS+=($JOB_ID) done done waiting -ENDTIME=$(date +%s) -echo "Processing ${#JOBS[@]} jobs took $((ENDTIME - STARTTIME)) sec to complete." +ETIME=$(date +%s) +echo "Processing ${#JOBS[@]} jobs took $((ETIME - STIME)) sec to complete." # collect job accounting info for j in "${JOBS[@]}"; do diff --git a/scripts/R/aux_functions.R b/scripts/R/aux_functions.R new file mode 100644 index 00000000..0823bd46 --- /dev/null +++ b/scripts/R/aux_functions.R @@ -0,0 +1,149 @@ +suppressPackageStartupMessages(require(tools)) +suppressPackageStartupMessages(require(StructuralVariantAnnotation)) +suppressPackageStartupMessages(require(dplyr)) +suppressPackageStartupMessages(require(rtracklayer)) +suppressPackageStartupMessages(require(argparser)) + +# this option avoid the scientific notation for numbers (such as 1e2) +options(scipen = 999) + +# The palette with black: +cbbPalette <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7") + +make_percent <- function(x) { + signif(x * 100, digits = 4) +} + +getRegionsFromTruthSet <- +function(ts.file) { + # handle BED(PE) and VCF files + if (file_ext(ts.file) %in% c('vcf', 'gz')) { + vcf <- VariantAnnotation::readVcf(ts.file) + # fix: SVLEN type: CharacterList->IntegerList + info(vcf)$SVLEN <- IntegerList(info(vcf)$SVLEN) + return(StructuralVariantAnnotation::breakpointRanges(vcf)) + } + + if (file_ext(ts.file) == 'bed') { + bedpe.file <- paste0(file_path_sans_ext(ts.file), '.bedpe') + cmd <- + paste( + "awk 'BEGIN {a=0; OFS=\"\t\"} NR>1 {print $1,$2,$2+1,$1,$3,\ + $3+1,\"DEL_\" a,-1,\"+\",\"+\",\"DEL\"; a+=1}'", + ts.file, + '>', + bedpe.file + ) + system(cmd) + ts.file <- bedpe.file + } + return(pairs2breakpointgr(rtracklayer::import(ts.file))) +} + +infer_svtype <- function(gr) +{ + gr$svtype <- + ifelse( + seqnames(gr) != seqnames(partner(gr)), + "CTX", + ifelse( + gr$insLen >= abs(gr$svLen) * 0.7, + "INS", + ifelse( + strand(gr) == strand(partner(gr)), + "INV", + ifelse(xor( + start(gr) < start(partner(gr)), strand(gr) == "-" + ), "DEL", + "DUP") + ) + ) + ) + return(gr) +} + +load_bed <- function(bed_file) +{ + bed_regions <- rtracklayer::import(bed_file) + # set NCBI seqlevels + seqlevelsStyle(bed_regions) <- "NCBI" + return(bed_regions) +} + +load_bedpe <- function(bedpe_file, encode.blacklist, n.regions) +{ + bedpe_gr <- pairs2breakpointgr(rtracklayer::import(bedpe_file)) + + bedpe_gr <- filter_regions('ENCODE blacklist', bedpe_gr, load_bed(encode.blacklist), mode = 'remove') + bedpe_gr <- filter_regions('regions with Ns', bedpe_gr, load_bed(n.regions), mode = 'remove') + + return(bedpe_gr) +} + +load_vcf <- function(vcf_file, svtype, caller, encode.blacklist, n.regions) +{ + # Load VCF file + vcf_gr <- + VariantAnnotation::readVcf(vcf_file) + + # Keep only SVs that passed the filtering (PASS or .) + vcf_gr <- vcf_gr[rowRanges(vcf_gr)$FILTER %in% c("PASS", ".")] + + if (caller == 'lumpy') + { + # Read evidence support as a proxy for QUAL + fixed(vcf_gr)$QUAL <- unlist(info(vcf_gr)$SU) + + } else if (caller == 'delly') + { + # Split-read support plus Paired-end read support as a proxy for QUAL + sr_support <- info(vcf_gr)$SR + sr_support[is.na(vcf_gr)] <- 0 + fixed(vcf_gr)$QUAL <- sr_support + info(vcf_gr)$PE + } + + vcf_gr <- StructuralVariantAnnotation::breakpointRanges(vcf_gr) + vcf_gr <- infer_svtype(vcf_gr) + + # Select only one SV type + vcf_gr <- vcf_gr[which(vcf_gr$svtype == svtype)] + + message(paste('Loading', length(vcf_gr), svtype, 'calls for', caller, sep=" ")) + + # Select SVs >= 50 bp + if (! svtype %in% c('CTX', 'INS')) + { + vcf_gr <- vcf_gr[abs(vcf_gr$svLen) >= 50] + } + + # Filter regions + vcf_gr <- filter_regions('ENCODE blacklist', vcf_gr, load_bed(encode.blacklist), mode = 'remove') + vcf_gr <- filter_regions('regions with Ns', vcf_gr, load_bed(n.regions), mode = 'remove') + + return(vcf_gr) +} + +filter_regions <- +function(filter.name, regions_to_filter, ref_regions, mode = 'remove') +{ + print(length(regions_to_filter)) + if (mode == 'keep') + { + result <- + regions_to_filter[overlapsAny(regions_to_filter, ref_regions) & + overlapsAny(partner(regions_to_filter), ref_regions),] + } else if (mode == 'remove') { + result <- regions_to_filter[! ( + overlapsAny(regions_to_filter, ref_regions) | + overlapsAny(partner(regions_to_filter), ref_regions) + ),] + } + message(paste(length(result), 'calls after filtering for', filter.name, sep=" ")) + return(result) +} + +count_sv_lines <- function(filename){ + length(grep('^#', readLines(filename), perl = TRUE, invert = TRUE)) > 0 +} + +############ \ No newline at end of file diff --git a/scripts/R/benchmark_functions.R b/scripts/R/benchmark_functions.R deleted file mode 100644 index 12bf30bf..00000000 --- a/scripts/R/benchmark_functions.R +++ /dev/null @@ -1,125 +0,0 @@ - -suppressPackageStartupMessages(require(StructuralVariantAnnotation)) -suppressPackageStartupMessages(require(dplyr)) -suppressPackageStartupMessages(require(ggplot2)) - -# SV type inference -infer_svtype <- function(gr) -{ - gr$svtype <- - ifelse( - seqnames(gr) != seqnames(partner(gr)), - "TRA", # Using TRA instead of ITX or BP - ifelse( - gr$insLen >= abs(gr$svLen) * 0.7, - "INS", - ifelse( - strand(gr) == strand(partner(gr)), - "INV", - ifelse(xor( - start(gr) < start(partner(gr)), strand(gr) == "-" - ), "DEL", - "DUP") - ) - ) - ) - return(gr) -} - -# load a BED file and returns a GRanges object -load_bed <- function(bed_file) -{ - bed_regions <- rtracklayer::import(bed_file) - # set NCBI seqlevels - seqlevelsStyle(bed_regions) <- "NCBI" - return(bed_regions) -} - -# load a BEDPE file and returns a Pairs object -load_bedpe <- function(bedpe_file, filter_regions) -{ - bedpe_gr <- pairs2breakpointgr(rtracklayer::import(bedpe_file)) - bedpe_gr <- filter_regions(bedpe_gr, load_bed(filter_regions), mode = 'remove') - return(bedpe_gr) -} - -# Filter regions using a BED file either for inclusion (keep) or exclusion (remove) -filter_regions <- function(regions_to_filter, ref_regions, mode='remove') -{ - print(length(regions_to_filter)) - if (mode == 'keep') - { - result <- regions_to_filter[overlapsAny(regions_to_filter, ref_regions) & - overlapsAny(partner(regions_to_filter), ref_regions),] - } else if (mode == 'remove') { - result <- regions_to_filter[! ( - overlapsAny(regions_to_filter, ref_regions) | - overlapsAny(partner(regions_to_filter), ref_regions) - ),] - } - print(length(result)) - return(result) -} - -# load a VCF file and returns a Pairs object -load_vcf <- function(vcf_file, svtype, caller, filter_regions) -{ - - # Load VCF file - vcf_gr <- - VariantAnnotation::readVcf(vcf_file) - - # set NCBI seqlevels - seqlevelsStyle(vcf_gr) <- 'NCBI' - - if(caller=='survivor') - { - # SURVIVOR simSV assigns LowQual to all artificial SVs - vcf_gr <- vcf_gr[rowRanges(vcf_gr)$FILTER%in%c("LowQual")] - - # handle SURVIVOR simSV SVTYPE <TRA> as TIGRA <CTX> - if("TRA" %in% info(vcf_gr)$SVTYPE & !("CT" %in% names(info(vcf_gr)))) - { - info(vcf_gr)$SVTYPE[info(vcf_gr)$SVTYPE=="TRA"] <- "CTX" - } - - if(svtype == 'INS') - { - info(vcf_gr)$END <- end(ranges(rowRanges(vcf_gr))) - } - - }else{ - # Keep only SVs that passed the filtering (PASS or .) - vcf_gr <- vcf_gr[rowRanges(vcf_gr)$FILTER%in%c("PASS",".")] - } - - if (caller == 'lumpy') - { - # Read evidence support as a proxy for QUAL - support <- unlist(info(vcf_gr)$SU) - fixed(vcf_gr)$QUAL <- support - } else if (caller == 'delly') - { - # Split-read support plus Paired-end read support as a proxy for QUAL - sr_support <- info(vcf_gr)$SR - sr_support[is.na(vcf_gr)] <- 0 - fixed(vcf_gr)$QUAL <- - sr_support + info(vcf_gr)$PE - } - - vcf_gr <- breakpointRanges(vcf_gr) - vcf_gr <- infer_svtype(vcf_gr) - - # Select only one SV type - vcf_gr <- vcf_gr[which(vcf_gr$svtype == svtype)] - - # Select SVs >= 50 bp - if(!svtype %in% c('TRA','INS')) - { - vcf_gr <- vcf_gr[abs(vcf_gr$svLen) >= 50] - } - - #Filter regions - vcf_gr <- filter_regions(vcf_gr, load_bed(filter_regions), mode='remove') - return(vcf_gr) -} \ No newline at end of file diff --git a/scripts/R/generate_figure2.R b/scripts/R/generate_figure2.R new file mode 100644 index 00000000..8011d26a --- /dev/null +++ b/scripts/R/generate_figure2.R @@ -0,0 +1,426 @@ +# remove everything in the environment +rm(list = ls()) + +setwd('~/Documents/Local_GitHub/sv-channels/scripts/R') + +source('./aux_functions.R') + +DEBUG <- FALSE + +default_param <- 'coverage' +default_param_val <- 60 +default_sample <- 'hmz-sv' +default_svtype <- 'INV' + +get_default_param_val <- function(parameters.vec, param) { + if (DEBUG) { + return(c(default_param_val)) + } else{ + return(parameters.vec[[param]]) + } +} + +input.dir <- + '~/Documents/Projects/GTCG/sv-channels/manuscript/Figures/F2/simulated-data-results/' +encode.list <- '../../data/ENCFF001TDO.bed' +n.regions <- '../../data/reference_N_regions.bed' +default.mode <- 'split_reads' + + +# create a parser and add command-line arguments +p <- + arg_parser( + "Generate figure with the performance (F1-score) of sv-channels + versus [GRIDSS, Manta, Lumpy, DELLY] for the SV type [DEL, INS, INV, DUP, BND] using SVs simulated with sv-gen" + ) + +p <- add_argument(p, + "-inputpath", + help = "path to the results of the simulated data", + type = "character", + default = input.dir) +p <- add_argument( + p, + "-outputpath", + help = "path to the output folder", + type = "character", + default = file.path(input.dir, '../plots') +) +p <- add_argument(p, + "-encodelist", + help = "path to the ENCODE blacklist", + type = "character", + default = encode.list) +p <- add_argument(p, + "-nregions", + help = "path to the ENCODE blacklist", + type = "character", + default = n.regions) +p <- add_argument(p, + "-mode", + help = "sv-channels mode: [split_reads, gridss, manta, delly, lumpy]", + type = "character", + default = default.mode) + +argv <- parse_args(p) + +parent_dir <- argv$inputpath +out_dir <- argv$outputpath +encode.blacklist <- argv$encodelist +regions.with.Ns <- argv$nregions +mode <- argv$mode + +# TODO: refactor to load from JSON + +# list of parameters +parameters <- list() +parameters[['cov']] <- c(5, 10, 15, 30, 45, 60, 75, 90) +parameters[['isize']] <- c(200, 250, 300, 400, 500, 600) +parameters[['rlen']] <- c(36, 50, 75, 100, 150, 250) + +# abbreviations +abbrv <- c('cov', 'isize', 'rlen') +names(abbrv) <- c('coverage', 'insert-size', 'read-length') + +if (DEBUG) { + params.list <- c(default_param) + samples.list <- c(default_sample) + svtype.list <- c(default_svtype) +} else{ + params.list <- names(abbrv) + samples.list <- c('hmz-sv', 'htz-sv') + svtype.list <- c('DEL', 'INS', 'INV', 'DUP', 'CTX') +} + +# Loop over three parameter vectors: coverage, insert size and read length +for (param in params.list) +{ + # Loop over the parameter values + for (param_val in get_default_param_val(parameters, abbrv[param])) + { + fld_val <- paste(abbrv[param], param_val, sep = "") + print(fld_val) + for (sample.name in samples.list) + { + print(sample.name) + + files.dir <- + file.path(parent_dir, param, fld_val, sample.name) + print(files.dir) + + truth_set_file <- + file.path(files.dir, paste(sample.name, '.proper.vcf', sep = '')) + + # SV callsets + callsets <- list() + callsets[['GRIDSS']] <- file.path(files.dir, 'gridss.vcf') + callsets[['Manta']] <- file.path(files.dir, 'manta.vcf') + callsets[['Lumpy']] <- file.path(files.dir, 'lumpy.vcf') + callsets[['DELLY']] <- file.path(files.dir, 'delly.vcf') + #callsets[['ensemble']] <- file.path(files.dir, 'all.vcf') + + for (svtype in svtype.list) + { + truth_set_svtype <- + load_vcf(truth_set_file, + svtype, + sample.name, + encode.blacklist, + regions.with.Ns) + + sv_regions <- list() + for (caller in c('GRIDSS', 'Manta', 'Lumpy', 'DELLY')) + { + if (file.exists(callsets[[caller]]) & + count_sv_lines(callsets[[caller]])) + { + sv_regions[[caller]] <- + load_vcf(callsets[[caller]], + svtype, + tolower(caller), + encode.blacklist, + regions.with.Ns) + } + } + + callsets[['sv-channels']] <- + file.path(files.dir, + 'sv-channels.split_reads.vcf') + if (file.exists(callsets[['sv-channels']])) + { + sv_regions[['sv-channels']] <- + load_vcf(callsets[['sv-channels']], + svtype, + 'sv-channels', + encode.blacklist, + regions.with.Ns) + } + + # TODO: use seqs.bed as input file + # chr.lengths <- c("10" = 135534747, "12" = 133851895) + # + # seqlengths(truth_set_svtype) <- + # chr.lengths[names(seqlengths(truth_set_svtype))] + # for (c in names(sv_regions)) + # { + # seqlengths(sv_regions[[c]]) <- + # chr.lengths[names(seqlengths(sv_regions[[c]]))] + # } + # + # for (n in names(sv_regions)) { + # message(paste(n, length(sv_regions[[n]]))) + # } + + # exclude callsets with zero calls + sv_regions <- + sv_regions[sapply(sv_regions, length) != 0] + + # add caller name + for (c in names(sv_regions)) + { + sv_regions[[c]]$caller <- c + } + + # collapse breakpoints by merging + for (c in names(sv_regions)) + { + # insertions are a special case + if (svtype == 'INS') { + sv_regions[[c]]$truth_matches <- + countBreakpointOverlaps( + sv_regions[[c]], + truth_set_svtype, + # using a smaller margin for insertions + maxgap = 5, + # sizemargin cannot be used for insertions + #sizemargin = 0.25, + ignore.strand = FALSE, + #restrictMarginToSizeMultiple = 0.5, + # countOnlyBest cannot be used for insertions + countOnlyBest = TRUE + ) + + } else { + sv_regions[[c]]$truth_matches <- + countBreakpointOverlaps( + sv_regions[[c]], + truth_set_svtype, + # read pair based callers make imprecise calls. + # A margin around the call position is required when matching with the truth set + maxgap = 200, + # Since we added a maxgap, we also need to restrict the mismatch between the + # size of the events. We don't want to match a 100bp deletion with a + # 5bp duplication. This will happen if we have a 100bp margin but don't also + # require an approximate size match as well + sizemargin = 0.25, + ignore.strand = FALSE, + # We also don't want to match a 20bp deletion with a 20bp deletion 80bp away + # by restricting the margin based on the size of the event, we can make sure + # that simple events actually do overlap + restrictMarginToSizeMultiple = 0.5, + # Some callers make duplicate calls and will sometimes report a variant multiple + # times with slightly different bounds. countOnlyBest prevents these being + # double-counted as multiple true positives. + countOnlyBest = TRUE + ) + } + } + + sv_regions_unlisted <- + unlist(GRangesList(sv_regions)) + + if (length(sv_regions_unlisted) > 0) + { + main.title <- + paste(param, param_val, sample.name, mode, svtype, sep = ' ') + print(main.title) + + p <- ggplot( + as.data.frame(sv_regions_unlisted) %>% + dplyr::select(QUAL, caller, truth_matches) %>% + dplyr::group_by(caller, QUAL) %>% + dplyr::summarise( + calls = n(), + tp = sum(truth_matches > 0) + ) %>% + dplyr::group_by(caller) %>% + dplyr::arrange(dplyr::desc(QUAL)) %>% + dplyr::mutate( + cum_tp = cumsum(tp), + cum_n = cumsum(calls), + cum_fp = cum_n - cum_tp, + precision = cum_tp / cum_n, + recall = cum_tp / length(truth_set_svtype) + ) + ) + + aes(x = recall, + y = precision, + colour = caller) + + geom_point() + + geom_line() + + scale_y_continuous(labels = scales::percent) + + scale_x_continuous( + labels = scales::percent, + sec.axis = sec_axis(~ (.) * length(truth_set_svtype), name = "true positives") + ) + + labs(title = main.title) + + output_path <- + file.path(out_dir, param, fld_val, sample.name, svtype, mode) + dir.create(output_path, recursive = TRUE) + filename <- + file.path(output_path, 'precision_recall_plot.png') + print(filename) + ggsave(file = filename) + + res.df <- as.data.frame(sv_regions_unlisted) %>% + dplyr::select(caller, truth_matches) %>% + dplyr::group_by(caller) %>% + dplyr::summarise(calls = n(), + TP = sum(truth_matches > 0)) %>% + dplyr::group_by(caller) %>% + dplyr::mutate( + FP = calls - TP, + precision = signif(TP / calls, digits = 4), + recall = signif(TP / length(truth_set_svtype), digits = 4) + ) + res.df$F1_score = with(res.df, 2 * (precision * recall) / (precision + recall)) + res.df$precision <- + make_percent(res.df$precision) + res.df$recall <- make_percent(res.df$recall) + res.df$F1_score <- make_percent(res.df$F1_score) + + filename <- + file.path(output_path, 'precision_recall_plot.csv') + print(filename) + write.csv( + file = filename, + res.df, + quote = FALSE, + row.names = FALSE + ) + } + } + } + } +} + +############ + + +#Summarize results + +wide <- data.frame() + +for (param in params.list) +{ + print(param) + for (param_val in get_default_param_val(parameters, abbrv[param])) + { + fld_val <- paste(abbrv[param], param_val, sep = "") + print(fld_val) + for (sample.name in samples.list) + { + print(sample.name) + + for (svtype in svtype.list) + { + files.dir <- + file.path(parent_dir, + '../plots', + param, + fld_val, + sample.name, + svtype, + mode) + print(files.dir) + + filename <- 'precision_recall_plot.csv' + if (file.exists(file.path(files.dir, filename))) + { + df <- read.csv(file.path(files.dir, filename)) + #print(df) + df$caller <- as.vector(df$caller) + + wide <- + rbind( + wide, + cbind( + df, + param = param, + param_val = param_val, + sample.name = sample.name, + svtype = svtype, + mode = mode + ) + ) + } + } + } + } +} +# wide + + +my_labeller <- function(variable, value) { + return(paste(value, ' (N=', table(truth_set_bedpe$sourceId)[value], ')', sep = + '')) +} + +for (param in names(abbrv)) +{ + print(param) + for (sample.name in c('hmz-sv', 'htz-sv')) + { + print(sample.name) + + wide_param <- wide[wide$param == param & + wide$sample.name == sample.name, ] + plot <- + ggplot(wide_param, aes(caller, F1_score)) + + geom_col(aes(fill = caller)) + + facet_grid(svtype ~ param_val) + + theme( + text = element_text(size = 30), + axis.text.y = element_text(size = 15), + axis.title.x = element_blank(), + axis.text.x = element_blank(), + axis.ticks.x = element_blank() + ) + + scale_fill_manual(values = cbbPalette) + plot <- + plot + ggtitle(paste(sample.name, ':', param, + #"TP:green", + #"FP:red", + sep = '')) # + + # geom_text( + # aes(label = FP), + # position = position_dodge(width = 0.9), + # vjust = -0.25, + # color = 'darkred', + # size = 2 + # ) + + # geom_text( + # aes(label = TP), + # position = position_dodge(width = 0.9), + # vjust = -1.50, + # color = 'darkgreen', + # size = 2 + # ) + #+ theme( + # panel.grid.major.y = element_line(colour = "grey", linetype="solid") + # ) + + ggsave( + plot, + file = file.path( + out_dir, + paste(param, sample.name, '_F1_score.png', sep = '_') + ), + dpi = 600, + h = 7, + w = 12 + ) + } +} + diff --git a/scripts/R/generate_figure3.R b/scripts/R/generate_figure3.R new file mode 100644 index 00000000..019fcf70 --- /dev/null +++ b/scripts/R/generate_figure3.R @@ -0,0 +1,197 @@ +source('/Users/lsantuari/Documents/Local_GitHub/sv-channels/scripts/R/summarize_results_fun.R') + +parent_dir <- + '/Users/lsantuari/Documents/Projects/GTCG/paper_data/real-data-results_noCR2SR_enh/' +out_dir <- file.path(parent_dir, 'results') + +regions_for_filtering <- + '/Users/lsantuari/Documents/Local_GitHub/sv-channels/data/ENCFF001TDO.bed' +N_regions_for_filtering <- + '/Users/lsantuari/Documents/Local_GitHub/sv-channels/data/Ns.bed' + +modes <- c('split_reads', 'gridss', 'manta', 'lumpy', 'delly') +samples.vec <- c('NA12878', 'NA24385', 'CHM1_CHM13') +svtype <-'DEL' + +truth_set_list <- c( + '~/Documents/Processed/SPIDER/notebook-data/NA12878/in/Personalis_1000_Genomes_deduplicated_deletions.bedpe', + '~/Documents/Processed/SPIDER/notebook-data/NA24385/in/nstd167.GRCh37.variant_call.vcf', + '~/Documents/Processed/SPIDER/notebook-data/CHM1_CHM13/in/nstd137.GRCh37.variant_call.vcf' +) +names(truth_set_list) <- samples.vec + +for (sample.name in samples.vec) +{ + sample.name <- 'NA24385' + print(sample.name) + + if(sample.name %in% c('NA24385', 'CHM1_CHM13')) + { + truth_set <- load_vcf(truth_set_list[[sample.name]], svtype, sample.name, regions_for_filtering, N_regions_for_filtering) + }else if (sample.name == 'NA12878') + { + truth_set <- load_bedpe(truth_set_list[[sample.name]], regions_for_filtering, N_regions_for_filtering) + } + + files.dir <- + file.path(parent_dir, sample.name) + print(files.dir) + + # SV callsets + callsets <- list() + callsets[['GRIDSS']] <- file.path(files.dir, 'gridss.vcf') + callsets[['Manta']] <- file.path(files.dir, 'manta.vcf') + callsets[['Lumpy']] <- file.path(files.dir, 'lumpy.vcf') + callsets[['DELLY']] <- file.path(files.dir, 'delly.vcf') + #callsets[['ensemble_set']] <- file.path(files.dir, 'all.vcf') + + sv_regions <- list() + sv_regions[['GRIDSS']] <- + load_vcf(callsets[['GRIDSS']], svtype, 'gridss', regions_for_filtering, N_regions_for_filtering) + sv_regions[['Manta']] <- + load_vcf(callsets[['Manta']], svtype, 'manta', regions_for_filtering, N_regions_for_filtering) + sv_regions[['Lumpy']] <- + load_vcf(callsets[['Lumpy']], svtype, 'lumpy', regions_for_filtering, N_regions_for_filtering) + sv_regions[['DELLY']] <- + load_vcf(callsets[['DELLY']], svtype, 'delly', regions_for_filtering, N_regions_for_filtering) + #sv_regions[['ensemble_set']] <- + # load_vcf(callsets[['ensemble_set']], svtype, 'ensemble_set', regions_for_filtering, N_regions_for_filtering) + + for (mode in modes) + { + # mode <- 'split_reads' + files.dir <- + file.path(parent_dir, sample.name) + print(files.dir) + mode.name <- paste('sv-channels',mode,sep=':') + callsets[[mode.name]] <- + file.path(files.dir, + 'sv-channels', mode, + paste('win50', '_', svtype, '.bedpe', sep = '')) + sv_regions[[mode.name]] <- + load_bedpe(callsets[[mode.name]], regions_for_filtering, N_regions_for_filtering) + } + + for (n in names(sv_regions)) { + message(paste(n, length(sv_regions[[n]]))) + } + + # exclude callsets with zero calls + sv_regions <- sv_regions[sapply(sv_regions, length) != 0] + + for (c in names(sv_regions)) + { + sv_regions[[c]]$caller <- c + } + + for (c in names(sv_regions)) + { + if (svtype == 'INS') { + sv_regions[[c]]$truth_matches <- + countBreakpointOverlaps( + sv_regions[[c]], + truth_set, + # using a smaller margin for insertions, insertion location should be precise + maxgap = 5, + # sizemargin cannot be used for insertions + # sizemargin = 0.25, + ignore.strand = TRUE, + restrictMarginToSizeMultiple = 0.5 + # countOnlyBest cannot be used for insertions + # countOnlyBest = TRUE + ) + + } else{ + sv_regions[[c]]$truth_matches <- + countBreakpointOverlaps( + sv_regions[[c]], + truth_set, + # read pair based callers make imprecise calls. + # A margin around the call position is required when matching with the truth set + maxgap = 200, + # Since we added a maxgap, we also need to restrict the mismatch between the + # size of the events. We don't want to match a 100bp deletion with a + # 5bp duplication. This will happen if we have a 100bp margin but don't also + # require an approximate size match as well + sizemargin = 0.25, + ignore.strand = TRUE, + # We also don't want to match a 20bp deletion with a 20bp deletion 80bp away + # by restricting the margin based on the size of the event, we can make sure + # that simple events actually do overlap + restrictMarginToSizeMultiple = 0.5, + # Some callers make duplicate calls and will sometimes report a variant multiple + # times with slightly different bounds. countOnlyBest prevents these being + # double-counted as multiple true positives. + countOnlyBest = TRUE + ) + } + } + + sv_regions_unlisted <- unlist(GRangesList(sv_regions)) + + main.title <- + paste(sample.name, svtype, 'CV10', sep = ' ') + print(main.title) + + ggplot( + as.data.frame(sv_regions_unlisted) %>% + dplyr::select(QUAL, caller, truth_matches) %>% + dplyr::group_by(caller, QUAL) %>% + dplyr::summarise(calls = n(), + tp = sum(truth_matches > 0)) %>% + dplyr::group_by(caller) %>% + dplyr::arrange(dplyr::desc(QUAL)) %>% + dplyr::mutate( + cum_tp = cumsum(tp), + cum_n = cumsum(calls), + cum_fp = cum_n - cum_tp, + precision = cum_tp / cum_n, + recall = cum_tp / length(truth_set) + ) + ) + + aes(x = recall, + y = precision, + colour = caller) + + geom_point() + + geom_line() + + theme(text = element_text(size=30)) + + scale_y_continuous(labels = scales::percent) + + scale_x_continuous( + labels = scales::percent, + sec.axis = sec_axis(~ (.) * length(truth_set), name = "true positives") + ) + + labs(title = main.title) + scale_fill_manual(values=cbbPalette) + + output_path <- + file.path(out_dir, sample.name, svtype) + dir.create(output_path, recursive = TRUE) + filename <- + file.path(output_path, 'precision_recall_plot.png') + print(filename) + ggsave(file = filename, width = 10, height = 5) + + res.df <- as.data.frame(sv_regions_unlisted) %>% + dplyr::select(caller, truth_matches) %>% + dplyr::group_by(caller) %>% + dplyr::summarise(calls = n(), + TP = sum(truth_matches > 0)) %>% + dplyr::group_by(caller) %>% + dplyr::mutate( + FP = calls - TP, + precision = signif(TP / calls, digits = 4), + recall = signif(TP / length(truth_set), digits = 4) + ) + res.df$F1_score = with(res.df, 2 * (precision * recall) / (precision + recall)) + res.df$precision <- make_percent(res.df$precision) + res.df$recall <- make_percent(res.df$recall) + res.df$F1_score <- make_percent(res.df$F1_score) + + filename <- + file.path(output_path, 'precision_recall_plot.csv') + print(filename) + write.csv(file = filename, + res.df, + quote = FALSE, + row.names = FALSE) + +} diff --git a/scripts/R/merge_sv_calls.R b/scripts/R/merge_sv_calls.R new file mode 100644 index 00000000..6e1e76b9 --- /dev/null +++ b/scripts/R/merge_sv_calls.R @@ -0,0 +1,157 @@ +#!/usr/bin/env Rscript + +source('./aux_functions.R') + +# create a parser and add command-line arguments +p <- + arg_parser("Convert sv-channels BEDPE predictions into VCF format.") +p <- + add_argument( + p, + "-i", + help = paste("a path containing multiple BEDPE files (CV mode"), + type = "character", + default = '../genome_wide/results' + ) +p <- + add_argument(p, "-f", help = "ENCODE blacklist", type = "character", + default = '../../data/ENCFF001TDO.bed' + ) +p <- + add_argument(p, "-n", help = "BED file with regions containing Ns", type = "character", + default = '../../data/reference_N_regions.bed' + ) +p <- + add_argument(p, "-m", help = "mode: 'split_reads', 'gridss', 'manta', 'delly', 'lumpy'", + type = "character", + default="split_reads" + ) +p <- + add_argument(p, "-o", help = "Output in BEDPE", type = "character", + default="../genome_wide/results/sv-channels" + ) + +# parse the command line arguments +argv <- parse_args(p) +if (is.na(argv$i)) +{ + message('Input path missing') + print(p) + q(status = 1) +} else if (is.na(argv$o)) +{ + message('Output file missing') + print(p) + q(status = 1) +} + +input_path <- argv$i +regions_for_filtering <- argv$f +ref_regions <- argv$n +mode <- argv$m +output_fn <- argv$o + +# For each svtype, collect all predictions in BEDPE format and merge the SVs using SVA +sv_regions <- GRangesList() +sv_types <- c('DEL', 'INS', 'INV', 'DUP', 'CTX') +#for (svtype in ) + +for (svtype in sv_types) +{ + svtype_path <- file.path(input_path, svtype) + if(file.exists(svtype_path)) + { + # svtype <- 'DEL' + print(svtype) + filenames <- + list.files( + path = svtype_path, + pattern = "correct.bedpe$", + recursive = TRUE, + full.names = TRUE + ) + + print(filenames) + bedpe.file <- + file.path(input_path, paste(mode, '_', svtype, '.bedpe', sep = '')) + print(bedpe.file) + if (file.exists(bedpe.file)) { + file.remove(bedpe.file) + } + + for (f in filenames) + { + cmd <- + paste( + "awk '{if($1==$4){print $0\"\t*\t*\t\" $5-$2}else{print $0\"\t*\t*\t\"0}}'", + f, + '>>', + bedpe.file + ) + system(cmd) + } + + print('Loading predictions...') + + if(file.exists(bedpe.file)) + { + + sv_regions[[svtype]] <- + load_bedpe(bedpe.file, regions_for_filtering, ref_regions) + + + # Merge SVs + if (svtype != 'INS') + { + hits <- findBreakpointOverlaps( + sv_regions[[svtype]], + sv_regions[[svtype]], + # read pair based callers make imprecise calls. + # A margin around the call position is required when matching with the truth set + maxgap = 100, + # Since we added a maxgap, we also need to restrict the mismatch between the + # size of the events. We don't want to match a 100bp deletion with a + # 5bp duplication. This will happen if we have a 100bp margin but don't also + # require an approximate size match as well + sizemargin = 0.25, + ignore.strand = TRUE, + # We also don't want to match a 20bp deletion with a 20bp deletion 80bp away + # by restricting the margin based on the size of the event, we can make sure + # that simple events actually do overlap + restrictMarginToSizeMultiple = 0.5 + ) + data <- as.data.frame(hits) + getfirst <- function(x) { + x[1] + } + get_best_qual <- + function(x) { + x[which.max(sv_regions[[svtype]]$QUAL[x])] + } + aggdata <- + aggregate(data$queryHits, + by = list(data$subjectHits), + FUN = get_best_qual) + sv_regions[[svtype]] <- sv_regions[[svtype]][unique(aggdata$x)] + sv_regions[[svtype]] <- + sv_regions[[svtype]][sv_regions[[svtype]]$partner %in% names(sv_regions[[svtype]])] + } + } + print(length(sv_regions[[svtype]])) + } +} + +# Export to BEDPE +for (svtype in sv_types) +{ + if(length(sv_regions[[svtype]])>0){ + bp_pairs <- breakpointgr2pairs(sv_regions[[svtype]]) + out_file = paste(output_fn, svtype, 'bedpe', sep='.') + print(out_file) + rtracklayer::export(bp_pairs, con = out_file) + } +} +#concatenate the BEDPE files +system(paste("cat ", output_fn, ".*.bedpe", " > ", output_fn, ".bedpe", sep="")) + + diff --git a/scripts/R/vcf2bedpe.R b/scripts/R/vcf2bedpe.R index ee999ce0..e59a9b05 100755 --- a/scripts/R/vcf2bedpe.R +++ b/scripts/R/vcf2bedpe.R @@ -1,88 +1,106 @@ #!/usr/bin/env Rscript +options(scipen = 999) + suppressPackageStartupMessages(require(StructuralVariantAnnotation)) library(tools) library(argparser, quietly = TRUE) -script.name <- basename(sub(".*=", "", commandArgs()[4])) # script name +script.name <- + basename(sub(".*=", "", commandArgs()[4])) # script name # create a parser and add command-line arguments -p <- arg_parser("Convert VCF output of Manta, DELLY, LUMPY or GRIDSS to BEDPE format.") -p <- add_argument(p, "-i", help = "Input in VCF", type = "character") -p <- add_argument(p, "-o", help = "Output in BEDPE", type = "character") -p <- add_argument(p, "-p", help = "SVTYPE=INS if insertion length >= SV length * p", -type = "numeric", default = 0.7) -p <- add_argument(p, "-l", help = "Minimum SV length to consider", type = "int", -default = 50) +p <- + arg_parser("Convert VCF output of Manta, DELLY, LUMPY or GRIDSS to BEDPE format.") +p <- + add_argument(p, "-i", help = "Input in VCF", type = "character") +p <- + add_argument(p, "-o", help = "Output in BEDPE", type = "character") +p <- + add_argument(p, + "-p", + help = "SVTYPE=INS if insertion length >= SV length * p", + type = "numeric", + default = 0.7) +p <- + add_argument(p, + "-l", + help = "Minimum SV length to consider", + type = "int", + default = 50) # parse the command line arguments argv <- parse_args(p) if (is.na(argv$i)) { - print(p) - q(status = 1) + print(p) + q(status = 1) } if (is.na(argv$o)) { - argv$o = paste(file_path_sans_ext(argv$i), "bedpe", sep = '.') + argv$o = paste(file_path_sans_ext(argv$i), "bedpe", sep = '.') } # SV types assigned according to # https://github.com/PapenfussLab/gridss/blob/7b1fedfed32af9e03ed5c6863d368a821a4c699f/example/simple-event-annotation.R#L9 apply_svtype <- function(gr, p_inslen) { - gr$svtype <- - ifelse( - seqnames(gr) != seqnames(partner(gr)), - "TRA", - ifelse( - gr$insLen >= abs(gr$svLen) * p_inslen, - "INS", + gr$svtype <- ifelse( - strand(gr) == strand(partner(gr)), - "INV", - ifelse(xor( - start(gr) < start(partner(gr)), strand(gr) == "-" - ), "DEL", - "DUP") + seqnames(gr) != seqnames(partner(gr)), + "CTX", + ifelse( + gr$insLen >= abs(gr$svLen) * p_inslen, + "INS", + ifelse( + strand(gr) == strand(partner(gr)), + "INV", + ifelse(xor( + start(gr) < start(partner(gr)), strand(gr) == "-" + ), "DEL", + "DUP") + ) + ) ) - ) - ) - gr + gr } -# t est if there is at least one argument: if not, return an error -#if (length(args) <= 1) { -# print_usage() -#if (! "o" %in% names(argv)) -#{ -# # default output file -# argv$o = paste(file_path_sans_ext(argv$i), "bedpe", sep='.') -#} - sv_callset_vcf <- VariantAnnotation::readVcf(argv$i) -# update info header -info(header(sv_callset_vcf)) <- rbind(info(header(sv_callset_vcf)), - data.frame(Number = '4', Type = 'String', Description = 'DELLY CT')) -# update info -info(sv_callset_vcf) <- cbind(info(sv_callset_vcf), - data.frame(CT = factor(rep('3to5', nrow(info(sv_callset_vcf))), - levels = c('5to5', '3to3', '3to5', '5to3')))) -# TRA -idx <- which(info(sv_callset_vcf)$SVTYPE == 'TRA') -info(sv_callset_vcf)$CT[idx[seq(1, length(idx), by = 2)]] <- '3to3' -info(sv_callset_vcf)$CT[idx[seq(2, length(idx), by = 2)]] <- '5to5' - -# INV -idx <- which(info(sv_callset_vcf)[['SVTYPE']] == 'INV') -info(sv_callset_vcf)$CT[idx[seq(1, length(idx), by = 2)]] <- '3to5' -info(sv_callset_vcf)$CT[idx[seq(2, length(idx), by = 2)]] <- '5to3' - -# other SVTYPEs -idx <- which(! info(sv_callset_vcf)[['SVTYPE']] %in% c('INV', 'TRA')) -info(sv_callset_vcf)$CT[idx] <- '3to5' +# if the file was generated with SURVIVOR simSV, treat translocations as DELLY TRA entries +if ('source' %in% names(meta(header(sv_callset_vcf)))){ + if (meta(header(sv_callset_vcf))[['source']]$Value == 'Sniffles') + { + # Make TRA IDs unique + sv.names <- names(rowRanges(sv_callset_vcf)) + idx <- grep('TRA', names(rowRanges(sv_callset_vcf))) + names(rowRanges(sv_callset_vcf))[idx] <- + paste(sv.names[idx], as.character(1 - idx %% 2 + 1), sep = '_') + + # update info header + info(header(sv_callset_vcf)) <- + rbind(info(header(sv_callset_vcf)), + data.frame( + Number = '4', + Type = 'String', + Description = 'DELLY CT' + )) + # update info + info(sv_callset_vcf) <- cbind(info(sv_callset_vcf), + data.frame(CT = factor( + rep('3to5', nrow(info(sv_callset_vcf))), + levels = c('5to5', '3to3', '3to5', '5to3') + ))) + # TRA + idx <- which(info(sv_callset_vcf)$SVTYPE == 'TRA') + if (length(idx) > 0) { + info(sv_callset_vcf)$CT[idx[seq(1, length(idx), by = 2)]] <- '3to3' + info(sv_callset_vcf)$CT[idx[seq(2, length(idx), by = 2)]] <- + '5to5' + } + } +} # Not including breakends (unpaired breakpoints) # bpgr <- breakpointRanges(sv_callset_vcf) @@ -90,13 +108,34 @@ info(sv_callset_vcf)$CT[idx] <- '3to5' # begr <- breakendRanges(sv_callset_vcf) # gr <- sort(c(bpgr, begr)) -gr <- breakpointRanges(sv_callset_vcf) +# generate SV IDs as 'svtype_chromosome_position' +sv_callset_vcf.ids <- + paste(substr(as.character(mcols( + rowRanges(sv_callset_vcf) + )$ALT), 2, 4), + seqnames(rowRanges(sv_callset_vcf)), + start(rowRanges(sv_callset_vcf)), + sep = "_") + +# SVA considers some DELs generated by SURVIVOR as INS +gr <- StructuralVariantAnnotation::breakpointRanges(sv_callset_vcf) + +#print('SVTYPEs loaded with StructuralVariantAnnotation::breakpointRanges') +#print(table(gr$svtype)) + +gr.ids <- + paste(substr(as.character(mcols( + rowRanges(sv_callset_vcf) + )$ALT), 2, 4), + seqnames(rowRanges(sv_callset_vcf)), + start(rowRanges(sv_callset_vcf)), + sep = "_") gr <- apply_svtype(gr, p_inslen = argv$p) # select SVs >= 50 bp. svLen==NA for svtype=='BP' -gr <- gr[abs(gr$svLen) >= argv$l | gr$svtype == 'TRA'] +gr <- gr[abs(gr$svLen) >= argv$l | gr$svtype == 'CTX'] -bedpe <- breakpointgr2bedpe(gr) +bedpe <- StructuralVariantAnnotation::breakpointgr2bedpe(gr) # create a vector with mappings: sourceId -> svtype svtype_vec <- gr$svtype @@ -104,14 +143,37 @@ names(svtype_vec) <- names(gr) # typeof(svtype_vec) must be equal to typeof(bedpe_keys) bedpe_keys <- as.vector(bedpe[, 7]) -bedpe_svtype <- cbind(bedpe[, 1 : 6], svtype_vec[bedpe_keys]) + +bedpe_svtype <- cbind(bedpe[, 1:6], SVTYPE = svtype_vec[bedpe_keys], name = bedpe$name) + +# inversions should be one per line in the BEDPE file +bedpe.ins <- bedpe_svtype[bedpe_svtype$SVTYPE=='INV',] + +bedpe.ins <- bedpe.ins[grep(as.character(bedpe.ins$name), pattern='_bp1'),] + +bedpe_svtype <- rbind( + bedpe_svtype[bedpe_svtype$SVTYPE!='INV',], + bedpe.ins +) +# remove the name +bedpe_svtype <- bedpe_svtype[,-which(names(bedpe_svtype)=='name')] # check that all SVs with svtype==BP have breakpoints on different chromosomes -if (any(bedpe_svtype[, 1] == bedpe_svtype[, 4] & bedpe_svtype[, 7] == 'TRA')) +if (any(bedpe_svtype[, 1] == bedpe_svtype[, 4] & + bedpe_svtype[, 7] == 'CTX')) { - stop("Some SVs with svtype BP contain breakpoints that are both on the same chromosomes") + stop("Some SVs with svtype BP contain breakpoints that are both on the same chromosomes") } +print('SVTYPEs in BEDPE output:') +print(table(bedpe_svtype$SVTYPE)) + # write output in BEDPE -write.table(file = argv$o, bedpe_svtype, quote = FALSE, row.names = FALSE, -col.names = FALSE, sep = '\t') +write.table( + file = argv$o, + bedpe_svtype, + quote = FALSE, + row.names = FALSE, + col.names = FALSE, + sep = '\t' +) diff --git a/scripts/genome_wide/add_win_channels.py b/scripts/genome_wide/add_win_channels.py index 561920d8..b42a8ed3 100644 --- a/scripts/genome_wide/add_win_channels.py +++ b/scripts/genome_wide/add_win_channels.py @@ -16,6 +16,8 @@ def init_log(logfile): def parse_args(): + + default_win = 25 parser = argparse.ArgumentParser( description='Add window specific channels') @@ -27,47 +29,37 @@ def parse_args(): parser.add_argument('-w', '--win', type=int, - default=200, + default=default_win, help="Window size") parser.add_argument('-i', '--input', type=str, - default='./windows.npz', + default='./cnn/win'+str(default_win)+'/split_reads/windows/DEL/windows.npz', help="input file") parser.add_argument('-o', '--output', type=str, - default='./windows_en.npz', + default='./cnn/win'+str(default_win)+'/split_reads/windows/DEL/windows_en.npz', help="output file") parser.add_argument('-l', '--logfile', - default='./windows_en.log', + default='./cnn/win'+str(default_win)+'/split_reads/windows/DEL/windows_en.log', help='File in which to write logs.') - parser.add_argument('-p', - '--padding', - type=int, - default=10, - help='Number of column for the zero-valued in between' - 'the two side-by-side window arrays to add based on the CNN kernel size' - 'to avoid artifacts that can be generated when the kernel' - 'includes data points from both windows') - parser.add_argument('-n', + parser.add_argument('-lp', '--log_every_n_pos', type=int, default=1000, - help='File in which to write logs.') - parser.add_argument('-m', - '--max_coverage', + help='Write in log file every log_every_n_pos positions') + parser.add_argument('-p', + '--padding', type=int, - default=1000, - help='Do not compute window-specific channels for regions' - 'with read depth higher than max_coverage') + default=10, + help="Length of the padding in between windows") return parser.parse_args() def get_channels(): - ch = [ # All reads (clipped or not) 'F_AR_N', 'R_AR_N', @@ -105,70 +97,110 @@ def update_channel(X, ch, iter, read, win_mid_pos, is_second_win, win_len, paddi orientation = 'R' if read.is_reverse else 'F' start_win = win_len + padding if is_second_win else 0 + end_win = win_len * 2 + padding if is_second_win else win_len + + abs_start = int(win_mid_pos - int(win_len / 2)) if win_len % 2 == 0 else \ + int(win_mid_pos - int(win_len + 1 / 2)) - abs_start = int(win_mid_pos - win_len / 2) - abs_end = int(win_mid_pos + win_len / 2) + abs_end = int(win_mid_pos + int(win_len / 2)) if win_len % 2 == 0 else \ + int(win_mid_pos + int(win_len + 1 / 2)) start = max(read.reference_start, abs_start) end = min(read.reference_end, abs_end) # print('reference_start:{}, reference_end:{}'.format(s0, e0)) + #rel_start = start - abs_start + #rel_end = end - abs_start + rel_start = start_win + start - abs_start rel_end = start_win + end - abs_start - # print('relative reference_start:{}, relative reference_end:{}'.format(s, e)) - k = '_'.join([orientation, clipped_state, clipping]) - if k in ch.keys(): - X[iter, rel_start:rel_end, ch[k]] += 1 + # print(read) + # print('start_win={}\nend_win={}\nabs_start={}\nabs_end={}\nstart={}\nend={}\nrel_start={}\nrel_end={}\n'.format( + # start_win, end_win, abs_start, + # abs_end, start, end, rel_start, + # rel_end)) - if not read.is_proper_pair: - k = '_'.join(['DR', orientation]) - if k in ch.keys(): - X[iter, rel_start:rel_end, ch[k]] += 1 + assert rel_start >= 0 + assert rel_end >= 0 - if read.is_reverse and not read.mate_is_reverse \ - and read.reference_start < read.next_reference_start: - k = '_'.join(['DUP', 'A']) - if k in ch.keys(): - X[iter, rel_start:rel_end, ch[k]] += 1 + assert start_win <= rel_start <= end_win + assert start_win <= rel_end <= end_win + + skip = False + if is_left_clipped(read): + if (is_second_win and win_len + padding <= rel_start < win_len * 2 + padding) or \ + (not is_second_win and 0 <= rel_start < win_len): + rel_pos = rel_start + else: + skip = True + elif is_right_clipped(read): + if (is_second_win and win_len + padding <= rel_end < win_len * 2 + padding) or \ + (not is_second_win and 0 <= rel_end < win_len): + rel_pos = rel_end + else: + skip = True + else: + rel_pos = np.arange(max(start_win, rel_start), min(rel_end, end_win)) + + if not skip: + + # print('relative reference_start:{}, relative reference_end:{}'.format(s, e)) + k = '_'.join([orientation, clipped_state, clipping]) - if not read.is_reverse and read.mate_is_reverse \ - and read.reference_start > read.next_reference_start: - k = '_'.join(['DUP', 'B']) if k in ch.keys(): - X[iter, rel_start:rel_end, ch[k]] += 1 + X[iter, rel_pos, ch[k]] += 1 - if read.is_reverse == read.mate_is_reverse: - if read.reference_start < read.next_reference_start: - k = '_'.join(['INV', 'B']) + if not read.is_proper_pair: + k = '_'.join(['DR', orientation]) if k in ch.keys(): - X[iter, rel_start:rel_end, ch[k]] += 1 - else: - k = '_'.join(['INV', 'A']) + X[iter, rel_pos, ch[k]] += 1 + + if read.is_reverse and not read.mate_is_reverse \ + and read.reference_start < read.next_reference_start: + k = '_'.join(['DUP', 'A']) if k in ch.keys(): - X[iter, rel_start:rel_end, ch[k]] += 1 + X[iter, rel_pos, ch[k]] += 1 + + if not read.is_reverse and read.mate_is_reverse \ + and read.reference_start > read.next_reference_start: + k = '_'.join(['DUP', 'B']) + if k in ch.keys(): + X[iter, rel_pos, ch[k]] += 1 - if read.reference_name != read.next_reference_name: if read.is_reverse == read.mate_is_reverse: if read.reference_start < read.next_reference_start: - k = '_'.join(['TRA', 'S']) + k = '_'.join(['INV', 'B']) if k in ch.keys(): - X[iter, rel_start:rel_end, ch[k]] += 1 + X[iter, rel_pos, ch[k]] += 1 else: - k = '_'.join(['TRA', 'O']) + k = '_'.join(['INV', 'A']) if k in ch.keys(): - X[iter, rel_start:rel_end, ch[k]] += 1 + X[iter, rel_pos, ch[k]] += 1 + + if read.reference_name != read.next_reference_name: + if read.is_reverse == read.mate_is_reverse: + if read.reference_start < read.next_reference_start: + k = '_'.join(['TRA', 'S']) + if k in ch.keys(): + X[iter, rel_pos, ch[k]] += 1 + else: + k = '_'.join(['TRA', 'O']) + if k in ch.keys(): + X[iter, rel_pos, ch[k]] += 1 return X -def add_channels(ibam, win, ifile, padding, log_every_n_pos, max_coverage): +def add_channels(args, aln): - def get_reads(ibam, chrom, pos): - return [read for read in ibam.fetch(chrom, pos - win / 2, pos + win / 2)] + win = args.win if args.win % 2 == 0 else args.win + 1 + ifile = args.input + padding = args.padding + log_every_n_pos = args.log_every_n_pos - def count_reads(ibam, chrom, pos): - return ibam.count(chrom, pos - win / 2, pos + win / 2) + def get_reads(chrom, pos): + return [read for read in aln.fetch(chrom, pos - int(win / 2), pos + int(win / 2))] # Load the windows logging.info("Loading windows...") @@ -184,9 +216,8 @@ def count_reads(ibam, chrom, pos): # Initialize numpy array X_enh = np.zeros(shape=(X.shape[:2] + (len(ch),)), dtype=np.int8) - # Skip regions with too high read coverage? - too_high_cov_i = [] - too_high_cov_p = [] + # print(X.shape) + # print(X_enh.shape) for i, p in enumerate(y.keys(), start=0): @@ -199,17 +230,12 @@ def count_reads(ibam, chrom, pos): last_t = time() # Get genomic coordinates - chrom1, pos1, chrom2, pos2 = p.split('_') + chrom1, pos1, chrom2, pos2, strand_info = p.split('_') pos1, pos2 = int(pos1), int(pos2) - if count_reads(ibam, chrom1, pos1) > max_coverage and count_reads(ibam, chrom2, pos2) > max_coverage: - too_high_cov_i.append(i) - too_high_cov_p.append(p) - continue - # Fetch reads overlapping each window - win1_reads = get_reads(ibam, chrom1, pos1) - win2_reads = get_reads(ibam, chrom2, pos2) + win1_reads = get_reads(chrom1, pos1) + win2_reads = get_reads(chrom2, pos2) # Which reads are in both windows? win1_read_names_set = set([read.query_name for read in win1_reads]) @@ -226,35 +252,31 @@ def count_reads(ibam, chrom, pos): for r in win2_reads: X_enh = update_channel(X_enh, ch, i, r, pos2, True, win, padding) - logging.info("{} regions with too high coverage".format(len(too_high_cov_i))) - X = np.concatenate((X, X_enh), axis=2) + for i in np.arange(X_enh.shape[2]): + logging.info('win channels array:' + \ + 'non-zero elements at index {}:{}'.format(i, np.argwhere(X_enh[i, :] != 0).shape[0])) - X = np.delete(X, too_high_cov_i, axis=0) - for p in too_high_cov_p: - del y[p] + X = np.concatenate((X, X_enh), axis=2) + print(X.shape) - logging.info("X shape:{}, y length:{}".format(X.shape, len(y))) + for i in np.arange(X.shape[2]): + logging.info('full channels array:' + \ + 'NaN elements at index {}:{}'.format(i, len(np.argwhere(np.isnan(X[i, :]))))) return X, y def main(): - # parse arguments args = parse_args() # initialize log file init_log(args.logfile) - bam_handle = pysam.AlignmentFile(args.bam, "rb") t0 = time() - X, y = add_channels(ibam=bam_handle, - win=args.win, - ifile=args.input, - padding=args.padding, - log_every_n_pos=args.log_every_n_pos, - max_coverage=args.max_coverage - ) + aln = pysam.AlignmentFile(args.bam, "rb") + + X, y = add_channels(args, aln) save_windows(X, y, args.output) diff --git a/scripts/genome_wide/chr_array.py b/scripts/genome_wide/chr_array.py index 04d782a8..32fcf77b 100644 --- a/scripts/genome_wide/chr_array.py +++ b/scripts/genome_wide/chr_array.py @@ -14,11 +14,6 @@ from functions import * -config = get_config_file() -HPC_MODE = config["DEFAULT"]["HPC_MODE"] -REF_GENOME = config["DEFAULT"]["REF_GENOME"] - - def get_chr_len(ibam, chrName): # check if the BAM file exists assert os.path.isfile(ibam), ibam + " file not existent!" @@ -45,15 +40,6 @@ def count_clipped_read_positions(cpos_cnt): (i + 1, len([k for k, v in cpos_cnt.items() if v > i]))) -def get_mappability_bigwig(): - mappability_file = os.path.join("/hpc/cog_bioinf/ridder/users/lsantuari/Datasets/Mappability", - REF_GENOME, REF_GENOME + ".151mer.bw") if HPC_MODE \ - else os.path.join("/Users/lsantuari/Documents/Data/GEM", REF_GENOME + ".151mer.bw") - bw = pyBigWig.open(mappability_file) - - return bw - - def load_bam(ibam): with pysam.AlignmentFile(ibam, "rb") as f: return f @@ -115,8 +101,6 @@ def load_channel(chr_list, outDir, ch): if suffix == '.npy.gz': with gzip.GzipFile(filename, 'r') as fin: channel_data[chrom][ch] = np.load(fin) - channel_data[chrom][ch] = np.swapaxes(channel_data[chrom][ch], - 0, 1) else: with gzip.GzipFile(filename, 'r') as fin: channel_data[chrom][ch] = json.loads( @@ -127,27 +111,26 @@ def load_channel(chr_list, outDir, ch): def create_carray(ibam, chrom, twobit, bigwig, outDir, cmd_name): - def get_percentile(v): - return np.percentile(v, np.arange(50, 110, 10)) - chrlen = get_chr_len(ibam, chrom) - n_channels = 48 + + n_channels = 54 + chr_array = np.zeros(shape=(chrlen, n_channels), - dtype=np.float32) # bz.zeros + dtype=np.float64) # bz.zeros bw_map = pyBigWig.open(bigwig) # get_mappability_bigwig() # dictionary of key choices direction_list = { 'clipped_reads': [ - 'left_F', 'left_R', 'right_F', 'right_R', 'disc_right_F', - 'disc_right_R', 'disc_left_F', 'disc_left_R', 'D_left_F', + 'left_F', 'left_R', 'right_F', 'right_R', 'disc_left_F', + 'disc_left_R', 'disc_right_F', 'disc_right_R', 'D_left_F', 'D_left_R', 'D_right_F', 'D_right_R', 'I_F', 'I_R' ], 'split_reads': ['left_F', 'left_R', 'right_F', 'right_R'], 'split_read_distance': ['left_F', 'left_R', 'right_F', 'right_R'], - 'clipped_reads_inversion': ['before', 'after'], - 'clipped_reads_duplication': ['before', 'after'], - 'clipped_reads_translocation': ['opposite', 'same'], + 'clipped_reads_inversion': ['before', 'after', 'before_split', 'after_split'], + 'clipped_reads_duplication': ['before', 'after', 'before_split', 'after_split'], + 'clipped_reads_translocation': ['opposite', 'same', 'opposite_split', 'same_split'], 'clipped_read_distance': ['forward', 'reverse'] } @@ -205,10 +188,11 @@ def get_percentile(v): vals = np.fromiter(channel_data[chrom][current_channel] [split_direction].values(), dtype=np.float32) - chr_array[idx, channel_index] = vals + if len(idx) > 0: + chr_array[idx, channel_index] = vals - assert chr_array[idx, channel_index].any(), \ - print('{}:{} is all zeros!'.format(current_channel, split_direction)) + assert chr_array[idx, channel_index].any(), \ + print('{}:{} is all zeros!'.format(current_channel, split_direction)) channel_index += 1 del channel_data[chrom][current_channel][split_direction] @@ -230,7 +214,8 @@ def get_percentile(v): [current_channel][split_direction] [clipped_arrangement].values()))) - chr_array[idx, channel_index] = vals + if len(idx) > 0: + chr_array[idx, channel_index] = vals channel_index += 1 # vals = np.array(list(map(get_percentile, @@ -258,8 +243,8 @@ def get_percentile(v): map( statistics.median, channel_data[chrom] [current_channel][split_direction].values()))) - - chr_array[idx, channel_index] = vals + if len(idx) > 0: + chr_array[idx, channel_index] = vals channel_index += 1 # vals = np.array(list(map(get_percentile, @@ -277,9 +262,19 @@ def get_percentile(v): # bw_chrom = chrom.replace('chr', '') # start and end position hard coded at the moment, to be updated with (0, chrlen) - chr_array[:, channel_index] = np.array(bw_map.values( - chrom, 44000000, 46000000), - dtype=np.float32) + + # test data + if chr_array.shape[0] == 2000000: + # test data + chr_array[:, channel_index] = np.array(bw_map.values( + chrom, 0, chr_array.shape[0]), + dtype=np.float32) + else: + # real data + chr_array[:, channel_index] = np.array(bw_map.values( + chrom, 0, chrlen), + dtype=np.float32) + channel_index += 1 current_channel = 'one_hot_encoding' @@ -312,51 +307,43 @@ def main(): :return: None ''' - # Default BAM file for testing - # On the HPC - # wd = '/hpc/cog_bioinf/ridder/users/lsantuari/Datasets/DeepSV/'+ - # 'artificial_data/run_test_INDEL/samples/T0/BAM/T0/mapping' - # inputBAM = wd + "T0_dedup.bam" - # Locally - wd = '/Users/lsantuari/Documents/Data/HPC/DeepSV/Artificial_data/run_test_INDEL/BAM/' - inputBAM = wd + "T1_dedup.bam" - + default_chr = '22' parser = argparse.ArgumentParser( description='Create channels from saved data') parser.add_argument('-b', '--bam', type=str, - default=inputBAM, + default='../../data/test.bam', help="Specify input file (BAM)") parser.add_argument('-c', '--chr', type=str, - default='17', + default=default_chr, help="Specify chromosome") parser.add_argument('-m', '--map', type=str, - default='chr22.bw', + default='../../data/test.bw', help="Specify input file (bigWig)") parser.add_argument('-t', '--twobit', type=str, - default='chr22.2bit', + default='../../data/test.2bit', help="Specify input file (2bit)") parser.add_argument('-o', '--out', type=str, - default='channel_maker.npy.gz', + default='chr_array/'+default_chr+'_chr_array', help="Specify output") parser.add_argument( '-p', '--outputpath', type=str, - default='/Users/lsantuari/Documents/Processed/channel_maker_output', + default='.', help="Specify output path") parser.add_argument('-l', '--logfile', - default='window_maker.log', + default='chr_array.log', help='File in which to write logs.') parser.add_argument('-w', '--window', @@ -369,7 +356,7 @@ def main(): cmd_name = 'chr_array' output_dir = os.path.join(args.outputpath, cmd_name) os.makedirs(output_dir, exist_ok=True) - logfilename = os.path.join(output_dir, args.logfile) + logfilename = os.path.join(output_dir, args.chr+'_'+args.logfile) # output_file = os.path.join(output_dir, args.out) FORMAT = '%(asctime)s %(message)s' diff --git a/scripts/genome_wide/clipped_read_distance.py b/scripts/genome_wide/clipped_read_distance.py index e2852eae..6b1d2577 100644 --- a/scripts/genome_wide/clipped_read_distance.py +++ b/scripts/genome_wide/clipped_read_distance.py @@ -11,7 +11,7 @@ from functions import * -def get_clipped_read_distance(ibam, chrName, outFile): +def get_clipped_read_distance(ibam, chrName, min_mapq, outFile): ''' :param ibam: BAM file in input @@ -23,12 +23,11 @@ def get_clipped_read_distance(ibam, chrName, outFile): # Check if the BAM file in input exists assert os.path.isfile(ibam) - # minimum read mapping quality to consider - config = get_config_file() - minMAPQ = config["DEFAULT"]["MIN_MAPQ"] - # open BAM file bamfile = pysam.AlignmentFile(ibam, "rb") + + bam_mean, bam_stddev = get_insert_size(ibam, bamfile, min_mapq) + # get chromosome length from BAM header header_dict = bamfile.header chrLen = [i['LN'] for i in header_dict['SQ'] if i['SN'] == chrName][0] @@ -60,15 +59,15 @@ def set_distance(direction, read, dist): if is_left_clipped(read): pos = read.reference_start - #if pos not in clipped_read_distance[direction]['left'].keys(): + # if pos not in clipped_read_distance[direction]['left'].keys(): # clipped_read_distance[direction]['left'][pos] = [dist] - #else: + # else: clipped_read_distance[direction]['left'][pos].append(dist) elif is_right_clipped(read): pos = read.reference_end + 1 - #if pos not in clipped_read_distance[direction]['right'].keys(): + # if pos not in clipped_read_distance[direction]['right'].keys(): # clipped_read_distance[direction]['right'][pos] = [dist] - #else: + # else: clipped_read_distance[direction]['right'][pos].append(dist) # Consider all the chromosome: interval [0, chrLen] @@ -78,7 +77,7 @@ def set_distance(direction, read, dist): iter = bamfile.fetch(chrName, start_pos, stop_pos) # Log information every n_r reads - n_r = 10**6 + n_r = 10 ** 6 # print(n_r) last_t = time() # print(type(last_t)) @@ -92,11 +91,12 @@ def set_distance(direction, read, dist): last_t = time() # Both read and mate should be mapped - if not read.is_unmapped and not read.mate_is_unmapped and read.mapping_quality >= minMAPQ: + if not read.is_unmapped and not read.mate_is_unmapped and read.mapping_quality >= min_mapq: # Read and mate should be mapped on the same chromosome if read.reference_name == read.next_reference_name: # Calculate absolute read to mate distance dist = abs(read.reference_start - read.next_reference_start) + dist = (dist - bam_mean) / bam_stddev # Read is mapped in forward orientation, mate is in reverse orientation, read is mapped before mate if not read.is_reverse and read.mate_is_reverse and read.reference_start <= read.next_reference_start: set_distance('forward', read, dist) @@ -115,42 +115,39 @@ def set_distance(direction, read, dist): def main(): - # Default BAM file for testing - # On the HPC - #wd = '/hpc/cog_bioinf/ridder/users/lsantuari/Datasets/DeepSV/artificial_data/run_test_INDEL/samples/T0/BAM/T0/mapping' - #inputBAM = wd + "T0_dedup.bam" - # Locally - wd = '/Users/lsantuari/Documents/Data/HPC/DeepSV/Artificial_data/run_test_INDEL/BAM/' - inputBAM = wd + "T1_dedup.bam" - parser = argparse.ArgumentParser( description= 'Create channels with distance between clipped/non-clipped reads') parser.add_argument('-b', '--bam', type=str, - default=inputBAM, + default='../../data/test.bam', help="Specify input file (BAM)") parser.add_argument('-c', '--chr', type=str, - default='17', + default='12', help="Specify chromosome") parser.add_argument('-o', '--out', type=str, default='clipped_read_distance.json.gz', help="Specify output") - parser.add_argument( - '-p', - '--outputpath', - type=str, - default='/Users/lsantuari/Documents/Processed/channel_maker_output', - help="Specify output path") + parser.add_argument('-p', + '--outputpath', + type=str, + default='.', + help="Specify output path") parser.add_argument('-l', '--logfile', default='clipped_read_distance.log', help='File in which to write logs.') + parser.add_argument('-m', + '--min_mapq', + type=int, + default=10, + help='Minimum read mapping quality') + args = parser.parse_args() @@ -169,10 +166,10 @@ def main(): t0 = time() get_clipped_read_distance(ibam=args.bam, chrName=args.chr, + min_mapq=args.min_mapq, outFile=output_file) logging.info('Time: clipped read distance on BAM %s and Chr %s: %f' % (args.bam, args.chr, (time() - t0))) - if __name__ == '__main__': main() diff --git a/scripts/genome_wide/clipped_read_pos.py b/scripts/genome_wide/clipped_read_pos.py index d4d8ad4d..9f24a985 100644 --- a/scripts/genome_wide/clipped_read_pos.py +++ b/scripts/genome_wide/clipped_read_pos.py @@ -42,7 +42,7 @@ def get_clipped_read_positions(ibam, chr_list, outFile): iter = bamfile.fetch() # Print every n_r alignments processed - n_r = 10**6 + n_r = 10 ** 6 # Record the current time last_t = time() @@ -62,7 +62,7 @@ def get_clipped_read_positions(ibam, chr_list, outFile): # Both read and mate should be mapped, read should have a minimum mapping quality # if (not read.is_unmapped) and (not read.mate_is_unmapped) and read.mapping_quality >= minMAPQ: - if (not read.is_unmapped) and read.mapping_quality >= minMAPQ: + if (not read.is_unmapped) and read.mapping_quality >= minMAPQ and not has_suppl_aln(read): if (read.query_name, read.reference_start ) in lc_mate_set[read.next_reference_name]: @@ -85,7 +85,6 @@ def get_clipped_read_positions(ibam, chr_list, outFile): right_clipped_pos_by_query[read.query_name]) if is_left_clipped(read): - # read.reference_start is the 1-based start position of the read mapped on the reference genome left_clipped_pos_by_query[ read.query_name] = read.reference_start + 1 @@ -93,7 +92,6 @@ def get_clipped_read_positions(ibam, chr_list, outFile): (read.query_name, read.next_reference_start)) if is_right_clipped(read): - # read.reference_end is the 0-based end position of the read mapped on the reference genome right_clipped_pos_by_query[ read.query_name] = read.reference_end @@ -110,9 +108,9 @@ def get_clipped_read_positions(ibam, chr_list, outFile): for chrom in chr_list: left_clipped_pos_cnt[chrom] = Counter(left_clipped_pos[chrom]) right_clipped_pos_cnt[chrom] = Counter(right_clipped_pos[chrom]) - logging.info('Unique positions on Chr{} left clipped: {}'.format( + logging.info('Unique positions on Chr{} left clipped, not split: {}'.format( chrom, len(left_clipped_pos_cnt[chrom]))) - logging.info('Unique positions on Chr{} right clipped: {}'.format( + logging.info('Unique positions on Chr{} right clipped, not split {}'.format( chrom, len(right_clipped_pos_cnt[chrom]))) # Write @@ -127,29 +125,18 @@ def get_clipped_read_positions(ibam, chr_list, outFile): def main(): - # Default BAM file for testing - # On the HPC - # wd = '/hpc/cog_bioinf/ridder/users/lsantuari/Datasets/DeepSV/artificial_data/run_test_INDEL/samples/T0/BAM/T0/mapping' - # inputBAM = wd + "T0_dedup.bam" - # Locally - wd = '/Users/lsantuari/Documents/Data/HPC/DeepSV/Artificial_data/run_test_INDEL/BAM/' - inputBAM = wd + "T1_dedup.bam" - # wd = '/Users/lsantuari/Documents/mount_points/hpc_mnt/Datasets/CretuStancu2017/Patient1/' - # inputBAM = wd + 'Patient1.bam' - - # Default chromosome is 17 for the artificial data # Parse the arguments of the script parser = argparse.ArgumentParser(description='Get clipped reads positions') parser.add_argument('-b', '--bam', type=str, - default=inputBAM, + default='../../data/test.bam', help="Specify input file (BAM)") parser.add_argument('-c', '--chrlist', type=str, - default='17', + default='12,22', help="Comma separated list of chromosomes to consider") parser.add_argument('-o', '--out', @@ -160,7 +147,7 @@ def main(): '-p', '--outputpath', type=str, - default='/Users/lsantuari/Documents/Processed/channel_maker_output', + default='', help="Specify output path") parser.add_argument('-l', '--logfile', diff --git a/scripts/genome_wide/clipped_reads.py b/scripts/genome_wide/clipped_reads.py index 595c5603..131b8950 100644 --- a/scripts/genome_wide/clipped_reads.py +++ b/scripts/genome_wide/clipped_reads.py @@ -12,7 +12,7 @@ from functions import * -def get_clipped_reads(ibam, chr_list, outFile): +def get_clipped_reads(ibam, chr_list, minMAPQ, outFile): ''' :param ibam: input BAM alignment file @@ -24,9 +24,6 @@ def get_clipped_reads(ibam, chr_list, outFile): # Check if the BAM file in input exists assert os.path.isfile(ibam) - config = get_config_file() - minMAPQ = config["DEFAULT"]["MIN_MAPQ"] - # Dictionary to store number of clipped reads per position clipped_reads = dict() clipped_reads_inversion = dict() @@ -39,9 +36,9 @@ def get_clipped_reads(ibam, chr_list, outFile): # For left- and right-clipped reads for split_direction in [ - 'left_F', 'left_R', 'right_F', 'right_R', 'disc_right_F', - 'disc_right_R', 'disc_left_F', 'disc_left_R', 'D_left_F', - 'D_left_R', 'D_right_F', 'D_right_R', 'I_F', 'I_R' + 'left_F', 'left_R', 'right_F', 'right_R', 'disc_right_F', + 'disc_right_R', 'disc_left_F', 'disc_left_R', 'D_left_F', + 'D_left_R', 'D_right_F', 'D_right_R', 'I_F', 'I_R' ]: clipped_reads[chrom][split_direction] = defaultdict(int) @@ -67,11 +64,11 @@ def get_clipped_reads(ibam, chr_list, outFile): clipped_reads_translocation[chrom] = dict() # Mate is mapped before or after? - for mate_position in ['before', 'after']: + for mate_position in ['before', 'after', 'before_split', 'after_split']: clipped_reads_inversion[chrom][mate_position] = defaultdict(int) clipped_reads_duplication[chrom][mate_position] = defaultdict(int) - for orientation in ['opposite', 'same']: + for orientation in ['opposite', 'same', 'opposite_split', 'same_split']: clipped_reads_translocation[chrom][orientation] = defaultdict(int) # Open BAM file @@ -81,8 +78,8 @@ def get_clipped_reads(ibam, chr_list, outFile): iter = bamfile.fetch() # Log information every n_r reads - n_r = 10**6 - #print(n_r) + n_r = 10 ** 6 + # print(n_r) last_t = time() # print(type(last_t)) for i, read in enumerate(iter, start=1): @@ -142,88 +139,107 @@ def get_clipped_reads(ibam, chr_list, outFile): # print('Clipped at the start: %s -> %s' % (str(read.cigarstring), str(read.cigartuples))) # print('Pos:%d, clipped_pos:%d' % (read.reference_start, read.get_reference_positions()[0])) # print('start:'+str(read.get_reference_positions()[0])+'=='+str(read.reference_start)) - #if ref_pos not in clipped_reads['left'].keys(): + # if ref_pos not in clipped_reads['left'].keys(): # clipped_reads['left'][ref_pos] = 1 - #else: - if not read.is_reverse: - clipped_reads[read.reference_name]['left_F'][ - ref_pos] += 1 - if not read.is_proper_pair: - clipped_reads[read.reference_name][ - 'disc_left_F'][ref_pos] += 1 - else: - clipped_reads[read.reference_name]['left_R'][ - ref_pos] += 1 - if not read.is_proper_pair: - clipped_reads[read.reference_name][ - 'disc_left_R'][ref_pos] += 1 + # else: + if not has_suppl_aln(read): + if not read.is_reverse: + clipped_reads[read.reference_name]['left_F'][ + ref_pos] += 1 + if not read.is_proper_pair: + clipped_reads[read.reference_name][ + 'disc_left_F'][ref_pos] += 1 + else: + clipped_reads[read.reference_name]['left_R'][ + ref_pos] += 1 + if not read.is_proper_pair: + clipped_reads[read.reference_name][ + 'disc_left_R'][ref_pos] += 1 # DUPlication, channel 2 # Read is mapped on the Reverse strand and mate is mapped on the Forward strand if read.is_reverse and not read.mate_is_reverse \ - and read.reference_start < read.next_reference_start: # Mate is mapped after read - clipped_reads_duplication[ - read.reference_name]['after'][ref_pos] += 1 + and read.reference_start < read.next_reference_start: # Mate is mapped after read + if not has_suppl_aln(read): + clipped_reads_duplication[ + read.reference_name]['after'][ref_pos] += 1 + else: + clipped_reads_duplication[ + read.reference_name]['after_split'][ref_pos] += 1 # Read is right-clipped elif is_right_clipped(read): # print('Clipped at the end: %s -> %s' % (str(read.cigarstring), str(read.cigartuples))) # print('Pos:%d, clipped_pos:%d' %(read.reference_end, read.get_reference_positions()[-1])) # print('end: '+str(read.get_reference_positions()[-1]) + '==' + str(read.reference_end)) - #if ref_pos not in clipped_reads['right'].keys(): + # if ref_pos not in clipped_reads['right'].keys(): # clipped_reads['right'][ref_pos] = 1 - #else: - if not read.is_reverse: - clipped_reads[read.reference_name]['right_F'][ - ref_pos] += 1 - if not read.is_proper_pair: - clipped_reads[read.reference_name][ - 'disc_right_F'][ref_pos] += 1 - else: - clipped_reads[read.reference_name]['right_R'][ - ref_pos] += 1 - if not read.is_proper_pair: - clipped_reads[read.reference_name][ - 'disc_right_R'][ref_pos] += 1 + # else: + if not has_suppl_aln(read): + if not read.is_reverse: + clipped_reads[read.reference_name]['right_F'][ + ref_pos] += 1 + if not read.is_proper_pair: + clipped_reads[read.reference_name][ + 'disc_right_F'][ref_pos] += 1 + else: + clipped_reads[read.reference_name]['right_R'][ + ref_pos] += 1 + if not read.is_proper_pair: + clipped_reads[read.reference_name][ + 'disc_right_R'][ref_pos] += 1 # DUPlication, channel 1 # Read is mapped on the Forward strand and mate is mapped on the Reverse strand if not read.is_reverse and read.mate_is_reverse: # Mate is mapped before read if read.reference_start > read.next_reference_start: - clipped_reads_duplication[ - read. - reference_name]['before'][ref_pos] += 1 + if not has_suppl_aln(read): + clipped_reads_duplication[ + read. + reference_name]['before'][ref_pos] += 1 + else: + clipped_reads_duplication[ + read. + reference_name]['before_split'][ref_pos] += 1 # The following if statement takes care of the inversion channels # Read and mate are mapped on the same strand: either Forward-Forward or Reverse-Reverse elif read.is_reverse == read.mate_is_reverse: - if is_clipped(read): + if is_clipped(read) and not has_suppl_aln(read): # Mate is mapped before read if read.reference_start > read.next_reference_start: - #if ref_pos not in clipped_reads_inversion['before'].keys(): + # if ref_pos not in clipped_reads_inversion['before'].keys(): # clipped_reads_inversion['before'][ref_pos] = 1 - #else: - #print('Before') - #print(read) + # else: + # print('Before') + # print(read) # print('{}:Inversion before at {}:{}'.format( # read.query_name, # read.reference_name, ref_pos)) - clipped_reads_inversion[read.reference_name][ - 'before'][ref_pos] += 1 + if not has_suppl_aln(read): + clipped_reads_inversion[read.reference_name][ + 'before'][ref_pos] += 1 + else: + clipped_reads_inversion[read.reference_name][ + 'before_split'][ref_pos] += 1 # Mate is mapped after read else: - #if ref_pos not in clipped_reads_inversion['after'].keys(): + # if ref_pos not in clipped_reads_inversion['after'].keys(): # clipped_reads_inversion['after'][ref_pos] = 1 - #else: - #print('After') - #print(read) + # else: + # print('After') + # print(read) # print('{}:Inversion after at {}:{}'.format( # read.query_name, # read.reference_name, ref_pos)) - clipped_reads_inversion[ - read.reference_name]['after'][ref_pos] += 1 + if not has_suppl_aln(read): + clipped_reads_inversion[ + read.reference_name]['after'][ref_pos] += 1 + else: + clipped_reads_inversion[ + read.reference_name]['after_split'][ref_pos] += 1 else: if is_clipped(read): @@ -232,15 +248,23 @@ def get_clipped_reads(ibam, chr_list, outFile): # read.query_name, # read.reference_name, ref_pos, # read.next_reference_name, read.next_reference_start)) - clipped_reads_translocation[ - read.reference_name]['opposite'][ref_pos] += 1 + if not has_suppl_aln(read): + clipped_reads_translocation[ + read.reference_name]['opposite'][ref_pos] += 1 + else: + clipped_reads_translocation[ + read.reference_name]['opposite_split'][ref_pos] += 1 else: # print('{}:Translocation same at {}:{}->{}:{}'.format( # read.query_name, # read.reference_name, ref_pos, # read.next_reference_name, read.next_reference_start)) - clipped_reads_translocation[ - read.reference_name]['same'][ref_pos] += 1 + if not has_suppl_aln(read): + clipped_reads_translocation[ + read.reference_name]['same'][ref_pos] += 1 + else: + clipped_reads_translocation[ + read.reference_name]['same_split'][ref_pos] += 1 # for mate_position in ['after', 'before']: # print([(pos, clipped_reads_inversion[mate_position][pos]) \ @@ -261,40 +285,33 @@ def get_clipped_reads(ibam, chr_list, outFile): def main(): - # Default BAM file for testing - # On the HPC - #wd = '/hpc/cog_bioinf/ridder/users/lsantuari/Datasets/DeepSV/artificial_data/run_test_INDEL/samples/T0/BAM/T0/mapping' - #inputBAM = wd + "T0_dedup.bam" - # Locally - wd = '/Users/lsantuari/Documents/Data/HPC/DeepSV/Artificial_data/run_test_INDEL/BAM/' - inputBAM = wd + "T1_dedup.bam" - - # inputBAM = "/Users/lsantuari/Documents/mount_points/hpc_giab/RMNISTHS_30xdownsample.bam" - - # Default chromosome is 17 for the artificial data - parser = argparse.ArgumentParser( description='Create channels with number of left/right clipped reads') parser.add_argument('-b', '--bam', type=str, - default=inputBAM, + default='../../data/test.bam', help="Specify input file (BAM)") parser.add_argument('-c', '--chrlist', type=str, - default='17', + default='12,22', help="Comma separated list of chromosomes to consider") parser.add_argument('-o', '--out', type=str, default='clipped_reads.json.gz', help="Specify output") + parser.add_argument('-m', + '--min_mapq', + type=int, + default=10, + help='Minimum read mapping quality') parser.add_argument( '-p', '--outputpath', type=str, - default='/Users/lsantuari/Documents/Processed/channel_maker_output', + default='.', help="Specify output path") parser.add_argument('-l', '--logfile', @@ -318,6 +335,7 @@ def main(): t0 = time() get_clipped_reads(ibam=args.bam, chr_list=args.chrlist.split(','), + minMAPQ=args.min_mapq, outFile=output_file) logging.info('Time: clipped reads on BAM %s: %f' % (args.bam, (time() - t0))) diff --git a/scripts/genome_wide/coverage.py b/scripts/genome_wide/coverage.py index 27feae16..34b32efa 100644 --- a/scripts/genome_wide/coverage.py +++ b/scripts/genome_wide/coverage.py @@ -1,53 +1,24 @@ import argparse import logging import os -from time import time -from zlib import crc32 - import numpy as np import pysam +from time import time -from functions import get_config_file - -config = get_config_file() -minMAPQ = config["DEFAULT"]["MIN_MAPQ"] - - -def check_read(read): - ''' - - :param read: AlignedSegment - :return: True if all these conditions are valid: - - read and mate are mapped on the same chromosome, - - read mapping quality is greater than minMAPQ, - - read and mate are mapped on opposite strands - ''' - - if read.reference_name == read.next_reference_name and read.mapping_quality >= minMAPQ \ - and read.is_reverse != read.mate_is_reverse: - return True - - return False +from functions import get_config_file, get_insert_size -def check_read_is_proper_paired_forward(read): - ''' +def is_discordant(read, bam_mean, bam_stddev): - :param read: AlignedSegment - :return: True if all these conditions are valid: - - read and mate are mapped on the same chromosome, - - read mapping quality is greater than minMAPQ, - - read and mate are mapped on opposite strands - ''' + dist = read.next_reference_start - read.reference_start - if not read.is_unmapped and not read.mate_is_unmapped and read.mapping_quality >= minMAPQ \ - and not read.is_proper_pair and not read.is_reverse: + if dist > 0 and abs(dist) <= bam_mean + bam_stddev: + return False + else: return True - return False - -def check_read_is_proper_paired_reverse(read): +def is_properly_mapped(read): ''' :param read: AlignedSegment @@ -57,14 +28,14 @@ def check_read_is_proper_paired_reverse(read): - read and mate are mapped on opposite strands ''' - if not read.is_unmapped and not read.mate_is_unmapped and read.mapping_quality >= minMAPQ \ - and not read.is_proper_pair and read.is_reverse: + if read.reference_name == read.next_reference_name \ + and read.is_reverse != read.mate_is_reverse: return True return False -def get_coverage(ibam, chrName, outFile): +def get_coverage(ibam, chrName, minMAPQ, outFile): ''' This function fills the coverage array for the chromosome :param ibam: input BAM alignment file @@ -75,6 +46,9 @@ def get_coverage(ibam, chrName, outFile): # Open BAM file bamfile = pysam.AlignmentFile(ibam, "rb") + + bam_mean, bam_stddev = get_insert_size(ibam, bamfile, minMAPQ) + # Get chromosome length from BAM header header_dict = bamfile.header chrLen = [i['LN'] for i in header_dict['SQ'] if i['SN'] == chrName][0] @@ -83,13 +57,10 @@ def get_coverage(ibam, chrName, outFile): stop_pos = chrLen # Numpy array to store the coverage - cov = np.zeros((5, chrLen), dtype=np.uint32) - - read_quality_sum = np.zeros(chrLen, dtype=np.uint32) - read_quality_count = np.zeros(chrLen, dtype=np.uint32) + cov = np.zeros((chrLen, 5)) # Log information every n_r base pair positions - n_r = 10**6 + n_r = 10 ** 6 # print(n_r) last_t = time() # print(type(last_t)) @@ -106,92 +77,37 @@ def get_coverage(ibam, chrName, outFile): # print(type(now_t)) logging.info("%d alignments processed (%f alignments / s)" % (i, n_r / (now_t - last_t))) - last_t = time() - if check_read(read): - cov[0, read.reference_start:read.reference_end - 1] += 1 - if check_read_is_proper_paired_forward(read): - cov[1, read.reference_start:read.reference_end - 1] += 1 - if check_read_is_proper_paired_reverse(read): - cov[2, read.reference_start:read.reference_end - 1] += 1 + last_t = time() if not read.is_unmapped and read.mapping_quality >= minMAPQ: - # add read mapping quality - read_quality_sum[read.reference_start:read.reference_end - - 1] += read.mapping_quality - read_quality_count[read.reference_start:read.reference_end - - 1] += 1 - - # using hash of query name - j = int((read.reference_end-read.reference_start)/2) - #print(j) - h = crc32(read.query_name.encode('utf8')) & 0xffffffff - #print(h) - if not read.is_reverse: - cov[3, j] = h if not cov[3, j] else 0 - else: - cov[4, j] = h if not cov[4, j] else 0 - - # # Iterate over the chromosome positions - # for i, pile in enumerate(bamfile.pileup(chrName, start_pos, stop_pos, truncate=True), start=1): - # - # if not i % n_r: - # now_t = time() - # # print(type(now_t)) - # logging.info("%d pileup positions processed (%f positions / s)" % ( - # i, - # n_r / (now_t - last_t))) - # last_t = time() - # #print('Pos: %d, Cov: %d' % (pile.pos, pile.n)) - # try: - # cov[pile.pos] = pile.n - # except MemoryError: - # logging.info("Out of memory for chr %s and BAM file %s !" % (chrName, ibam)) - - # Replacing pileup with count_coverage - # cov_A, cov_C, cov_G, cov_T = bamfile.count_coverage(chrName, start_pos, stop_pos, read_callback=check_read) - # cov = np.asarray(cov_A, dtype=int) + \ - # np.asarray(cov_C, dtype=int) + \ - # np.asarray(cov_G, dtype=int) + \ - # np.asarray(cov_T, dtype=int) - # - # cov_A, cov_C, cov_G, cov_T = bamfile.count_coverage(chrName, start_pos, stop_pos, - # read_callback=check_read_is_proper_paired_forward) - # cov_disc_f = np.asarray(cov_A, dtype=int) + \ - # np.asarray(cov_C, dtype=int) + \ - # np.asarray(cov_G, dtype=int) + \ - # np.asarray(cov_T, dtype=int) - # - # cov_A, cov_C, cov_G, cov_T = bamfile.count_coverage(chrName, start_pos, stop_pos, - # read_callback=check_read_is_proper_paired_reverse) - # cov_disc_r = np.asarray(cov_A, dtype=int) + \ - # np.asarray(cov_C, dtype=int) + \ - # np.asarray(cov_G, dtype=int) + \ - # np.asarray(cov_T, dtype=int) - # - # cov = np.vstack((cov, cov_disc_f, cov_disc_r)) - - # print(cov) - - # cov_A, cov_C, cov_G, cov_T = bamfile.count_coverage(chrName, start_pos, stop_pos) - # cov_nofilter = np.asarray(cov_A, dtype=int) + \ - # np.asarray(cov_C, dtype=int) + \ - # np.asarray(cov_G, dtype=int) + \ - # np.asarray(cov_T, dtype=int) - # print(cov_nofilter) - # - # assert np.all(cov == cov_nofilter) - - read_quality = np.divide(read_quality_sum, - read_quality_count, - where=read_quality_count != 0) - # where there are no reads, use median mapping quality - read_quality[np.where(read_quality_count == 0)] = np.median(read_quality) - - cov = np.vstack((cov, read_quality)) + if is_properly_mapped(read): + cov[read.reference_start:read.reference_end - 1, 0] += 1 + + read_discordant = is_discordant(read, bam_mean, bam_stddev) + + if not read.mate_is_unmapped: + if read_discordant: + if read.is_reverse: + cov[read.reference_start:read.reference_end - 1, 2] += 1 + else: + cov[read.reference_start:read.reference_end - 1, 1] += 1 + if not read.is_proper_pair: + if read.is_reverse: + cov[read.reference_start:read.reference_end - 1, 4] += 1 + else: + cov[read.reference_start:read.reference_end - 1, 3] += 1 + logging.info(cov.shape) + for i in np.arange(cov.shape[1]): + logging.info('chromosome {} coverage: non-zero elements at index {}:{}'.format(chrName, + i, + np.argwhere( + cov[i, :] != 0).shape[0])) + logging.info('mean:{}, sd:{}'.format(np.mean(cov[:, i]), np.std(cov[:, i]))) + # Save coverage numpy array try: np.save(file=outFile, arr=cov) @@ -205,38 +121,34 @@ def get_coverage(ibam, chrName, outFile): def main(): - - # Default BAM file for testing - # On the HPC - # wd = '/hpc/cog_bioinf/ridder/users/lsantuari/Datasets/DeepSV/artificial_data/run_test_INDEL/samples/T0/BAM/T0/mapping' - # inputBAM = wd + "T0_dedup.bam" - # Locally - wd = '/Users/lsantuari/Documents/Data/HPC/DeepSV/Artificial_data/run_test_INDEL/BAM/' - inputBAM = wd + "T1_dedup.bam" - - # Default chromosome is 17 for the artificial data + # Default chromosome is 12 for the artificial data parser = argparse.ArgumentParser(description='Create coverage channel') parser.add_argument('-b', '--bam', type=str, - default=inputBAM, + default='../../data/test.bam', help="Specify input file (BAM)") parser.add_argument('-c', '--chr', type=str, - default='17', + default='22', help="Specify chromosome") parser.add_argument('-o', '--out', type=str, default='coverage.npy', help="Specify output") + parser.add_argument('-m', + '--min_mapq', + type=int, + default=10, + help='Minimum read mapping quality') parser.add_argument( '-p', '--outputpath', type=str, - default='/Users/lsantuari/Documents/Processed/channel_maker_output', + default='.', help="Specify output path") parser.add_argument('-l', '--logfile', @@ -258,7 +170,9 @@ def main(): level=logging.INFO) t0 = time() - get_coverage(ibam=args.bam, chrName=args.chr, outFile=output_file) + + get_coverage(ibam=args.bam, chrName=args.chr, minMAPQ=args.min_mapq, outFile=output_file) + logging.info('Time: coverage on BAM %s and Chr %s: %f' % (args.bam, args.chr, (time() - t0))) diff --git a/scripts/genome_wide/create_window_pairs.py b/scripts/genome_wide/create_window_pairs.py index b24d4593..d968d631 100644 --- a/scripts/genome_wide/create_window_pairs.py +++ b/scripts/genome_wide/create_window_pairs.py @@ -15,16 +15,6 @@ def get_range(dictionary, begin, end): return dict(itertools.islice(dictionary.items(), begin, end)) -def get_chr_list(chrom): - # if sampleName in ['T1', 'G1', 'ART_INDEL_HET', 'ART_INDEL_HOM']: - # chrlist = ['17'] - # else: - # chrlist = list(map(str, range(1, 23))) - # chrlist.extend(['X']) - # - return [chrom] - - def load_chr_array(channel_data_dir, chrlist): chr_array = dict() @@ -61,28 +51,16 @@ def split_labels(labels): def unfold_win_id(win_id): - chr1, pos1, chr2, pos2 = win_id.split('_') + chr1, pos1, chr2, pos2, strand_info = win_id.split('_') pos1 = int(pos1) pos2 = int(pos2) - return chr1, pos1, chr2, pos2 + return chr1, pos1, chr2, pos2, strand_info -def get_window_by_id(win_id, chr_array, padding, win_hlen): - chr1, pos1, chr2, pos2 = win_id.split('_') - pos1 = int(pos1) - pos2 = int(pos2) +def get_windows(carrays_dir, outDir, chrom_list, win, label_file_path, mode, npz_mode, padding_len): - dask_arrays = list() - dask_arrays.append(chr_array[chr1][pos1 - win_hlen:pos1 + win_hlen, :]) - dask_arrays.append(padding) - dask_arrays.append(chr_array[chr2][pos2 - win_hlen:pos2 + win_hlen, :]) - return da.concatenate(dask_arrays, axis=0) - - -def get_windows(carrays_dir, outDir, chrom_list, win, label_file_path, mode, npz_mode): - def same_chr_in_winid(win_id): - chr1, pos1, chr2, pos2 = win_id.split('_') - return chr1 == chr2 + if win % 2 != 0: + win += 1 chr_array = load_chr_array(carrays_dir, chrom_list) n_channels = chr_array[chrom_list[0]].shape[1] @@ -110,7 +88,6 @@ def same_chr_in_winid(win_id): labels_set = {'test': labels} - padding_len = 10 win_hlen = int(int(win) / 2) for labs_name, labs in labels_set.items(): @@ -122,22 +99,21 @@ def same_chr_in_winid(win_id): last_t = time() i = 1 - outfile = os.path.join(outDir, 'windows') - - bcolz_array = bcolz.carray(bcolz.zeros(shape=(0, int(win) * 2 + - padding_len, n_channels), - dtype=np.float32), - mode='w', - rootdir=outfile + '_carray') + # outfile = os.path.join(outDir, 'windows') + # bcolz_array = bcolz.carray(bcolz.zeros(shape=(0, int(win) * 2 + + # padding_len, n_channels), + # dtype=np.float32), + # mode='w', + # rootdir=outfile + '_carray') - padding = bcolz.zeros(shape=(padding_len, n_channels), + padding = np.zeros(shape=(padding_len, n_channels), dtype=np.float32) if npz_mode: numpy_array = [] logging.info('Creating dask_arrays_win1 and dask_arrays_win2...') - for chr1, pos1, chr2, pos2 in map(unfold_win_id, labs.keys()): + for chr1, pos1, chr2, pos2, strand_info in map(unfold_win_id, labs.keys()): # logging.info("chr1={} pos1={} chr2={} pos2={}".format( # chr1, pos1, chr2, pos2)) if not i % n_r: @@ -149,42 +125,51 @@ def same_chr_in_winid(win_id): (i, n_r / (now_t - last_t))) last_t = time() - dask_array = list() + partial_array = list() d = chr_array[chr1][pos1 - win_hlen:pos1 + win_hlen, :] - dask_array.append(d) - dask_array.append(padding) + partial_array.append(d) + partial_array.append(padding) d = chr_array[chr2][pos2 - win_hlen:pos2 + win_hlen, :] - dask_array.append(d) + partial_array.append(d) try: - - dask_array = np.concatenate(dask_array, axis=0) + # print(len(partial_array)) + full_array = np.concatenate(partial_array, axis=0) if npz_mode: - numpy_array.append(dask_array) + numpy_array.append(full_array) except ValueError: print('{}:{}-{}:{}'.format(chr1, pos1, chr2, pos2)) - for d in dask_array: - print(d.shape) + for d in numpy_array: + print(d.shape) # print(type(dask_array)) - bcolz_array.append(dask_array) - i += 1 + # bcolz_array.append(dask_array) # bcolz_array.append(dask_array) - bcolz_array.attrs['labels'] = labs - bcolz_array.flush() - logging.info(bcolz_array.shape) + # bcolz_array.attrs['labels'] = labs + # bcolz_array.flush() + # logging.info(bcolz_array.shape) if npz_mode: + numpy_array = np.stack(numpy_array, axis=0) + logging.info('Numpy array shape: {}'.format(numpy_array.shape)) + + for i in np.arange(numpy_array.shape[2]): + logging.info('windows array:' + \ + 'non-zero elements at index {}:{}'.format(i, + np.argwhere(numpy_array[i, :] != 0).shape[0])) + np.savez(file=os.path.join(outDir, 'windows'), data=numpy_array, labels=labs) + else: + logging.info('Enable bcolz!') def main(): @@ -193,36 +178,28 @@ def main(): :return: None ''' - # Default BAM file for testing - # On the HPC - # wd = '/hpc/cog_bioinf/ridder/users/lsantuari/Datasets/DeepSV/'+ - # 'artificial_data/run_test_INDEL/samples/T0/BAM/T0/mapping' - # inputBAM = wd + "T0_dedup.bam" - # Locally - wd = '/Users/lsantuari/Documents/Data/HPC/DeepSV/Artificial_data/run_test_INDEL/BAM/' - inputBAM = wd + "T1_dedup.bam" parser = argparse.ArgumentParser( description='Create windows from chromosome arrays') parser.add_argument('-b', '--bam', type=str, - default=inputBAM, + default='../../data/test.bam', help="Specify input file (BAM)") parser.add_argument('-c', '--chrlist', type=str, - default='17', + default='12,22', help="List of chromosomes to consider") parser.add_argument('-ca', '--carraydir', type=str, - default='', + default='.', help="chr_array directory") parser.add_argument('-p', '--outputpath', type=str, - default='', + default='./cnn/win25/split_reads/windows/DEL', help="Specify output path") parser.add_argument('-l', '--logfile', @@ -230,13 +207,13 @@ def main(): help='File in which to write logs.') parser.add_argument('-w', '--window', - type=str, - default=200, + type=int, + default=25, help="Specify window size") parser.add_argument('-lb', '--labels', type=str, - default='labels.json.gz', + default='./cnn/win25/split_reads/windows/DEL/labels.json.gz', help="Specify label file") parser.add_argument('-m', '--mode', @@ -248,11 +225,15 @@ def main(): type=bool, default=True, help="save in npz format?") + parser.add_argument('-pd', + '--padding', + type=int, + default=10, + help="Length of the padding in between windows") args = parser.parse_args() - cmd_name = 'windows' - output_dir = os.path.join(args.outputpath, cmd_name) + output_dir = args.outputpath os.makedirs(output_dir, exist_ok=True) logfilename = os.path.join(output_dir, args.logfile) # output_file = os.path.join(output_dir, args.out) @@ -271,7 +252,8 @@ def main(): win=args.window, label_file_path=args.labels, mode=args.mode, - npz_mode=args.save_npz) + npz_mode=args.save_npz, + padding_len=args.padding) # print('Elapsed time channel_maker_real on BAM %s and Chr %s = %f' % (args.bam, args.chr, time() - t0)) logging.info('Elapsed time create_windows = %f seconds' % (time() - t0)) diff --git a/scripts/genome_wide/environment.yaml b/scripts/genome_wide/environment.yaml deleted file mode 100644 index 1e7395ff..00000000 --- a/scripts/genome_wide/environment.yaml +++ /dev/null @@ -1,14 +0,0 @@ -channels: - - conda-forge - - bioconda - -dependencies: - - numpy - - bcolz - - intervaltree - - pysam - - pybigwig - - twobitreader - - ucsc-fatotwobit - - ucsc-twobittofa - - ucsc-twobitinfo diff --git a/scripts/genome_wide/functions.py b/scripts/genome_wide/functions.py index 5022d375..b03831dc 100644 --- a/scripts/genome_wide/functions.py +++ b/scripts/genome_wide/functions.py @@ -7,6 +7,9 @@ import pysam import twobitreader as twobit from cigar import Cigar +from statistics import mean, stdev +import pandas as pd +# import matplotlib.pyplot as plt del_min_size = 50 ins_min_size = 50 @@ -456,8 +459,8 @@ def get_filepath(vec_type): if clipped_type == 'SR': locations_sr[chrom] = [ - (chr1, pos1, chr2, pos2) - for chr1, pos1, chr2, pos2 in total_reads_coord_min_support + (chr1, pos1, chr2, pos2, strand_info) + for chr1, pos1, chr2, pos2, strand_info in total_reads_coord_min_support if chr1 in chr_dict.keys() and chr2 in chr_dict.keys() and chr1 == chrom and win_hlen <= pos1 <= (chr_dict[chr1] - win_hlen) and win_hlen <= pos2 <= (chr_dict[chr2] - win_hlen) @@ -567,6 +570,7 @@ def get_filepath(vec_type): return cpos_list_right, cpos_list_left + def load_windows(win_file): npzfile = np.load(win_file, allow_pickle=True, mmap_mode='r') @@ -582,17 +586,77 @@ def save_windows(X, y, win_file): data=X, labels=y) -def chr_dict_from_bed(input_bed): - # Check file existence - assert os.path.isfile(input_bed), input_bed + ' not found!' - # Dictionary. Keys: chromosome name, values: chromosome lengths - d = {} +def get_chr_dict(fasta_file): + + d = dict() + + with pysam.FastaFile(filename=fasta_file, filepath_index=fasta_file + '.fai') as fa: + for i,seqid in enumerate(fa.references): + d[seqid] = fa.lengths[i] - 1 + return d + + +def estimate_insert_size(ibam, pysam_bam, min_mapq): + + base =os.path.basename(ibam) + prefix = os.path.splitext(base)[0] + + isize_out = os.path.join( + os.path.dirname(ibam), + prefix+'.insert_size.csv' + ) + # print(isize_out) + + # n_bins = 100 + + # fig, axs = plt.subplots(1, 1, sharey=True, tight_layout=True) + + isize_distr = [] + i = 0 + + for read in pysam_bam.fetch(): + if (not read.is_unmapped) and read.mapping_quality >= min_mapq \ + and read.is_reverse != read.mate_is_reverse \ + and read.reference_name == read.next_reference_name: + dist = abs(read.reference_start - read.next_reference_start) + + if dist < 10 ** 3: - with (open(input_bed, 'r')) as bed: - for line in bed: - columns = line.rstrip().split("\t") - d[columns[0]] = int(columns[2]) - int(columns[1]) + isize_distr.append( + dist + ) + + if i == 2 * 10 ** 6: + break + i += 1 + + df = pd.DataFrame({'mean': [mean(isize_distr)], + 'sd': [stdev(isize_distr)] + }) + + df.to_csv(isize_out, index=False) + + # axs.hist(isize_distr, bins=n_bins) + # plt.show() + + return df + + +def get_insert_size(ibam, pysam_bam, min_mapq): + + base =os.path.basename(ibam) + prefix = os.path.splitext(base)[0] + + isize_file = os.path.join( + os.path.dirname(ibam), + prefix+'.insert_size.csv' + ) + + if os.path.exists(isize_file): + df = pd.read_csv(isize_file) + else: + df = estimate_insert_size(ibam, pysam_bam, min_mapq) - return d + return df.at[0, 'mean'], df.at[0, 'sd'] diff --git a/scripts/genome_wide/label_classes.py b/scripts/genome_wide/label_classes.py index 80941db8..9913df07 100644 --- a/scripts/genome_wide/label_classes.py +++ b/scripts/genome_wide/label_classes.py @@ -7,6 +7,18 @@ __bpRE__ = None __symbolicRE__ = None +def setupREs(): + ''' + Function of the mergevcf tool by Jonathan Dursi (Simpson Lab) + URL: https://github.com/ljdursi/mergevcf + ''' + global __symbolicRE__ + global __bpRE__ + if __symbolicRE__ is None or __bpRE__ is None: + __symbolicRE__ = re.compile(r'.*<([A-Z:]+)>.*') + __bpRE__ = re.compile( + r'([ACGTNactgn\.]*)([\[\]])([a-zA-Z0-9\.\_]+:\d+)([\[\]])([ACGTNacgtn\.]*)' + ) class SVRecord: def __init__(self, record, svcaller): @@ -161,18 +173,10 @@ def get_bnd_info(self, record): ct, chr2, pos2, indellen = self.locFromBkpt( str(record.ref), resultBP.group(1), resultBP.group(2), resultBP.group(3), resultBP.group(4), resultBP.group(5)) - return (ct, chr2, pos2, indellen) + res = (ct, chr2, pos2, indellen) -def setupREs(): - ''' - Function of the mergevcf tool by Jonathan Dursi (Simpson Lab) - URL: https://github.com/ljdursi/mergevcf - ''' - global __symbolicRE__ - global __bpRE__ - if __symbolicRE__ is None or __bpRE__ is None: - __symbolicRE__ = re.compile(r'.*<([A-Z:]+)>.*') - __bpRE__ = re.compile( - r'([ACGTNactgn\.]*)([\[\]])([a-zA-Z0-9\.]+:\d+)([\[\]])([ACGTNacgtn\.]*)' - ) + else: + res = None + + return res diff --git a/scripts/genome_wide/label_window_pairs_on_split_read_positions.py b/scripts/genome_wide/label_window_pairs_on_split_read_positions.py deleted file mode 100644 index 1c785779..00000000 --- a/scripts/genome_wide/label_window_pairs_on_split_read_positions.py +++ /dev/null @@ -1,496 +0,0 @@ -# Imports -import argparse -import gzip -import json -import logging -import os -from collections import Counter, defaultdict -from time import time - -from intervaltree import IntervalTree -from pysam import VariantFile - -from functions import * -from label_classes import SVRecord - - -def read_vcf(invcf): - # Check file existence - assert os.path.isfile(invcf), invcf + ' not found!' - # Dictionary with chromosome keys to store SVs - sv_list = [] - - vcf_in = VariantFile(invcf, 'r') - for rec in vcf_in.fetch(): - - var = SVRecord(rec, 'gridss') - - chrom1 = var.chrom - pos1_start = var.start + var.cipos[0] - pos1_end = var.start + var.cipos[1] + 1 - - chrom2 = var.chrom2 - pos2_start = var.end + var.ciend[0] - pos2_end = var.end + var.ciend[1] + 1 - svtype = var.svtype - - if svtype == "DEL": - sv_list.append((chrom1, pos1_start, pos1_end, chrom2, pos2_start, - pos2_end, svtype)) - - logging.info('{} SVs'.format(len(sv_list))) - - return sv_list - - -def read_bedpe(inbedpe, svtype_to_select): - # Check file existence - assert os.path.isfile(inbedpe), inbedpe + ' not found!' - # Dictionary with chromosome keys to store SVs - sv_list = [] - - with (open(inbedpe, 'r')) as bed: - for line in bed: - columns = line.rstrip().split("\t") - chrom1, pos1_start, pos1_end = str(columns[0]), int( - columns[1]), int(columns[2]) - chrom2, pos2_start, pos2_end = str(columns[3]), int( - columns[4]), int(columns[5]) - svtype = columns[-1] - - if svtype == "TYPE:DELETION": - svtype = "DEL" - - if svtype_to_select == svtype: - if svtype in ['DEL', 'INV', 'DUP', 'TRA']: - sv_list.append((chrom1, pos1_start, pos1_end, chrom2, - pos2_start, pos2_end, svtype)) - elif svtype == "INS": - sv_list.append((chrom1, pos1_start, pos1_end, chrom1, - pos1_start + 1, pos1_end + 1, svtype)) - - logging.info('{} SVs'.format(len(sv_list))) - - return sv_list - - -def filter_bedpe(inbedpe, sv_id_list, outDir): - # Check file existence - assert os.path.isfile(inbedpe), inbedpe + ' not found!' - # Dictionary with chromosome keys to store SVs - logging.info('{} SVs to filter out'.format(len(sv_id_list))) - lines_to_keep = [] - - with (open(inbedpe, 'r')) as bed: - for line in bed: - columns = line.rstrip().split("\t") - chrom1, pos1_start, pos1_end = str(columns[0]), int( - columns[1]), int(columns[2]) - chrom2, pos2_start, pos2_end = str(columns[3]), int( - columns[4]), int(columns[5]) - svtype = columns[-1] - svtype = "DEL" if svtype == "TYPE:DELETION" else svtype - - sv_id = '_'.join( - (svtype, chrom1, str(pos1_start), chrom2, str(pos2_start))) - - if svtype in ['DEL', 'INS', 'INV', 'DUP', 'TRA'] and sv_id not in sv_id_list: - lines_to_keep.append(line) - - fileout = os.path.join(outDir, 'uncaptured_SVs.bedpe') - logging.info('Writing {}'.format(fileout)) - - with (open(fileout, 'w')) as fout: - for line in lines_to_keep: - fout.write(line) - - logging.info('{} SVs written'.format(len(lines_to_keep))) - - -def read_survivor_simsv_output(insur, svtype): - # Check file existence - assert os.path.isfile(insur), insur + ' not found!' - # Dictionary with chromosome keys to store SVs - sv_list = [] - - with (open(insur, 'r')) as bed: - for line in bed: - - columns = line.rstrip().split("\t") - chrom1, pos1_start, pos1_end = str( - columns[0]), int(columns[1]) - 1, int(columns[1]) - chrom2, pos2_start, pos2_end = str( - columns[2]), int(columns[3]) - 1, int(columns[3]) - type = columns[4] - - if type == svtype: - - if type == 'DEL': - sv_list.append((chrom1, pos1_start, pos1_end, chrom2, - pos2_start, pos2_end, svtype)) - - elif type == 'INS': - sv_list.append((chrom1, pos1_start, pos1_end, chrom1, - pos1_start, pos1_end, svtype)) - - logging.info('{} SVs'.format(len(sv_list))) - - return sv_list - - -def filter_survivor_output(insur, sv_id_list, outDir): - # Check file existence - assert os.path.isfile(insur), insur + ' not found!' - - logging.info('{} SVs to filter out'.format(len(sv_id_list))) - lines_to_keep = [] - - with (open(insur, 'r')) as bed: - for line in bed: - columns = line.rstrip().split("\t") - chrom1, pos1_start, pos1_end = str( - columns[0]), int(columns[1]) - 1, int(columns[1]) - chrom2, pos2_start, pos2_end = str( - columns[2]), int(columns[3]) - 1, int(columns[3]) - svtype = columns[4] - - sv_id = '_'.join( - (svtype, chrom1, str(pos1_start), chrom2, str(pos2_start))) - - if svtype == "DEL" and sv_id not in sv_id_list: - lines_to_keep.append(line) - - fileout = os.path.join(outDir, 'uncaptured_SVs.sur') - logging.info('Writing {}'.format(fileout)) - - with (open(fileout, 'w')) as fout: - for line in lines_to_keep: - fout.write(line) - - logging.info('{} SVs written'.format(len(lines_to_keep))) - - -def overlap(svtype, sv_list, cpos_list, win_hlen, ground_truth, outDir): - ''' - - :param sv_list: list, list of SVs - :param cr_pos: list, list of clipped read positions - :return: list, list of clipped read positions whose window completely overlap either the CIPOS interval - or the CIEND interval - ''' - - def make_gtrees_from_svlist(sv_list): - - logging.info('Building SV GenomicTrees...') - # Tree with windows for candidate positions - trees_start = defaultdict(IntervalTree) - trees_end = defaultdict(IntervalTree) - - # Populate tree - for sv in sv_list: - # print(sv) - - chrom1, pos1_start, pos1_end, chrom2, pos2_start, pos2_end, svtype = sv - sv_id = '_'.join( - (svtype, chrom1, str(pos1_start), chrom2, str(pos2_start))) - # print(sv_id) - - trees_start[chrom1][pos1_start:pos1_end] = (svtype, sv_id) - trees_end[chrom2][pos2_start:pos2_end] = (svtype, sv_id) - - # print('Tree start') - # for k in trees_start.keys(): - # print('{} : {}'.format( k, len(trees_start[k]))) - # print('Tree end') - # for k in trees_end.keys(): - # print('{} : {}'.format( k, len(trees_end[k]))) - - return trees_start, trees_end - - def search_tree_with_cpos(cpos, trees_start, trees_end): - - lookup_region = 10 - - logging.info('Searching SV GenomicTrees with candidate positions...') - - lookup_start = [] - lookup_end = [] - - # Log info every n_r times - n_r = 10 ** 6 - last_t = time() - - for i, p in enumerate(cpos, start=1): - - if not i % n_r: - now_t = time() - # print(type(now_t)) - logging.info( - "%d candidate positions processed (%f positions / s)" % - (i, n_r / (now_t - last_t))) - last_t = time() - - chrom1, pos1, chrom2, pos2 = p - - lookup_start.append(trees_start[chrom1][pos1 - lookup_region:pos1 + - lookup_region + 1]) - lookup_end.append(trees_end[chrom2][pos2 - lookup_region:pos2 + - lookup_region + 1]) - - return lookup_start, lookup_end - - trees_start, trees_end = make_gtrees_from_svlist(sv_list) - - lookup_start, lookup_end = search_tree_with_cpos(cpos_list, trees_start, - trees_end) - - # print([l for l in lookup_start if len(l) > 0]) - # print([l for l in lookup_end if len(l) > 0]) - - labels = dict() - - sv_covered = set() - - for p, lu_start, lu_end in zip(cpos_list, lookup_start, lookup_end): - - chrom1, pos1, chrom2, pos2 = p - pos_id = '_'.join((chrom1, str(pos1), chrom2, str(pos2))) - - l1 = len(lu_start) - l2 = len(lu_end) - - if l1 == 1 and l1 == l2: - - lu_start_elem_start, lu_start_elem_end, lu_start_elem_data = lu_start.pop( - ) - lu_end_elem_start, lu_end_elem_end, lu_end_elem_data = lu_end.pop() - - lu_start_elem_svtype, lu_start_elem_svid = lu_start_elem_data - lu_end_elem_svtype, lu_end_elem_svid = lu_end_elem_data - - # if lu_start_elem_svtype != 'TRA': - - if pos1 - win_hlen <= lu_start_elem_start and lu_start_elem_end <= pos1 + win_hlen and \ - pos2 - win_hlen <= lu_end_elem_start and lu_end_elem_end <= pos2 + win_hlen and \ - lu_start_elem_svid == lu_end_elem_svid: - # logging.info( - # 'Chr1:{}\tpos1:{}-{}\tChr2:{}\tpos2:{}-{}'.format( - # chrom1, pos1 - win_hlen, pos1 + win_hlen, chrom2, pos2 - win_hlen, pos2 + win_hlen - # ) - # ) - # logging.info( - # 'LookUp_start:{}-{}_{}\tLookUp_end:{}-{}_{}'.format( - # lu_start_elem_start, lu_start_elem_end, lu_start_elem_data, - # lu_end_elem_start, lu_end_elem_end, lu_end_elem_data - # ) - # ) - # if pos1 in np.arange(lu_start_elem_start-2, lu_start_elem_end+2) and \ - # pos2 in np.arange(lu_end_elem_start-2, lu_end_elem_end+2): - sv_covered.add(lu_start_elem_svid) - labels[pos_id] = lu_start_elem_svtype - # else: - # sv_covered.add(lu_start_elem_svid) - # labels[pos_id] = 'UK_overlap_not_matching' - else: - labels[pos_id] = 'UK_single_partial' - - # else: - # - # sv_covered.add(lu_start_elem_svid) - # labels[pos_id] = lu_start_elem_svtype - - elif l1 > 1 or l2 > 1: - - lu_start_set = set() - lu_end_set = set() - - for s in lu_start: - lu_start_elem_start, lu_start_elem_end, lu_start_elem_data = s - lu_start_elem_svtype, lu_start_elem_svid = lu_start_elem_data - lu_start_set.add(lu_start_elem_svid) - - for s in lu_end: - lu_end_elem_start, lu_end_elem_end, lu_end_elem_data = s - lu_end_elem_svtype, lu_end_elem_svid = lu_end_elem_data - lu_end_set.add(lu_end_elem_svid) - - sv_covered = sv_covered | (lu_start_set & lu_end_set) - - # if svtype in ['DEL', 'INV', 'DUP', 'TRA']: - # labels[pos_id] = 'UK_multiple_on_either_windows' - # elif svtype == 'INS': - # labels[pos_id] = svtype - - labels[pos_id] = svtype - - elif l1 == 0 and l1 == l2: - # logging.info('CPOS->Partial: %s\t%d\t%d' % (elem, start, end)) - labels[pos_id] = 'no' + svtype - - elif (l1 == 1 and l2 > 1) or (l2 == 1 and l1 > 1) or (l2 > 1 and l1 > 1): - - lu_start_set = set() - lu_end_set = set() - - for s in lu_start: - lu_start_elem_start, lu_start_elem_end, lu_start_elem_data = s - lu_start_elem_svtype, lu_start_elem_svid = lu_start_elem_data - lu_start_set.add(lu_start_elem_svid) - - for s in lu_end: - lu_end_elem_start, lu_end_elem_end, lu_end_elem_data = s - lu_end_elem_svtype, lu_end_elem_svid = lu_end_elem_data - lu_end_set.add(lu_end_elem_svid) - - sv_covered = sv_covered | (lu_start_set & lu_end_set) - # labels[pos_id] = 'UK_single_and_multiple' - labels[pos_id] = svtype - - else: - # (l1 == 1 and l2 == 0) or (l1 == 0 and l2 == 1) - labels[pos_id] = 'no' + svtype - - logging.info(Counter(labels.values())) - sv_coverage = int(len(sv_covered) / len(sv_list) * 100) - logging.info('SV coverage: {}/{}={}%'.format(len(sv_covered), len(sv_list), - sv_coverage)) - - filename, file_extension = os.path.splitext(ground_truth) - - if file_extension == '.bedpe': - # print(sv_covered) - filter_bedpe(ground_truth, sv_covered, outDir) - elif file_extension == '.sur': - filter_survivor_output(ground_truth, sv_covered, outDir) - - return labels - - -# Get labels -def get_labels(ibam, chrlist, win_len, svtype, ground_truth, channelDataDir, - outFile, outDir): - # windows half length - win_hlen = int(int(win_len) / 2) - # get chromosome lengths - chr_dict = get_chr_len_dict(ibam) - - cpos_list = load_all_clipped_read_positions(win_hlen, svtype, chr_dict, - channelDataDir) - - # Keep only positions that can be used to create windows - chr_len_dict = get_chr_len_dict(ibam) - - cpos_list = [ - (chrom1, pos1, chrom2, pos2) - for chrom1, pos1, chrom2, pos2 in cpos_list if chrom1 in chrlist - and chrom2 in chrlist and win_hlen <= pos1 <= chr_len_dict[ - chrom1] - - win_hlen and win_hlen <= pos2 <= chr_len_dict[chrom2] - win_hlen - ] - - filename, file_extension = os.path.splitext(ground_truth) - if file_extension == '.bedpe': - sv_list = read_bedpe(ground_truth, svtype) - elif file_extension == '.sur': - sv_list = read_survivor_simsv_output(ground_truth, svtype) - elif file_extension == '.vcf' or file_extension == '.gz': - sv_list = read_vcf(ground_truth) - - # Get overlap of candidate positions with all SV breakpoints (all 4 SV callers) - # crpos_all_sv = get_crpos_overlap_with_sv_callsets(sv_dict, cr_pos_dict) - - # filename, file_extension = os.path.splitext(ground_truth) - # trees_start, trees_end = make_gtrees_from_truth_set(sv_list, file_extension.upper()) - - labels = overlap(svtype, sv_list, cpos_list, win_hlen, ground_truth, - outDir) - - with gzip.GzipFile(outFile, 'wb') as fout: - fout.write(json.dumps(labels).encode('utf-8')) - - -def main(): - ''' - Label windows according to truth set - :return: None - ''' - - parser = argparse.ArgumentParser(description='Create labels') - parser.add_argument('-b', - '--bam', - type=str, - default='../../data/test.bam', - help="Specify input file (BAM)") - parser.add_argument('-c', - '--chrlist', - type=str, - default='12,22', - help="Comma separated list of chromosomes to consider") - parser.add_argument('-l', - '--logfile', - type=str, - default='labels.log', - help="Specify log file") - # parser.add_argument('-s', '--sample', type=str, default='NA24385', - # help="Specify sample") - parser.add_argument('-w', - '--window', - type=str, - default=500, - help="Specify window size") - parser.add_argument('-s', - '--svtype', - type=str, - default='TRA', - help="Specify SV type") - parser.add_argument('-gt', - '--ground_truth', - type=str, - default='../../data/test.bedpe', - help="Specify ground truth VCF/BEDPE file") - parser.add_argument('-o', - '--out', - type=str, - default='./labels/win500/TRA/split_reads/labels.json.gz', - help="Specify output") - parser.add_argument('-p', - '--outputpath', - type=str, - default='.', - help="Specify output path") - - args = parser.parse_args() - - # Log file - output_dir = os.path.join(args.outputpath, 'labels', - 'win' + str(args.window), - args.svtype, - 'split_reads') - os.makedirs(output_dir, exist_ok=True) - logfilename = os.path.join(output_dir, args.logfile) - output_file = os.path.join(output_dir, args.out) - - FORMAT = '%(asctime)s %(message)s' - logging.basicConfig(format=FORMAT, - filename=logfilename, - filemode='w', - level=logging.INFO) - - t0 = time() - - get_labels( - ibam=args.bam, - chrlist=args.chrlist.split(','), - win_len=args.window, - svtype=args.svtype, - ground_truth=args.ground_truth, - channelDataDir=args.outputpath, - outFile=output_file, - outDir=output_dir, - ) - - logging.info('Elapsed time making labels = %f' % (time() - t0)) - - -if __name__ == '__main__': - main() diff --git a/scripts/genome_wide/label_windows.py b/scripts/genome_wide/label_windows.py index 33bdb482..980916a8 100644 --- a/scripts/genome_wide/label_windows.py +++ b/scripts/genome_wide/label_windows.py @@ -14,11 +14,6 @@ from functions import * from label_classes import SVRecord -with open('parameters.json', 'r') as f: - config = json.load(f) - -HPC_MODE = config["DEFAULT"]["HPC_MODE"] - def read_vcf(invcf): @@ -41,9 +36,8 @@ def read_vcf(invcf): pos2_end = var.end + var.ciend[1] + 1 svtype = var.svtype - if svtype == "DEL": - sv_list.append((chrom1, pos1_start, pos1_end, chrom2, pos2_start, - pos2_end, svtype)) + sv_list.append((chrom1, pos1_start, pos1_end, chrom2, pos2_start, + pos2_end, svtype)) logging.info('{} SVs'.format(len(sv_list))) @@ -69,7 +63,7 @@ def read_bedpe(inbedpe, svtype_to_select): svtype = "DEL" if svtype_to_select == svtype: - if svtype in ['DEL', 'INV', 'DUP', 'TRA']: + if svtype in ['DEL', 'INV', 'DUP', 'CTX']: sv_list.append((chrom1, pos1_start, pos1_end, chrom2, pos2_start, pos2_end, svtype)) elif svtype == "INS": @@ -102,7 +96,7 @@ def filter_bedpe(inbedpe, sv_id_list, outDir): sv_id = '_'.join( (svtype, chrom1, str(pos1_start), chrom2, str(pos2_start))) - if svtype in ['DEL', 'INS', 'INV', 'DUP', 'TRA'] and sv_id not in sv_id_list: + if svtype in ['DEL', 'INS', 'INV', 'DUP', 'CTX'] and sv_id not in sv_id_list: lines_to_keep.append(line) fileout = os.path.join(outDir, 'uncaptured_SVs.bedpe') @@ -130,78 +124,81 @@ def read_svcaller_bedpe(inbedpe): chrom2, pos2_start, pos2_end = str(columns[3]), int( columns[4]), int(columns[5]) - cr_pos.append((chrom1, pos1_start, chrom2, pos2_start)) + cr_pos.append((chrom1, pos1_start, chrom2, pos2_start, '**')) logging.info('{} candidate positions'.format(len(cr_pos))) return cr_pos -def overlap(svtype, sv_list, cpos_list, win_hlen, ground_truth, outDir): - ''' +def make_gtrees_from_svlist(sv_list): - :param sv_list: list, list of SVs - :param cr_pos: list, list of clipped read positions - :return: list, list of clipped read positions whose window completely overlap either the CIPOS interval - or the CIEND interval - ''' - def make_gtrees_from_svlist(sv_list): + logging.info('Building SV GenomicTrees...') + # Tree with windows for candidate positions + trees_start = defaultdict(IntervalTree) + trees_end = defaultdict(IntervalTree) - logging.info('Building SV GenomicTrees...') - # Tree with windows for candidate positions - trees_start = defaultdict(IntervalTree) - trees_end = defaultdict(IntervalTree) + # Populate tree + for sv in sv_list: + chrom1, pos1_start, pos1_end, chrom2, pos2_start, pos2_end, svtype = sv + sv_id = '_'.join( + (svtype, chrom1, str(pos1_start), chrom2, str(pos2_start))) - # Populate tree - for sv in sv_list: - chrom1, pos1_start, pos1_end, chrom2, pos2_start, pos2_end, svtype = sv - sv_id = '_'.join( - (svtype, chrom1, str(pos1_start), chrom2, str(pos2_start))) + trees_start[chrom1][pos1_start:pos1_end] = (svtype, sv_id) + trees_end[chrom2][pos2_start:pos2_end] = (svtype, sv_id) - trees_start[chrom1][pos1_start:pos1_end] = (svtype, sv_id) - trees_end[chrom2][pos2_start:pos2_end] = (svtype, sv_id) + # print('Tree start') + # for k in trees_start.keys(): + # print('{} : {}'.format( k, len(trees_start[k]))) + # print('Tree end') + # for k in trees_end.keys(): + # print('{} : {}'.format( k, len(trees_end[k]))) - # print('Tree start') - # for k in trees_start.keys(): - # print('{} : {}'.format( k, len(trees_start[k]))) - # print('Tree end') - # for k in trees_end.keys(): - # print('{} : {}'.format( k, len(trees_end[k]))) + return trees_start, trees_end - return trees_start, trees_end - def search_tree_with_cpos(cpos, trees_start, trees_end): +def search_tree_with_cpos(cpos, trees_start, trees_end, win_hlen): - logging.info('Searching SV GenomicTrees with candidate positions...') + logging.info('Searching SV GenomicTrees with candidate positions...') - lookup_start = [] - lookup_end = [] + lookup_start = [] + lookup_end = [] - # Log info every n_r times - n_r = 10**6 - last_t = time() + # Log info every n_r times + n_r = 10**6 + last_t = time() - for i, p in enumerate(cpos, start=1): + for i, p in enumerate(cpos, start=1): - if not i % n_r: - now_t = time() - # print(type(now_t)) - logging.info( - "%d candidate positions processed (%f positions / s)" % - (i, n_r / (now_t - last_t))) - last_t = time() + if not i % n_r: + now_t = time() + # print(type(now_t)) + logging.info( + "%d candidate positions processed (%f positions / s)" % + (i, n_r / (now_t - last_t))) + last_t = time() - chrom1, pos1, chrom2, pos2 = p - lookup_start.append(trees_start[chrom1][pos1 - win_hlen:pos1 + - win_hlen + 1]) - lookup_end.append(trees_end[chrom2][pos2 - win_hlen:pos2 + - win_hlen + 1]) + chrom1, pos1, chrom2, pos2, strand_info = p + lookup_start.append(trees_start[chrom1].envelop(pos1 - win_hlen, + pos1 + win_hlen + 1)) + lookup_end.append(trees_end[chrom2].envelop(pos2 - win_hlen, + pos2 + win_hlen + 1)) - return lookup_start, lookup_end + return lookup_start, lookup_end + + +def overlap(svtype, sv_list, cpos_list, win_hlen, ground_truth, outDir): + ''' + + :param sv_list: list, list of SVs + :param cr_pos: list, list of clipped read positions + :return: list, list of clipped read positions whose window completely overlap either the CIPOS interval + or the CIEND interval + ''' trees_start, trees_end = make_gtrees_from_svlist(sv_list) lookup_start, lookup_end = search_tree_with_cpos(cpos_list, trees_start, - trees_end) + trees_end, win_hlen) # print([l for l in lookup_start if len(l) > 0]) # print([l for l in lookup_end if len(l) > 0]) @@ -212,8 +209,8 @@ def search_tree_with_cpos(cpos, trees_start, trees_end): for p, lu_start, lu_end in zip(cpos_list, lookup_start, lookup_end): - chrom1, pos1, chrom2, pos2 = p - pos_id = '_'.join((chrom1, str(pos1), chrom2, str(pos2))) + chrom1, pos1, chrom2, pos2, strand_info = p + pos_id = '_'.join((chrom1, str(pos1), chrom2, str(pos2), strand_info)) l1 = len(lu_start) l2 = len(lu_end) @@ -253,7 +250,7 @@ def search_tree_with_cpos(cpos, trees_start, trees_end): # sv_covered.add(lu_start_elem_svid) # labels[pos_id] = 'UK_overlap_not_matching' else: - labels[pos_id] = 'UK_single_partial' + labels[pos_id] = 'no'+svtype # else: # @@ -334,7 +331,7 @@ def get_labels(chrlist, chr_dict, win_len, svtype, ground_truth, sv_positions, # windows half length win_hlen = int(int(win_len) / 2) - sv_caller_file = os.path.join(channelDataDir, sv_positions + '.bedpe') + sv_caller_file = os.path.join('..', '..', 'data', sv_positions + '.bedpe') sv_caller_name = os.path.basename(sv_positions) if os.path.exists(sv_caller_file): @@ -350,8 +347,8 @@ def get_labels(chrlist, chr_dict, win_len, svtype, ground_truth, sv_positions, # Keep only positions that can be used to create windows cpos_list = [ - (chrom1, pos1, chrom2, pos2) - for chrom1, pos1, chrom2, pos2 in cpos_list if chrom1 in chrlist + (chrom1, pos1, chrom2, pos2, strand_info) + for chrom1, pos1, chrom2, pos2, strand_info, in cpos_list if chrom1 in chrlist and chrom2 in chrlist and win_hlen <= pos1 <= chr_dict[chrom1] - win_hlen and win_hlen <= pos2 <= chr_dict[chrom2] - win_hlen ] @@ -381,11 +378,11 @@ def main(): ''' parser = argparse.ArgumentParser(description='Create labels') - parser.add_argument('-b', - '--bed', + parser.add_argument('-f', + '--fasta', type=str, - default='../../data/seqs.bed', - help="Specify chromosome regions to consider (BED)") + default='../../data/test.fasta', + help="Specify FASTA file") parser.add_argument('-l', '--logfile', type=str, @@ -409,7 +406,6 @@ def main(): parser.add_argument('-sv', '--sv_positions', type=str, - #default=os.path.join('..', '..', 'data', 'gridss'), default=os.path.join('..', '..', 'data', 'split_reads'), help="Specify Manta/GRIDSS BEDPE file") parser.add_argument('-gt', @@ -431,11 +427,10 @@ def main(): args = parser.parse_args() sv_caller_name = os.path.basename(args.sv_positions) - output_dir = os.path.join(args.outputpath, 'labels', + output_dir = os.path.join(args.outputpath, 'cnn', 'win' + str(args.window), - args.svtype, - sv_caller_name - ) + sv_caller_name, 'windows', + args.svtype) os.makedirs(output_dir, exist_ok=True) # Log file logfilename = os.path.join(output_dir, args.logfile) @@ -450,7 +445,7 @@ def main(): t0 = time() # Get dictionary of chromosome lengths - chr_dict = chr_dict_from_bed(args.bed) + chr_dict = get_chr_dict(args.fasta) get_labels(chrlist=args.chrlist.split(','), chr_dict=chr_dict, diff --git a/scripts/genome_wide/model_functions.py b/scripts/genome_wide/model_functions.py index a74adbd8..775c481d 100644 --- a/scripts/genome_wide/model_functions.py +++ b/scripts/genome_wide/model_functions.py @@ -1,19 +1,18 @@ import logging import os - +from collections import Counter import matplotlib.pyplot as plt import numpy as np import pandas as pd -#from mcfly import modelgen, find_architecture +# from mcfly import modelgen, find_architecture from sklearn.metrics import (average_precision_score, f1_score, precision_recall_curve) def unfold_win_id(win_id): + chr1, pos1, chr2, pos2, strand_info = win_id.split('_') - chr1, pos1, chr2, pos2 = win_id.split('_') - - return chr1, pos1, chr2, pos2 + return chr1, pos1, chr2, pos2, strand_info # def create_model_with_mcfly(X, y_binary): @@ -65,29 +64,77 @@ def unfold_win_id(win_id): # # return history, best_model +def get_data(windows_list, npz_mode, svtype): + + def filter_labels(X, y, win_ids): + # print(y) + keep = [i for i, v in enumerate(y) if v in [svtype, 'no' + svtype]] + # print(keep) + X = X[np.array(keep)] + # print(y) + y = [y[i] for i in keep] + win_ids = [win_ids[i] for i in keep] + + print(Counter(y)) + return X, y, win_ids + + X = [] + y = [] + win_ids = [] + + for t in windows_list: + + logging.info('Loading data from {}...'.format(t)) + + npzfile = np.load(t, allow_pickle=True) + + X.extend(npzfile['data']) + labels = npzfile['labels'] + labels = labels.item() + + y.extend(labels.values()) + win_ids.extend(labels.keys()) + + logging.info('Data from {} loaded'.format(t)) + + X = np.stack(X, axis=0) + + logging.info(X.shape) + logging.info(Counter(y)) + + # X, y, win_ids = filter_labels(X, y, win_ids) -def evaluate_model(model, X_test, ytest_binary, win_ids_test, results, cv_iter, - output, mapclasses, output_dir, svtype): - def write_bed_wrong_predictions(probs, predicted, y_index, win_ids_test, - class_labels): + mapclasses = {svtype: 0, 'no' + svtype: 1} - #print(class_labels) + y = np.array([mapclasses[i] for i in y]) + win_ids = np.array(win_ids) + + return X, y, win_ids + + +def evaluate_model(model, X_test, ytest_binary, win_ids_test, + results, mapclasses, output_dir, svtype): + # print(mapclasses) + + def write_wrong_predictions(probs, predicted, y_index, win_ids_test, + class_labels): + + # print(class_labels) outdir = os.path.join(output_dir, 'predictions') os.makedirs(outdir, exist_ok=True) outfile = os.path.join( outdir, - 'cnn_wrong_predictions.bedpe') + 'wrong.bedpe') lines = [] for prob, p, r, w in zip(probs, predicted, y_index, win_ids_test): if class_labels[p] != class_labels[r]: - sv_score = prob[0] - chr1, pos1, chr2, pos2 = unfold_win_id(w) + chr1, pos1, chr2, pos2, strand_info = unfold_win_id(w) # print('{0}_{1}:{2}_{3}'.format(chr1, pos1, chr2, pos2)) lines.append('\t'.join([ str(chr1), @@ -96,10 +143,12 @@ def write_bed_wrong_predictions(probs, predicted, y_index, win_ids_test, str(chr2), str(pos2), str(int(pos2) + 1), 'PRED:' + class_labels[p] + '_TRUE:' + - class_labels[r], - str(sv_score) - #str(prob[0]), - #str(prob[1]) + class_labels[r], + str(sv_score), + strand_info[0], + strand_info[1] + # str(prob[0]), + # str(prob[1]) ]) + '\n') f = open(outfile, 'w') @@ -110,8 +159,8 @@ def write_bed_wrong_predictions(probs, predicted, y_index, win_ids_test, finally: f.close() - def write_bed_predictions(probs, predicted, y_index, win_ids_test, - class_labels, svtype): + def write_correct_predictions(probs, predicted, y_index, win_ids_test, + class_labels, svtype): # print(class_labels) @@ -120,18 +169,18 @@ def write_bed_predictions(probs, predicted, y_index, win_ids_test, outfile = os.path.join( outdir, - 'cnn_predictions.bedpe') + 'correct.bedpe') lines = [] j = 1 + for prob, p, r, w in zip(probs, predicted, y_index, win_ids_test): # print('{0}_{1}'.format(class_labels[p], class_labels[r])) if class_labels[p] == svtype: - sv_score = prob[0] - chr1, pos1, chr2, pos2 = unfold_win_id(w) + chr1, pos1, chr2, pos2, strand_info = unfold_win_id(w) # print('{0}_{1}:{2}_{3}'.format(chr1, pos1, chr2, pos2)) @@ -143,9 +192,11 @@ def write_bed_predictions(probs, predicted, y_index, win_ids_test, str(pos2), str(int(pos2) + 1), 'PRED_' + class_labels[p] + '_TRUE_' + class_labels[r] + '_' + str(j), - str(sv_score) - #str(prob[0]), - #str(prob[1]) + str(sv_score), + strand_info[0], + strand_info[1] + # str(prob[0]), + # str(prob[1]) ]) + '\n') j += 1 @@ -158,24 +209,13 @@ def write_bed_predictions(probs, predicted, y_index, win_ids_test, f.close() dict_sorted = sorted(mapclasses.items(), key=lambda x: x[1]) - # print(dict_sorted) class_labels = [i[0] for i in dict_sorted] n_classes = ytest_binary.shape[1] # print(y_binarized) # print(n_classes) - probs = model.predict_proba(X_test, batch_size=10000, verbose=False) - - # save model - outdir = os.path.join(output_dir, 'models') - - os.makedirs(outdir, exist_ok=True) - - model.save( - os.path.join( - outdir, '{0}_model_{1}.hdf5'.format(output, - str(int(cv_iter) + 1)))) + probs = model.predict(X_test, batch_size=1000, verbose=False) # columns are predicted, rows are truth predicted = probs.argmax(axis=1) @@ -185,14 +225,12 @@ def write_bed_predictions(probs, predicted, y_index, win_ids_test, # print(y_index) # write predictions - write_bed_wrong_predictions(probs, predicted, y_index, win_ids_test, - class_labels) - write_bed_predictions(probs, predicted, y_index, win_ids_test, - class_labels, svtype) + write_wrong_predictions(probs, predicted, y_index, win_ids_test, + class_labels) + write_correct_predictions(probs, predicted, y_index, win_ids_test, + class_labels, svtype) # print(y_index) - outdir = os.path.join(output_dir, 'confusion_matrix') - os.makedirs(outdir, exist_ok=True) confusion_matrix = pd.crosstab(pd.Series(y_index), pd.Series(predicted)) confusion_matrix.index = [class_labels[i] for i in confusion_matrix.index] @@ -201,9 +239,8 @@ def write_bed_predictions(probs, predicted, y_index, win_ids_test, ] confusion_matrix.reindex(columns=[l for l in class_labels], fill_value=0) confusion_matrix.to_csv(os.path.join( - outdir, '{0}_confusion_matrix_{1}.csv'.format(output, - str(int(cv_iter) + 1))), - sep='\t') + output_dir, 'confusion_matrix.csv'), + sep='\t') # For each class precision = dict() @@ -228,9 +265,9 @@ def write_bed_predictions(probs, predicted, y_index, win_ids_test, average_precision["weighted"] = average_precision_score(ytest_binary, probs, average="weighted") - print( - 'Average precision score, weighted over all classes: {0:0.2f}'.format( - average_precision["weighted"])) + # print( + # 'Average precision score, weighted over all classes: {0:0.2f}'.format( + # average_precision["weighted"])) f1_score_metric["weighted"] = f1_score(y_index, predicted, @@ -238,26 +275,21 @@ def write_bed_predictions(probs, predicted, y_index, win_ids_test, results = results.append( { - "run": str(cv_iter + 1), "test_set_size": X_test.shape[0], "average_precision_score": average_precision["weighted"], "f1_score": f1_score_metric["weighted"] }, ignore_index=True) - plot_precision_recall(cv_iter, mapclasses, precision, recall, - average_precision, output, output_dir) + plot_precision_recall(mapclasses, precision, recall, + average_precision, output_dir) return results, (average_precision, precision, recall, thresholds, f1_score_metric) -def plot_precision_recall(cv_iter, mapclasses, precision, recall, - average_precision, output, output_dir): - - outdir = os.path.join(output_dir, 'plots') - os.makedirs(outdir, exist_ok=True) - +def plot_precision_recall(mapclasses, precision, recall, + average_precision, output_dir): from itertools import cycle # setup plot details colors = cycle( @@ -296,6 +328,6 @@ def plot_precision_recall(cv_iter, mapclasses, precision, recall, plt.legend(lines, labels, loc=(0, -.38), prop=dict(size=14)) plt.savefig(os.path.join( - outdir, '{0}_PrecRec_{1}.png'.format(output, str(int(cv_iter) + 1))), - bbox_inches='tight') + output_dir, 'precision_vs_recall.png'), + bbox_inches='tight') plt.close() diff --git a/scripts/genome_wide/predict.py b/scripts/genome_wide/predict.py new file mode 100644 index 00000000..035ca29d --- /dev/null +++ b/scripts/genome_wide/predict.py @@ -0,0 +1,142 @@ +import argparse +import os +import pandas as pd +import subprocess + +from tensorflow.keras.utils import to_categorical +from tensorflow.keras.models import load_model + +from model_functions import \ + evaluate_model, get_data + + +def predict(input_data, sample_name, svtype, model_fn, model_name, output_dir): + + os.makedirs(output_dir, exist_ok=True) + + X, y, win_ids = get_data(input_data, True, svtype) + y_binary = to_categorical(y, num_classes=params['n_classes']) + + print('Predicting {} with model trained on {}...'.format(sample_name, model_name)) + + model = load_model(model_fn) + + results = pd.DataFrame() + + intermediate_results, metrics = evaluate_model(model, + X, y_binary, win_ids, + results, + params['mapclasses'], + output_dir, svtype) + + results = results.append(intermediate_results) + results.to_csv(os.path.join(output_dir, 'metrics.csv'), sep='\t') + + +def main(): + + default_win = 200 + default_path = os.path.join('./cnn/win'+str(default_win), 'split_reads') + def_windows_file = os.path.join(default_path, 'windows', 'DEL', 'windows_en.npz') + + parser = argparse.ArgumentParser(description='Use model to predict') + + parser.add_argument('-m', + '--model', + type=str, + default='./cnn/win200/split_reads/cv/DEL/1/model.hdf5', + help="TensorFlow model in HDF5 format" + ) + parser.add_argument('-n', + '--model_name', + type=str, + default='cnn', + help="Name of the model" + ) + parser.add_argument('-i', + '--input', + type=str, + default=def_windows_file, + help="Specify list of windows" + ) + parser.add_argument('-sn', + '--sample_name', + type=str, + default='test', + help="Specify sample name" + ) + parser.add_argument('-s', + '--svtype', + type=str, + default='DEL', + help="Specify SV type") + parser.add_argument('-fe', + '--encode_blacklist', + type=str, + default='../../data/ENCFF001TDO.bed', + help="ENCODE blacklist") + parser.add_argument('-fn', + '--n_regions', + type=str, + default='../../data/reference_N_regions.bed', + help="Regions in the genome containing Ns") + parser.add_argument('-tb', + '--twobit', + type=str, + default='../../data/test.2bit', + help="TwoBit reference genome") + parser.add_argument('-o', + '--output', + type=str, + default='results', + help="Output folder" + ) + + args = parser.parse_args() + + global params + mapclasses = {args.svtype: 0, 'no' + args.svtype: 1} + + # Parameters + params = { + 'mapclasses': mapclasses, + 'n_classes': len(mapclasses.keys()) + } + + windows_list = args.input.split(',') + + predict(windows_list, args.sample_name, args.svtype, + args.model, args.model_name, os.path.join(args.output, args.svtype)) + + out_prefix = os.path.join(args.output, "sv-channels") + + merge_sv_calls = ' '.join([ + "cd ../R; " + "Rscript merge_sv_calls.R", + "-i", os.path.join("../genome_wide", args.output), + "-f", args.encode_blacklist, + "-n", args.n_regions, + "-m split_reads", + "-o", os.path.join("../genome_wide", out_prefix) + ]) + print(merge_sv_calls) + cmd_out = subprocess.run(merge_sv_calls, shell=True, check=True) + print(cmd_out) + + assert os.path.join("../utils/bedpe_to_vcf.py") + assert os.path.join("../genome_wide", out_prefix+'.bedpe') + + bedpe_to_vcf = ' '.join([ + "source activate sv-channels; python ../utils/bedpe_to_vcf.py", + "-i", os.path.join("../genome_wide", out_prefix+'.bedpe'), + "-b", args.twobit, + "-s", args.sample_name, + "-o", os.path.join("../genome_wide", out_prefix+'.'+args.sample_name+'.vcf') + ]) + print(bedpe_to_vcf) + cmd_out = subprocess.run(bedpe_to_vcf, shell=True, check=True) + print(cmd_out) + + +if __name__ == '__main__': + main() diff --git a/scripts/genome_wide/snv.py b/scripts/genome_wide/snv.py index 9dc64de0..eb70de6c 100644 --- a/scripts/genome_wide/snv.py +++ b/scripts/genome_wide/snv.py @@ -10,20 +10,8 @@ from functions import * -config = get_config_file() - -HPC_MODE = config["DEFAULT"]["HPC_MODE"] -REF_GENOME = config["DEFAULT"]["REF_GENOME"] - -MAX_PILEUP_BUFFER_SIZE = 8000 -minMAPQ = config["DEFAULT"]["MIN_MAPQ"] - - -def get_snvs(ibam, itwobit, chrName, outFile): - def get_2bit_genome(): - genome = twobit.TwoBitFile(itwobit) - return genome +def get_snvs(ibam, itwobit, chrName, max_coverage, outFile): def get_snv_number(query_seq_list, reference_base): reference_base = reference_base.upper() @@ -34,26 +22,6 @@ def get_snv_number(query_seq_list, reference_base): else: return 0 - def correct_for_indels(read, ref_string): - - seq = read.query_alignment_sequence - base_quals = [i for i in read.query_alignment_qualities] - # print(base_quals) - bq = [] - pos = 0 - bq.append(read.reference_start + i) - for op, l in read.cigartuples: - if op not in [0, 1, 2]: - continue - elif op == 1: - ref_string = ref_string[:pos] + '-' * l + ref_string[pos:] - base_quals = base_quals[:pos] + base_quals[pos + l:] - elif op == 2: - seq = seq[:pos] + '-' * l + seq[pos:] - base_quals = base_quals[:pos] + [0] * l + base_quals[pos:] - pos += l - return seq, base_quals, ref_string - # Check if the BAM file in input exists assert os.path.isfile(ibam) @@ -67,17 +35,16 @@ def correct_for_indels(read, ref_string): # Fetch reads over the entire chromosome between positions [0, chrLen] start_pos = 0 stop_pos = chrLen - # stop_pos = 10000000 reference_sequence = twobit.TwoBitFile(itwobit) - # snv_list = ['BQ', 'nALN', 'nSEG', 'A', 'a', 'C', 'c', 'G', 'g', 'T', 't'] - snv_list = ['BQ', 'SNV'] - snv_array = np.zeros(shape=(len(snv_list), stop_pos), dtype=np.uint32) + snv_list = ['BQ', 'SNV', 'MAPQ'] + snv_array = np.zeros(shape=(stop_pos, len(snv_list)), dtype=np.float32) + # print(snv_array.shape) snv_dict = {v: n for n, v in enumerate(snv_list)} # print(snv_dict) # Print every n_r alignments processed - n_r = 10**6 + n_r = 10 ** 6 # Record the current time last_t = time() @@ -85,24 +52,30 @@ def correct_for_indels(read, ref_string): start_pos, stop_pos, stepper='all'): + # pileupcolumn.set_min_base_quality(0) # print("\ncoverage at base %s = %s" % # (pileupcolumn.pos, pileupcolumn.nsegments)) - if 0 < pileupcolumn.nsegments < MAX_PILEUP_BUFFER_SIZE and start_pos <= pileupcolumn.pos <= stop_pos: + + if 0 < pileupcolumn.nsegments < max_coverage and start_pos <= pileupcolumn.pos <= stop_pos: quals = pileupcolumn.get_query_qualities() if len(quals) > 0: - snv_array[snv_dict['BQ'], pileupcolumn.pos] = np.median( - pileupcolumn.get_query_qualities()) - # snv_array[snv_dict['nALN'], pileupcolumn.pos] = pileupcolumn.get_num_aligned() - # snv_array[snv_dict['nSEG'], pileupcolumn.pos] = pileupcolumn.nsegments + snv_array[pileupcolumn.pos, snv_dict['BQ']] = np.median( + quals) + quals = pileupcolumn.get_mapping_qualities() + if len(quals) > 0: + snv_array[pileupcolumn.pos, snv_dict['MAPQ']] = np.median( + quals) + try: query_seq_list = pileupcolumn.get_query_sequences() - #chrName_2bit = chrName.replace('chr', '') #if REF_GENOME == 'GRCh38' else 'chr' + chrName + snv_number = get_snv_number( query_seq_list, reference_sequence[chrName][pileupcolumn.pos]) - snv_array[snv_dict['SNV'], pileupcolumn.pos] = snv_number/pileupcolumn.nsegments \ + + snv_array[pileupcolumn.pos, snv_dict['SNV']] = snv_number / pileupcolumn.nsegments \ if pileupcolumn.nsegments != 0 else 0 except AssertionError as error: @@ -112,81 +85,11 @@ def correct_for_indels(read, ref_string): chrName, pileupcolumn.pos, pileupcolumn.nsegments)) continue - # # Pysam iterator to fetch the reads - # iter = bamfile.fetch(chrName, start_pos, stop_pos) - # - # for i, read in enumerate(iter, start=1): - # - # # Every n_r alignments, write log informations - # if not i % n_r: - # # Record the current time - # now_t = time() - # # print(type(now_t)) - # logging.info("%d alignments processed (%f alignments / s)" % ( - # i, - # n_r / (now_t - last_t))) - # last_t = time() - # - # if not read.is_unmapped and read.mapping_quality >= minMAPQ: - # - # # print(read) - # # print(read.cigartuples) - # # print(read.mapping_quality) - # # print(read.query_alignment_qualities) - # # print('{} == {}'.format( - # # len(read.query_alignment_qualities), read.reference_end - read.reference_start)) - # - # try: - # - # ref_string = reference_sequence['chr' + chrName][read.reference_start:read.reference_end].upper() - # - # if len(read.cigartuples) == 1 and read.cigartuples[0][0] == 0: - # - # query_string = read.query_alignment_sequence - # query_base_qualities = read.query_alignment_qualities - # - # else: - # - # query_string, query_base_qualities, ref_string = correct_for_indels(read, ref_string) - # # print(read.query_alignment_sequence) - # # print(query_string) - # # print(ref_string) - # - # snv_idx = [read.reference_start + index for index, (e1, e2) in enumerate( - # zip(query_string, ref_string)) if e1 != e2 and e1 != '-' and e2 != '-' ] - # # for i in snv_idx: - # # pos = i - read.reference_start - # # print('{} != {}'.format(query_string[pos], ref_string[pos])) - # - # snv_array[snv_dict['SNV'], snv_idx] += 1 - # - # snv_array[snv_dict['BQ'], read.reference_start:read.reference_end] += \ - # np.array(query_base_qualities, dtype=np.uint32) - # snv_array[snv_dict['RC'], read.reference_start:read.reference_end] += 1 - # - # except: - # - # print(read) - # print(read.cigartuples) - # print(read.mapping_quality) - # print(read.query_alignment_qualities) - - # print(cnt) - # for k in cnt.keys(): - # if k in snv_list and k.upper() != reference_sequence['chr' + chrName][pileupcolumn.pos]: - # snv_array[snv_dict[k], pileupcolumn.pos] = cnt[k] - - # # Close the BAM file - # bamfile.close() - # - # snv_array = np.vstack( - # ( - # np.divide(snv_array[snv_dict['SNV']], snv_array[snv_dict['RC']], - # where=snv_array[snv_dict['RC']] != 0), - # np.divide(snv_array[snv_dict['BQ']], snv_array[snv_dict['RC']], - # where=snv_array[snv_dict['RC']] != 0) - # ) - # ) + for i in np.arange(snv_array.shape[1]): + logging.info('snv array {}: non-zero elements at index {}:{}'.format(chrName, + i, + np.argwhere(snv_array[:, i] != 0).shape[ + 0])) # Write the output # snv_array = np.delete(snv_array, 2, 0) @@ -195,34 +98,22 @@ def correct_for_indels(read, ref_string): def main(): - # Default BAM file for testing - # On the HPC - # wd = '/hpc/cog_bioinf/ridder/users/lsantuari/Datasets/DeepSV/artificial_data/run_test_INDEL/samples/T0/BAM/T0/mapping' - # inputBAM = wd + "T0_dedup.bam" - # Locally - #wd = '/Users/lsantuari/Documents/Data/HPC/DeepSV/Artificial_data/run_test_INDEL/BAM/' - #inputBAM = 'wd + "T1_dedup.bam" - # wd = '/Users/lsantuari/Documents/mount_points/hpc_mnt/Datasets/CretuStancu2017/Patient1/' - # inputBAM = wd + 'Patient1.bam' - - # Default chromosome is 17 for the artificial data - # Parse the arguments of the script - parser = argparse.ArgumentParser(description='Get snv info') + parser = argparse.ArgumentParser(description='Get SNV info') parser.add_argument('-b', '--bam', type=str, - default='hmz-sv.bam', + default='../../data/test.bam', help="Specify input file (BAM)") parser.add_argument('-t', '--twobit', type=str, - default='hmz-sv.2bit', + default='../../data/test.2bit', help="Specify input file (2bit)") parser.add_argument('-c', '--chr', type=str, - default='17', + default='12', help="Specify chromosome") parser.add_argument('-o', '--out', @@ -238,14 +129,22 @@ def main(): '--logfile', default='snv.log', help='File in which to write logs.') + parser.add_argument('-pb', + '--max_coverage', + type=int, + default=1000, + help='Consider only regions with coverage less than max_coverage to speed up the processing') args = parser.parse_args() # Log file cmd_name = 'snv' + output_dir = os.path.join(args.outputpath, cmd_name) + os.makedirs(output_dir, exist_ok=True) + logfilename = os.path.join(output_dir, '_'.join((args.chr, args.logfile))) output_file = os.path.join(output_dir, '_'.join((args.chr, args.out))) @@ -259,6 +158,7 @@ def main(): get_snvs(ibam=args.bam, itwobit=args.twobit, chrName=args.chr, + max_coverage=args.max_coverage, outFile=output_file) logging.info('Time: SNVs on BAM %s and Chr %s: %f' % (args.bam, args.chr, (time() - t0))) diff --git a/scripts/genome_wide/split_reads.py b/scripts/genome_wide/split_reads.py index 88d14650..6a6ba2af 100644 --- a/scripts/genome_wide/split_reads.py +++ b/scripts/genome_wide/split_reads.py @@ -3,53 +3,50 @@ import json import logging import os +import pysam from collections import Counter, defaultdict from time import time - -import pysam from cigar import Cigar -from intervaltree import IntervalTree - from functions import * strand_str = {True: '-', False: '+'} -def append_coord(split_pos_coord, chrName, refpos, chr, pos): - if chrName == chr: - if refpos < pos: - split_pos_coord.append((chrName, refpos, chr, pos)) +def append_coord(split_pos_coord, chr1, pos1, chr2, pos2, strand_info): + + if chr1 == chr1: + if pos2 < pos1: + split_pos_coord.append((chr2, pos2, chr1, pos1, strand_info)) else: - split_pos_coord.append((chr, pos, chrName, refpos)) - elif chrName < chr: - split_pos_coord.append((chrName, refpos, chr, pos)) - elif chrName > chr: - split_pos_coord.append((chr, pos, chrName, refpos)) + split_pos_coord.append((chr1, pos1, chr2, pos2, strand_info)) + elif chr2 < chr1: + split_pos_coord.append((chr2, pos2, chr1, pos1, strand_info)) + elif chr2 > chr1: + split_pos_coord.append((chr1, pos1, chr2, pos2, strand_info)) return split_pos_coord -def get_split_read_positions(ibam, chr_list, outFile, outBedpe): +def get_split_read_positions(ibam, chr_list, min_mapq, min_sr_support, outFile, outBedpe): # Check if the BAM file in input exists assert os.path.isfile(ibam) - config = get_config_file() - minMAPQ = config["DEFAULT"]["MIN_MAPQ"] - min_support = config["DEFAULT"]["MIN_SR_SUPPORT"] - # List to store the split read positions split_pos_coord = dict() - sv_type_list = ['INDEL_INS', 'INDEL_DEL', 'DEL', 'INS', 'INV', 'DUP', 'TRA', 'ND'] + sv_type_list = ['INDEL_INS', 'INDEL_DEL', 'DEL', 'INS', 'INV', 'DUP', 'CTX', 'ND'] for k in sv_type_list: split_pos_coord[k] = [] + # Load the BAM file + bamfile = pysam.AlignmentFile(ibam, "rb") + + bam_mean, bam_stddev = get_insert_size(ibam, bamfile, min_mapq) + # # Tree with split read positions # gtree = {k: dict() for k in sv_type_list} # for s in sv_type_list: # gtree[s] = {k: IntervalTree() for k in chr_list} - # Load the BAM file - bamfile = pysam.AlignmentFile(ibam, "rb") n_indels = 0 n_split = 0 @@ -104,7 +101,7 @@ def get_split_read_positions(ibam, chr_list, outFile, outBedpe): clipped_pos_dict['right'][read.reference_name].append( read.reference_end) - if not read.is_unmapped and read.mapping_quality >= minMAPQ and \ + if not read.is_unmapped and read.mapping_quality >= min_mapq and \ read.reference_name in chr_list: if has_indels(read): @@ -119,8 +116,8 @@ def get_split_read_positions(ibam, chr_list, outFile, outBedpe): n_indels += 1 split_pos_coord['INDEL_INS'] = append_coord( - split_pos_coord['INDEL_INS'], read.reference_name, - pos, read.reference_name, pos+1) + split_pos_coord['INDEL_INS'], read.reference_name, pos, + read.reference_name, pos + 1, '+-') for start, end in zip(dels_start, dels_end): @@ -132,8 +129,8 @@ def get_split_read_positions(ibam, chr_list, outFile, outBedpe): max_cigar_del = del_size split_pos_coord['INDEL_DEL'] = append_coord( - split_pos_coord['INDEL_DEL'], read.reference_name, - start, read.reference_name, end) + split_pos_coord['INDEL_DEL'], read.reference_name, start, + read.reference_name, end, '+-') if read.has_tag('SA'): @@ -187,30 +184,29 @@ def get_split_read_positions(ibam, chr_list, outFile, outBedpe): sv_type = 'ND' if clipped_string == 'right' and read.reference_name == chr_SA: - if read.reference_end < pos_SA: + if read.reference_start < pos_SA: sv_type = 'DEL' else: sv_type = 'DUP' elif clipped_string == 'left' and read.reference_name == chr_SA: if read.reference_start > pos_SA: - sv_type = 'DEL' - else: sv_type = 'DUP' - elif clipped_string == 'both' and read.reference_name == chr_SA: + else: sv_type = 'DEL' + elif clipped_string == 'both' and read.reference_name == chr_SA: + sv_type = 'DEL' if read.reference_name == chr_SA and \ - not read.mate_is_unmapped and \ - read.is_reverse == read.mate_is_reverse and \ - read.reference_name == read.next_reference_name: + strand_str[read.is_reverse] == strand_str[read.mate_is_reverse]: sv_type = 'INV' if read.reference_name != chr_SA: - sv_type = 'TRA' + sv_type = 'CTX' dist = abs( clipped_pos - pos_SA) if read.reference_name == chr_SA else 0 + dist = (dist - bam_mean) / bam_stddev if read.reference_name == chr_SA else 0 # if sv_type == 'TRA': # print('{} {} {} => {}:{}-{} {} {} {}'.format( @@ -221,9 +217,14 @@ def get_split_read_positions(ibam, chr_list, outFile, outBedpe): # read.query_name # )) + if strand_str[read.is_reverse] == strand_SA: + strand_info = '+-' + else: + strand_info = strand_str[read.is_reverse]+strand_SA + split_pos_coord[sv_type] = append_coord( split_pos_coord[sv_type], - read.reference_name, clipped_pos, chr_SA, pos_SA) + read.reference_name, clipped_pos, chr_SA, pos_SA, strand_info) split_reads[ read.reference_name][clipped_ch][clipped_pos] += 1 @@ -240,7 +241,11 @@ def get_split_read_positions(ibam, chr_list, outFile, outBedpe): # Look for INS positions: for chrom in chr_list: for k in ['right', 'left']: - clipped_pos_dict[k][chrom] = set(clipped_pos_dict[k][chrom]) + clipped_pos_dict_cnt = Counter(clipped_pos_dict[k][chrom]) + clipped_pos_dict[k][chrom] = set([ + int(key) for key, val in clipped_pos_dict_cnt.items() + if val >= 3] + ) # based on artificial INS for chrom in chr_list: @@ -250,14 +255,14 @@ def get_split_read_positions(ibam, chr_list, outFile, outBedpe): set(range(p - 1, p + 1, 1)) & clipped_pos_dict['left'][chrom]) > 0: split_pos_coord['INS'] = append_coord(split_pos_coord['INS'], - chrom, p, chrom, p + 1) + chrom, p, chrom, p + 1, '+-') for p in clipped_pos_dict['left'][chrom]: # is the right clipped position close to the left clipped position of a neighboring read? if len( set(range(p - 1, p + 1, 1)) & clipped_pos_dict['right'][chrom]) > 0: split_pos_coord['INS'] = append_coord(split_pos_coord['INS'], - chrom, p, chrom, p + 1) + chrom, p, chrom, p + 1, '+-') # Count the number of split reads per position for chrom in chr_list: @@ -279,7 +284,7 @@ def get_split_read_positions(ibam, chr_list, outFile, outBedpe): chrom, len([ p for p, c in split_pos_cnt[k][chrom].items() - if c >= min_support + if c >= min_sr_support ]))) # for chr1,pos1,chr2,pos2 in split_pos_coord['DEL']: # print('{}:{}-{}:{}'.format(chr1, pos1, chr2, pos2)) @@ -301,12 +306,12 @@ def get_split_read_positions(ibam, chr_list, outFile, outBedpe): for k in ['right', 'left']: positions_with_min_support[k][chrom] = [ p for p, c in total_reads_cnt[k][chrom].items() - if c >= min_support + if c >= min_sr_support ] logging.info('Number of ' + k + '-split positions' + ' on Chr%s with min %d support: %d' % - (chrom, min_support, + (chrom, min_sr_support, len(positions_with_min_support[k][chrom]))) for k in total_reads_coord.keys(): logging.info('Number of unique pair of total positions ' + k + @@ -327,8 +332,8 @@ def get_split_read_positions(ibam, chr_list, outFile, outBedpe): total_reads_coord_min_support[k] = total_reads_coord[k] else: total_reads_coord_min_support[k] = [ - (chr1, pos1, chr2, pos2) - for chr1, pos1, chr2, pos2 in total_reads_coord[k] + (chr1, pos1, chr2, pos2, strand_info) + for chr1, pos1, chr2, pos2, strand_info in total_reads_coord[k] if pos1 in positions_with_min_support_set[chr1] or pos2 in positions_with_min_support_set[chr2] ] @@ -348,18 +353,18 @@ def get_split_read_positions(ibam, chr_list, outFile, outBedpe): # Write BEDPE with gzip.open(outBedpe, 'wt') as fout: for k in total_reads_coord_min_support.keys(): - for chr1, pos1, chr2, pos2 in total_reads_coord_min_support[k]: + for chr1, pos1, chr2, pos2, strand_info in total_reads_coord_min_support[k]: + assert len(strand_info) == 2 fout.write('\t'.join([ chr1, str(pos1), str(pos1 + 1), chr2, str(pos2), - str(pos2 + 1), k + str(pos2 + 1), k, '*', strand_info[0], strand_info[1] ]) + '\n') def main(): - # Parse the arguments of the script parser = argparse.ArgumentParser(description='Get split reads positions') parser.add_argument('-b', @@ -387,11 +392,22 @@ def main(): '--outputpath', type=str, default='.', - help="Specify output path") + help="Specify output path" + ) parser.add_argument('-l', '--logfile', default='split_reads.log', help='File in which to write logs.') + parser.add_argument('-m', + '--min_mapq', + type=int, + default=10, + help='Minimum read mapping quality') + parser.add_argument('-s', + '--min_sr_support', + type=int, + default=1, + help='Minimum number of split reads') args = parser.parse_args() @@ -413,6 +429,8 @@ def main(): get_split_read_positions(ibam=args.bam, chr_list=args.chrlist.split(','), + min_mapq=args.min_mapq, + min_sr_support=args.min_sr_support, outFile=output_file, outBedpe=output_file_bedpe) diff --git a/scripts/genome_wide/train.py b/scripts/genome_wide/train.py new file mode 100644 index 00000000..39abe804 --- /dev/null +++ b/scripts/genome_wide/train.py @@ -0,0 +1,488 @@ +from __future__ import (absolute_import, division, print_function, + unicode_literals) + +import argparse +import gzip +import json +import logging +import os +import sys + +from time import time + +import bcolz +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +from tensorflow.keras.models import Sequential + +from tensorflow.keras.utils import to_categorical +from tensorflow.keras.models import load_model + +from tensorflow.keras.layers import Dense, Activation, Convolution1D, Lambda, \ + Convolution2D, Flatten, \ + Reshape, LSTM, Dropout, TimeDistributed, BatchNormalization + +from tensorflow.keras.regularizers import l2 +from tensorflow.keras.optimizers import Adam + +from tensorflow.keras.callbacks import (CSVLogger, EarlyStopping, ModelCheckpoint, + TensorBoard) + +from sklearn.model_selection import StratifiedKFold, train_test_split + +from model_functions import \ + evaluate_model, get_data # create_model_with_mcfly, train_model_with_mcfly + + +def get_labels(channel_data_dir, win): + label_file = os.path.join(channel_data_dir, 'labels_win' + str(win), + 'labels.json.gz') + + with gzip.GzipFile(label_file, 'r') as fin: + labels = json.loads(fin.read().decode('utf-8')) + + return labels + + +def train_and_test_data(sampleName, npz_mode, svtype): + # Datasets + X, y, win_ids = get_data(sampleName, npz_mode, svtype) + + X = np.array(X) + y = np.array(y) + + # split into train/validation sets + X_train, X_test, y_train, y_test, win_ids_train, win_ids_test = train_test_split( + X, y, win_ids, test_size=0.3, random_state=2, stratify=y, shuffle=True) + + return X_train, X_test, y_train, y_test, win_ids_train, win_ids_test + + +def create_model(dim_length, dim_channels, outputdim): + weightinit = 'lecun_uniform' # weight initialization + + learning_rate = 10 ** (-model_params['learning_rate_exp']) + regularization_rate = 10 ** (-model_params['regularization_rate_exp']) + + model = Sequential() + + model.add(BatchNormalization(input_shape=(dim_length, dim_channels))) + + filters = [model_params['cnn_filters']] * model_params['cnn_layers'] + + for filter_number in filters: + # model.add(MaxPooling1D(pool_size=5, strides=None, padding='same')) + + model.add( + Convolution1D(filter_number, + kernel_size=model_params['kernel_size'], + padding='same', + kernel_regularizer=l2(regularization_rate), + kernel_initializer=weightinit)) + model.add(BatchNormalization()) + model.add(Activation('relu')) + + model.add(Flatten()) + + model.add( + Dense(units=model_params['fc_nodes'], + kernel_regularizer=l2(regularization_rate), + kernel_initializer=weightinit)) # Fully connected layer + model.add(Activation('relu')) # Relu activation + + # Adding one more FC layer + model.add( + Dense(units=model_params['fc_nodes'], + kernel_regularizer=l2(regularization_rate), + kernel_initializer=weightinit)) # Fully connected layer + model.add(Activation('relu')) # Relu activation + + model.add(Dense(units=outputdim, kernel_initializer=weightinit)) + model.add(BatchNormalization()) + model.add(Activation("sigmoid")) # Final classification layer + + model.compile(loss='categorical_crossentropy', + optimizer=Adam(lr=learning_rate), + metrics=['accuracy']) + + # i = 0 + # for model, params, model_types in [model]: + # logging.info('model ' + str(i)) + # i = i + 1 + # logging.info(params) + # logging.info(model.summary()) + + return model + + +def train(model_fn, params, X_train, y_train, y_train_binary): + # Design model + logging.info('Creating model...') + model = create_model(params['dim'], params['n_channels'], + params['n_classes']) + + # earlystop = EarlyStopping(monitor='val_loss', + # min_delta=0, + # patience=3, + # verbose=1, + # restore_best_weights=True) + + checkpoint = ModelCheckpoint(model_fn, + monitor='val_loss', + mode='min', + save_best_only=True, + verbose=1) + + # csv_logger = CSVLogger(os.path.join(channel_data_dir, 'training.log')) + # + # tbCallBack = TensorBoard(log_dir=os.path.join(channel_data_dir, 'Graph'), + # histogram_freq=0, + # write_graph=True, + # write_images=True) + + callbacks = [checkpoint] + + nosv_count, sv_count = np.bincount(y_train) + total_count = len(y_train) + logging.info('nosv_count:{}, sv_count:{}, total_count:{}'.format(nosv_count, sv_count, total_count)) + + weight_nosv = (1 / nosv_count) * (total_count) / 2.0 + weight_sv = (1 / sv_count) * (total_count) / 2.0 + + class_weights = {0: weight_sv, 1: weight_nosv} + logging.info('class_weights: {}'.format(class_weights)) + + logging.info('Fitting model...') + + # Train model on dataset + history = model.fit( + X_train, + y_train_binary, + validation_split=model_params['validation_split'], + batch_size=model_params['batch_size'], + epochs=model_params['epochs'], + shuffle=True, + class_weight=class_weights, + verbose=1, + callbacks=callbacks) + + model = load_model(model_fn) + + return model, history, X_train.shape[0], int(X_train.shape[0] * + model_params['validation_split']) + + +def cv_train_and_evaluate(X, y, y_binary, win_ids, train_indices, test_indices, model_dir, svtype): + # Generate batches from indices + X_train, X_test = X[train_indices], X[test_indices] + y_train, y_test = y[train_indices], y[test_indices] + y_train_binary, y_test_binary = y_binary[train_indices], y_binary[ + test_indices] + win_ids_train, win_ids_test = win_ids[train_indices], win_ids[ + test_indices] + + # Parameters + params = { + 'dim': X_train.shape[1], + 'n_classes': len(mapclasses.keys()), + 'n_channels': X_train.shape[2], + 'shuffle': True + } + + os.makedirs(model_dir, exist_ok=True) + model_fn = os.path.join(model_dir, 'model.hdf5') + + model, history, train_set_size, validation_set_size = train( + model_fn, params, X_train, y_train, y_train_binary) + + model.save(model_fn) + + results = pd.DataFrame() + + intermediate_results, metrics = evaluate_model(model, X_test, y_test_binary, win_ids_test, + results, mapclasses, model_dir, svtype) + + results = results.append(intermediate_results) + results.to_csv(os.path.join(model_dir, 'metrics.csv'), sep='\t') + + +def cross_validation(training_windows, outDir, npz_mode, svtype, kfold): + X, y, win_ids = get_data(training_windows, npz_mode, svtype) + y_binary = to_categorical(y, num_classes=len(mapclasses.keys())) + + # Instantiate the cross validator + skf = StratifiedKFold(n_splits=kfold, shuffle=True, random_state=1) + + # Loop through the indices the split() method returns + for index, (train_indices, test_indices) in enumerate(skf.split(X, y)): + print("Training on fold " + str(index + 1) + "/" + str(kfold) + "...") + + model_dir = os.path.join(outDir, 'kfold', svtype, + str(index + 1)) + + cv_train_and_evaluate(X, y, y_binary, win_ids, train_indices, test_indices, model_dir, svtype) + + +def cross_validation_by_chrom(training_windows, outDir, npz_mode, svtype, chrlist): + X, y, win_ids = get_data(training_windows, npz_mode, svtype) + y_binary = to_categorical(y, num_classes=len(mapclasses.keys())) + + # print(win_ids) + chrom_num1 = map(lambda x: x.split('_')[0], win_ids) + + chrom_array = np.array([c for c in chrom_num1 if c in chrlist]) + # print(chrom_array) + + cv_dict = {} + + for c in np.unique(chrom_array): + # print('Considering chromosome: {}'.format(c)) + + idx_chr = np.where(chrom_array == c) + idx_not_chr = np.where(chrom_array != c) + + cv_dict[c] = (idx_not_chr, idx_chr) + + # Loop through the indices the split() method returns + for chrom in cv_dict.keys(): + train_indices, test_indices = cv_dict[chrom] + + print("Test on chromosome " + chrom + "...") + + model_dir = os.path.join(outDir, 'chrom', svtype, chrom) + + cv_train_and_evaluate(X, y, y_binary, win_ids, train_indices, test_indices, model_dir, svtype) + + +def train_and_test_model(training_name, test_name, training_windows, test_windows, + outDir, + npz_mode, svtype): + X_test, y_test, win_ids_test = get_data(training_windows, npz_mode, svtype) + X_test, y_test, win_ids_test = get_data(test_windows, npz_mode, svtype) + + # Parameters + params = { + 'dim': X_train.shape[1], + 'batch_size': model_params['batch_size'], + 'epochs': model_params['epochs'], + 'val_split': model_params['validation_split'], + 'n_classes': len(mapclasses.keys()), + 'n_channels': X_train.shape[2], + 'shuffle': True + } + + y_train_binary = to_categorical(y_train, num_classes=params['n_classes']) + y_test_binary = to_categorical(y_test, num_classes=params['n_classes']) + + model_dir = os.path.join(outDir, 'trained_on_' + training_name + '_tested_on_' + test_name) + os.makedirs(model_dir, exist_ok=True) + model_fn = os.path.join(model_dir, 'model.hdf5') + + print('Training model on {}...'.format(training_name)) + model, history, train_set_size, validation_set_size = train( + model_fn, params, + X_train, y_train, y_train_binary) + + results = pd.DataFrame() + + intermediate_results, metrics = evaluate_model(model, X_test, y_test_binary, win_ids_test, + results, mapclasses, model_dir, svtype) + + results = results.append(intermediate_results) + results.to_csv(os.path.join(model_dir, 'metrics.csv'), sep='\t') + + +def main(): + default_win = 200 + default_path = os.path.join('./cnn/win' + str(default_win), 'split_reads') + def_windows_file = os.path.join(default_path, 'windows', 'DEL', 'windows_en.npz') + + parser = argparse.ArgumentParser(description='Train and test model') + + parser.add_argument('-p', + '--outputpath', + type=str, + default=default_path, + help="Specify output path") + parser.add_argument('-t', + '--training_windows', + type=str, + default=def_windows_file + ',' + def_windows_file, + help="Comma separated list of training data") + parser.add_argument('-x', + '--test_windows', + type=str, + default=def_windows_file + ',' + def_windows_file, + help="Specify training sample") + parser.add_argument('-tn', + '--training_sample_name', + type=str, + default='git-data', + help="Specify training sample") + parser.add_argument('-xn', + '--test_sample_name', + type=str, + default='git-data', + help="Specify training sample") + parser.add_argument('-l', + '--logfile', + default='training.log', + help='File in which to write logs.') + parser.add_argument('-s', + '--svtype', + type=str, + default='DEL', + help="Specify SV type") + parser.add_argument('-cv', + '--cv', + type=str, + default='kfold', + help="kfold/chrom") + parser.add_argument('-c', + '--chrlist', + type=str, + default='12,22', + help="Comma separated list of chromosomes to consider") + parser.add_argument('-k', + '--kfold', + type=int, + default=2, + help="Specify [k]-fold cross validation") + parser.add_argument('-e', + '--epochs', + type=int, + default=1, + help="Number of epochs") + parser.add_argument('-b', + '--batch_size', + type=int, + default=32, + help="Batch size") + parser.add_argument('-val', + '--validation_split', + type=float, + default=0.2, + help="Percent of training set to use for validation") + parser.add_argument('-npz', + '--load_npz', + type=bool, + default=True, + help="load npz?") + parser.add_argument('-cnn_layers', + '--cnn_layers', + type=int, + default=4, + help="Number of convolutional layers") + parser.add_argument('-cnn_filters', + '--cnn_filters', + type=int, + default=8, + help="Number of convolutional filters") + parser.add_argument('-kernel_size', + '--kernel_size', + type=int, + default=7, + help="Number of convolutional filters") + parser.add_argument('-fc_nodes', + '--fc_nodes', + type=int, + default=16, + help="Number of neurons in the dense layer") + parser.add_argument('-learning_rate_exp', + '--learning_rate_exp', + type=int, + default=4, + help="Learning rate = 10 ** (-learning_rate_exp)") + parser.add_argument('-regularization_rate_exp', + '--regularization_rate_exp', + type=int, + default=1, + help="Regularization rate = 10 ** (-regularization_rate_exp)") + + args = parser.parse_args() + + global mapclasses + mapclasses = {args.svtype: 0, 'no' + args.svtype: 1} + + global model_params + model_params = { + 'batch_size': args.batch_size, + 'epochs': args.epochs, + 'validation_split': args.validation_split, + 'cnn_layers': args.cnn_layers, + 'cnn_filters': args.cnn_filters, + 'kernel_size': args.kernel_size, + 'fc_nodes': args.fc_nodes, + 'learning_rate_exp': args.learning_rate_exp, + 'regularization_rate_exp': args.regularization_rate_exp + } + + output_dir = args.outputpath + + os.makedirs(output_dir, exist_ok=True) + + logfilename = os.path.join(output_dir, args.logfile) + # output_file = os.path.join(output_dir, args.out) + + FORMAT = '%(asctime)s %(message)s' + logging.basicConfig(format=FORMAT, + filename=logfilename, + filemode='w', + level=logging.INFO) + + print('Writing log file to {}'.format(logfilename)) + + t0 = time() + + training_windows_list = args.training_windows.split(',') + test_windows_list = args.test_windows.split(',') + + for t in training_windows_list: + assert os.path.exists(t) + + for t in test_windows_list: + assert os.path.exists(t) + + if len(set(test_windows_list) & set(training_windows_list)) == 0: + + train_and_test_model( + training_name=args.training_sample_name, + test_name=args.test_sample_name, + training_windows=training_windows_list, + test_windows=args.test_windowsr, + outDir=output_dir, + npz_mode=args.load_npz, + svtype=args.svtype + ) + + else: + + if args.cv == 'kfold': + + cross_validation( + training_windows=training_windows_list, + outDir=output_dir, + npz_mode=args.load_npz, + svtype=args.svtype, + kfold=args.kfold + ) + + elif args.cv == 'chrom': + + cross_validation_by_chrom( + training_windows=training_windows_list, + outDir=output_dir, + npz_mode=args.load_npz, + svtype=args.svtype, + chrlist=args.chrlist + ) + + # print('Elapsed time channel_maker_real on BAM %s and Chr %s = %f' % (args.bam, args.chr, time() - t0)) + logging.info('Elapsed time training and testing = %f seconds' % + (time() - t0)) + + +if __name__ == '__main__': + main() diff --git a/scripts/genome_wide/train_model_with_fit.py b/scripts/genome_wide/train_model_with_fit.py deleted file mode 100644 index fe3b189e..00000000 --- a/scripts/genome_wide/train_model_with_fit.py +++ /dev/null @@ -1,768 +0,0 @@ -from __future__ import (absolute_import, division, print_function, - unicode_literals) - -import argparse -import gzip -import json -import logging -import os -import sys -from collections import Counter -from time import time - -import bcolz -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd - -from tensorflow.keras.models import Sequential - -from tensorflow.keras.utils import to_categorical -from tensorflow.keras.models import load_model - -from tensorflow.keras.layers import Dense, Activation, Convolution1D, Lambda, \ - Convolution2D, Flatten, \ - Reshape, LSTM, Dropout, TimeDistributed, BatchNormalization - -from tensorflow.keras.regularizers import l2 -from tensorflow.keras.optimizers import Adam - -from tensorflow.keras.callbacks import (CSVLogger, EarlyStopping, ModelCheckpoint, - TensorBoard) - -from sklearn.model_selection import StratifiedKFold, train_test_split -from sklearn.utils import class_weight - -from model_functions import \ - evaluate_model # create_model_with_mcfly, train_model_with_mcfly - -# from numba import jit - -# gpu_options = tf.compat.v1.GPUOptions(allow_growth=True) -# sess = tf.compat.v1.Session( -# config=tf.compat.v1.ConfigProto(gpu_options=gpu_options, -# intra_op_parallelism_threads=0, -# inter_op_parallelism_threads=0, -# allow_soft_placement=True)) -# tf.compat.v1.keras.backend.set_session(sess) - - -def get_channel_labels(): - # Fill labels for legend - - labels = list() - labels.append("coverage") - labels.append("discordant_reads_F") - labels.append("discordant_reads_R") - labels.append("mean_read_quality") - - labels.append("median_base_quality") - labels.append("SNV_frequency") - - labels.append("#left_clipped_reads") - labels.append("#right_clipped_reads") - - labels.append("#left split reads") - labels.append("#right split reads") - - labels.append("#CIGAR_D_left_reads") - labels.append("#CIGAR_D_right_reads") - labels.append("#CIGAR_I_right_reads") - - labels.append("INV_before") - labels.append("INV_after") - labels.append("DUP_before") - labels.append("DUP_after") - labels.append("TRA_opposite") - labels.append("TRA_same") - - for direction in ['Forward', 'Reverse']: - for clipped in ['Left', 'Right', 'All']: - for value in ['median']: - # for value in ['median']: - labels.append(direction + '_' + clipped + - '_ClippedRead_distance_' + value) - - for clipped in ['L', 'R']: - for value in ['median']: - labels.append(clipped + '_SplitRead_distance_' + value) - - labels.append("Mappability") - - for nuc in ['A', 'T', 'C', 'G', 'N']: - labels.append("One_hot_encoding_" + nuc) - - # for k, l in enumerate(labels): - # print(str(k) + ': ' + l) - - return labels - - -def plot_channels(outDir, X, z, l): - mapclasses_rev = {v: k for k, v in mapclasses.items()} - - title_plot = mapclasses_rev[z] + '_' + str(l) - print('Plotting %s' % title_plot) - - number_channels = X.shape[1] - # print(number_channels) - label = get_channel_labels() - # print(len(label)) - - fig = plt.figure(figsize=(6, 4)) - fig.suptitle(mapclasses_rev[z] + ' ' + l, fontsize=20) - - for j in range(number_channels - 1, -1, -1): - - if sum(X[:, j]) != 0: - X_win = (X[:, j] - min(X[:, j])) / max(X[:, j]) - else: - X_win = X[:, j] - - Z = [x + j + 1 for x in X_win] - plt.plot(Z, label=label[j], linewidth=0.9) - plt.fill_between(Z, 0, interpolate=True) - plt.legend(bbox_to_anchor=(1.05, 1), - loc=2, - borderaxespad=0., - prop={'size': 5}) - plt.yticks(range(0, len(label) + 1, 1)) - plt.tick_params(axis='both', which='major', labelsize=5) - plt.axvline(x=200, color='r', linewidth=0.05, alpha=0.5) - plt.axvline(x=210, color='r', linewidth=0.05, alpha=0.5) - - plt.savefig(os.path.join(outDir, title_plot + '.png'), - format='png', - dpi=300, - bbox_inches='tight') - # plt.show() - plt.close() - - -def create_plots(sampleName, X_train, y_train, win_ids_train): - plots_dir = os.path.join(sampleName, 'plots') - os.makedirs(plots_dir, exist_ok=True) - - # idx_positive = [i for i, v in enumerate(y_train) if v == mapclasses['DEL']] - # # idx_positive = [i for i, v in enumerate(y_train) if v == mapclasses['DEL_start'] or v == mapclasses['DEL_end']] - # idx_negative = [i for i, v in enumerate(y_train) if v == mapclasses['noDEL']] - # - # for i in idx_positive[:10]: - # plot_channels(plots_dir, X_train[i], y_train[i], win_ids_train[i]) - # - # for i in idx_negative[:10]: - # plot_channels(plots_dir, X_train[i], y_train[i], win_ids_train[i]) - - -def get_hpc_flag(): - fileDir = os.path.dirname(os.path.abspath(__file__)) - parentDir = os.path.dirname(fileDir) - newPath = os.path.join(parentDir, 'genome_wide') - # print('appending path: ' + newPath) - sys.path.append(newPath) - # print(os.listdir(newPath)) - - from functions import get_config_file - - config = get_config_file() - HPC_MODE = config["DEFAULT"]["HPC_MODE"] - - return HPC_MODE - - -def get_data_dir(sampleName): - HPC_MODE = get_hpc_flag() - - channel_dir = \ - os.path.join('/Users/lsantuari/Documents/Processed/channel_maker_output', - sampleName) if not HPC_MODE else \ - os.path.join('/hpc/cog_bioinf/ridder/users/lsantuari/Processed/DeepSV/channel_data', - sampleName) - - return channel_dir - - -def get_labels(channel_data_dir, win): - label_file = os.path.join(channel_data_dir, 'labels_win' + str(win), - 'labels.json.gz') - - with gzip.GzipFile(label_file, 'r') as fin: - labels = json.loads(fin.read().decode('utf-8')) - - return labels - - -def get_data(out_dir, npz_mode, svtype): - def filter_labels(X, y, win_ids): - # print(y) - keep = [i for i, v in enumerate(y) if v in [svtype, 'no' + svtype]] - # print(keep) - X = X[np.array(keep)] - # print(y) - y = [y[i] for i in keep] - win_ids = [win_ids[i] for i in keep] - - print(Counter(y)) - return X, y, win_ids - - logging.info('Loading data for {}...'.format(out_dir)) - - y = [] - # numpy_array = [] - win_ids = [] - - # class_dict = {'positive': 'DEL', 'negative': 'noDEL'} - - # for label_type in ['positive', 'negative']: - - # fn = os.path.join(channel_dir, 'windows', label_type + '.hdf5') - # d = h5py.File(fn) - # - # fn = os.path.join(channel_dir, 'windows', label_type + '_labels.json.gz') - # - # with gzip.GzipFile(fn, 'r') as fin: - # labels = json.loads(fin.read().decode('utf-8')) - - for label_type in ['test']: - - if npz_mode: - - outfile = os.path.join(out_dir, 'windows', 'windows.npz') - npzfile = np.load(outfile, allow_pickle=True) - # print(sorted(npzfile.files)) - X = npzfile['data'] - labels = npzfile['labels'] - labels = labels.item() - - else: - - carray_file = os.path.join(out_dir, 'windows', - label_type + '_win200_carray') - logging.info('Loading file: {}'.format(carray_file)) - assert os.path.exists(carray_file), carray_file + ' not found' - X = bcolz.open(rootdir=carray_file) - - labels = X.attrs['labels'] - - # labels = get_labels(channel_dir, '200') - - y.extend(labels.values()) - win_ids.extend(labels.keys()) - - # if label_type == 'positive': - # - # numpy_array.append(d['data']) - # y.extend(labels.values()) - # win_ids.extend(labels.keys()) - # - # elif label_type == 'negative': - # - # rnd_idx = np.random.choice(d['data'].shape[0], size=numpy_array[0].shape[0], replace=True) - # - # labs = list(d['labels'].item().values()) - # labs_keys = list(d['labels'].item().keys()) - # - # # print(len(labs)) - # numpy_array.append(d['data'][rnd_idx]) - # y.extend(list(map(lambda i: labs[i], rnd_idx))) - # win_ids.extend(list(map(lambda i: labs_keys[i], rnd_idx))) - - # X = np.concatenate(numpy_array, axis=0) - - # Select only coverage, CR and SR channels - # X = X[:, :, np.array([0,6,7])] - - # X = X[:, :, np.array([0, 6, 7])] - - # if sampleName == 'NA12878': - # X = np.delete(X, 33, 2) - - logging.info(X.shape) - logging.info(Counter(y)) - - # mapclasses = {'DEL': 0, 'noDEL': 1, 'UK_other': 2, 'UK_single_partial': 3, 'UK_multiple_on_either_windows': 4} - # mapclasses = {'DEL': 0, 'noDEL': 1} - - X, y, win_ids = filter_labels(X, y, win_ids) - - # transform data from window pairs to single windows - # X = np.concatenate([X[:, :51, :], X[:, 60:, :]], axis=0) - # y = list(map(lambda x: x + '_start' if x == 'DEL' else x, y)) - # y.extend( - # list(map(lambda x: x + '_end' if x == 'DEL' else x, y)) - # ) - # print(Counter(y)) - # win_ids = win_ids + win_ids - - y = np.array([mapclasses[i] for i in y]) - win_ids = np.array(win_ids) - - # Shuffle arrays - # new_indices = np.arange(X.shape[0]) - # np.random.shuffle(new_indices) - # # print(new_indices) - # X = X[new_indices] - # y = y[new_indices] - # win_ids = win_ids[new_indices] - - logging.info('Data for {} loaded'.format(out_dir)) - - print(X.shape) - - return X, y, win_ids - - -def train_and_test_data(sampleName, npz_mode, svtype): - # Datasets - X, y, win_ids = get_data(sampleName, npz_mode, svtype) - - X = np.array(X) - y = np.array(y) - - # split into train/validation sets - X_train, X_test, y_train, y_test, win_ids_train, win_ids_test = train_test_split( - X, y, win_ids, test_size=0.3, random_state=2, stratify=y, shuffle=True) - - return X_train, X_test, y_train, y_test, win_ids_train, win_ids_test - - -def create_model(dim_length, dim_channels, class_number): - # layers = 2 - # filters = [4] * layers - # fc_hidden_nodes = 6 - # learning_rate = 4 - # regularization_rate = 1 - # kernel_size = 7 - # drp_out1 = 0 - # drp_out2 = 0 - - layers = 4 # 4 # 2 - filters = [8] * layers # 8 # 4 - fc_hidden_nodes = 16 # 8 - learning_rate = 10 ** (-4) - regularization_rate = 10 ** (-1) - kernel_size = 7 - drp_out1 = 0 - drp_out2 = 0 - - outputdim = class_number # number of classes - - weightinit = 'lecun_uniform' # weight initialization - - model = Sequential() - model.add(BatchNormalization(input_shape=(dim_length, dim_channels))) - - for filter_number in filters: - # model.add(MaxPooling1D(pool_size=5, strides=None, padding='same')) - - model.add( - Convolution1D(filter_number, - kernel_size=kernel_size, - padding='same', - kernel_regularizer=l2(regularization_rate), - kernel_initializer=weightinit)) - model.add(BatchNormalization()) - model.add(Activation('relu')) - - model.add(Flatten()) - model.add(Dropout(drp_out1)) - model.add( - Dense(units=fc_hidden_nodes, - kernel_regularizer=l2(regularization_rate), - kernel_initializer=weightinit)) # Fully connected layer - model.add(Activation('relu')) # Relu activation - model.add(Dropout(drp_out2)) - - # Adding one more FC layer - model.add( - Dense(units=fc_hidden_nodes, - kernel_regularizer=l2(regularization_rate), - kernel_initializer=weightinit)) # Fully connected layer - model.add(Activation('relu')) # Relu activation - - model.add(Dense(units=outputdim, kernel_initializer=weightinit)) - model.add(BatchNormalization()) - model.add(Activation("softmax")) # Final classification layer - - model.compile(loss='categorical_crossentropy', - optimizer=Adam(lr=learning_rate), - metrics=['accuracy']) - - # i = 0 - # for model, params, model_types in [model]: - # logging.info('model ' + str(i)) - # i = i + 1 - # logging.info(params) - # logging.info(model.summary()) - - return model - - -def train(sample_folder, model_fn, params, X_train, y_train, y_train_binary): - channel_data_dir = sample_folder # get_data_dir(sampleName) - - # win_len = 200 - # padding_len = 10 - # dim = win_len * 2 + padding_len - - # print(Counter(y_train)) - # class_weights = class_weight.compute_class_weight('balanced', - # np.unique(y_train), - # y_train) - # class_weights = dict(zip(np.unique(y_train), class_weights)) - # print(class_weights) - - # Balancing dataset - sampling = 'oversample' - - cnt_lab = Counter(y_train) - - # maximum training samples per class - max_train = 10 ** 5 - - min_v = min([v for k, v in cnt_lab.items()]) - max_v = max([v for k, v in cnt_lab.items()]) - - print(cnt_lab) - print('Minimum number of labels = ' + str(min_v)) - print('Maximum number of labels = ' + str(max_v)) - - data_balanced = [] - labels_balanced = [] - - for l in cnt_lab.keys(): - # print(l) - iw = np.where(y_train == l) - - if sampling == 'oversample': - ii = np.random.choice(a=iw[0], - size=min(max_v, max_train), - replace=True) - elif sampling == 'undersample': - ii = np.random.choice(a=iw[0], size=min_v, replace=False) - - data_balanced.extend(X_train[ii]) - labels_balanced.extend(y_train[ii]) - - print(Counter(labels_balanced)) - - X_train = np.array(data_balanced) - y_train = np.array(labels_balanced) - - y_train_binary = to_categorical(y_train, num_classes=params['n_classes']) - - # End balancing - - # X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, - # test_size=0.3, - # random_state=2, - # stratify=y_train, - # shuffle=True) - # - # y_train_binary = to_categorical(y_train, num_classes=params['n_classes']) - # y_val_binary = to_categorical(y_val, num_classes=params['n_classes']) - # - # model = create_model_with_mcfly(X_train, y_train_binary) - # - # history, model = train_model_with_mcfly(model, X_train, y_train_binary, - # X_val, y_val_binary) - - # Design model - logging.info('Creating model...') - model = create_model(params['dim'], params['n_channels'], - params['n_classes']) - - earlystop = EarlyStopping(monitor='val_loss', - min_delta=0, - patience=3, - verbose=1, - restore_best_weights=True) - - checkpoint = ModelCheckpoint(model_fn, - monitor='val_loss', - mode='min', - save_best_only=True, - verbose=1) - - csv_logger = CSVLogger(os.path.join(channel_data_dir, 'training.log')) - - tbCallBack = TensorBoard(log_dir=os.path.join(channel_data_dir, 'Graph'), - histogram_freq=0, - write_graph=True, - write_images=True) - - callbacks = [earlystop, tbCallBack, csv_logger, checkpoint] - - logging.info('Fitting model...') - - # Train model on dataset - history = model.fit( - X_train, - y_train_binary, - validation_split=params['val_split'], - batch_size=params['batch_size'], - epochs=params['epochs'], - shuffle=True, - # class_weight=class_weights, - verbose=1, - callbacks=callbacks) - - model = load_model(model_fn) - - return model, history, X_train.shape[0], int(X_train.shape[0] * - params['val_split']) - - -def cross_validation(sample_name, sample_folder, outDir, npz_mode, svtype, kfold): - - X, y, win_ids = get_data(os.path.join(outDir, '..'), npz_mode, svtype) - y_binary = to_categorical(y, num_classes=len(mapclasses.keys())) - - create_plots(sample_name, X, y, win_ids) - - # Instantiate the cross validator - skf = StratifiedKFold(n_splits=kfold, shuffle=True, random_state=1) - - # Loop through the indices the split() method returns - for index, (train_indices, test_indices) in enumerate(skf.split(X, y)): - print("Training on fold " + str(index + 1) + "/" + str(kfold) + "...") - - # Generate batches from indices - X_train, X_test = X[train_indices], X[test_indices] - y_train, y_test = y[train_indices], y[test_indices] - y_train_binary, y_test_binary = y_binary[train_indices], y_binary[ - test_indices] - win_ids_train, win_ids_test = win_ids[train_indices], win_ids[ - test_indices] - - batch_size = 32 - epochs = 10 - - # Parameters - params = { - 'dim': X_train.shape[1], - 'batch_size': batch_size, - 'epochs': epochs, - 'val_split': 0.1, - 'n_classes': len(mapclasses.keys()), - 'n_channels': X_train.shape[2], - 'shuffle': True - } - - model_fn = os.path.join(outDir, - 'model_train_cv' + str(index + 1) + '.hdf5') - - # if os.path.exists(model_fn): - # - # print('Model {} found. Loading model...'.format(model_fn)) - # model = load_model(model_fn) - # - # else: - - print('Training model on {}...'.format(sample_name)) - model, history, train_set_size, validation_set_size = train( - sample_folder, model_fn, params, X_train, y_train, y_train_binary) - - model.save(model_fn) - - results = pd.DataFrame() - - # X_test, y_test, win_ids_test = data(sampleName_test) - # ytest_binary = to_categorical(y_test, num_classes=len(mapclasses.keys())) - # print(win_ids_test[0]) - - # mapclasses = {'DEL': 0, 'noDEL': 1} - - outDit_eval = os.path.join( - outDir, 'train_' + sample_name + '_test_' + sample_name + '_cv' + - str(index + 1)) - - intermediate_results, metrics = evaluate_model(model, X_test, - y_test_binary, - win_ids_test, results, - 1, 'results', - mapclasses, outDit_eval, svtype) - - results = results.append(intermediate_results) - - results.to_csv(os.path.join(outDir, - 'train_' + sample_name + '_test_' + \ - sample_name + '_cv' + str(index + 1) + '_results.csv'), - sep='\t') - - -def train_and_test_model(training_name, test_name, training_folder, test_folder, outDir, - npz_mode, svtype): - if training_name == test_name: - X_train, X_test, y_train, y_test, win_ids_train, win_ids_test = train_and_test_data( - training_folder, npz_mode, svtype) - else: - X_train, y_train, win_ids_train = get_data(training_folder, npz_mode, svtype) - X_test, y_test, win_ids_test = get_data(test_folder, npz_mode, svtype) - - batch_size = 32 - epochs = 10 - - # Parameters - params = { - 'dim': X_train.shape[1], - 'batch_size': batch_size, - 'epochs': epochs, - 'val_split': 0.1, - 'n_classes': len(mapclasses.keys()), - 'n_channels': X_train.shape[2], - 'shuffle': True - } - - y_train_binary = to_categorical(y_train, num_classes=params['n_classes']) - y_test_binary = to_categorical(y_test, num_classes=params['n_classes']) - - model_fn = os.path.join( - outDir, 'model_train_' + training_name + '_test_' + - test_name + '.hdf5') - - # if os.path.exists(model_fn): - # - # print('Model {} found. Loading model...'.format(model_fn)) - # model = load_model(model_fn) - # - # else: - - print('Training model on {}...'.format(training_name)) - model, history, train_set_size, validation_set_size = train( - training_folder, model_fn, params, X_train, y_train, - y_train_binary) - - # model.save(model_fn) - - results = pd.DataFrame() - - # X_test, y_test, win_ids_test = data(sampleName_test) - # ytest_binary = to_categorical(y_test, num_classes=len(mapclasses.keys())) - # print(win_ids_test[0]) - - # mapclasses = {'DEL': 0, 'noDEL': 1} - - outDit_eval = os.path.join( - outDir, 'train_' + training_name + '_test_' + test_name) - - # intermediate_results, metrics = evaluate_model(model, X_test, ytest_binary, win_ids_test, - # results, 1, 'results', mapclasses, outDit_eval) - - intermediate_results, metrics = evaluate_model(model, X_test, - y_test_binary, win_ids_test, - results, 1, 'results', - mapclasses, outDit_eval, svtype) - - results = results.append(intermediate_results) - - results.to_csv(os.path.join( - outDir, 'train_' + training_name + '_test_' + test_name + - '_results.csv'), - sep='\t') - - # get_channel_labels() - - -def main(): - parser = argparse.ArgumentParser(description='Train and test model') - parser.add_argument( - '-p', - '--outputpath', - type=str, - default='/Users/lsantuari/Documents/Processed/channel_maker_output', - help="Specify output path") - parser.add_argument('-t', - '--training_sample_folder', - type=str, - default='CHM1_CHM13', - help="Specify training sample") - parser.add_argument('-x', - '--test_sample_folder', - type=str, - default='NA24385', - help="Specify training sample") - parser.add_argument('-tn', - '--training_sample_name', - type=str, - default='CHM1_CHM13', - help="Specify training sample") - parser.add_argument('-xn', - '--test_sample_name', - type=str, - default='NA24385', - help="Specify training sample") - parser.add_argument('-l', - '--logfile', - default='windows.log', - help='File in which to write logs.') - parser.add_argument('-s', - '--svtype', - type=str, - default='INS', - help="Specify SV type") - parser.add_argument('-m', - '--mode', - type=str, - default='training', - help="training/test mode") - parser.add_argument('-k', - '--kfold', - type=int, - default=3, - help="Specify [k]-fold cross validation") - parser.add_argument('-npz', - '--load_npz', - type=bool, - default=True, - help="load npz?") - - args = parser.parse_args() - - cmd_name = 'train_model_with_fit' - - global mapclasses - mapclasses = {args.svtype: 0, 'no' + args.svtype: 1} - - output_dir = os.path.join(args.outputpath, cmd_name) - - os.makedirs(output_dir, exist_ok=True) - - logfilename = os.path.join(output_dir, args.logfile) - # output_file = os.path.join(output_dir, args.out) - - FORMAT = '%(asctime)s %(message)s' - logging.basicConfig(format=FORMAT, - filename=logfilename, - filemode='w', - level=logging.INFO) - - print('Writing log file to {}'.format(logfilename)) - - t0 = time() - - if args.training_sample_name != args.test_sample_name: - - train_and_test_model(training_name=args.training_sample_name, - test_name=args.test_sample_name, - training_folder=args.training_sample_folder, - test_folder=args.test_sample_folder, - outDir=output_dir, - npz_mode=args.load_npz, - svtype=args.svtype) - else: - - cross_validation(sample_name=args.training_sample_name, - sample_folder=args.training_sample_folder, - outDir=output_dir, - npz_mode=args.load_npz, - svtype=args.svtype, - kfold=args.kfold) - - # print('Elapsed time channel_maker_real on BAM %s and Chr %s = %f' % (args.bam, args.chr, time() - t0)) - logging.info('Elapsed time training and testing = %f seconds' % - (time() - t0)) - - -if __name__ == '__main__': - main() diff --git a/scripts/utils/README.md b/scripts/utils/README.md deleted file mode 100644 index 0313ae56..00000000 --- a/scripts/utils/README.md +++ /dev/null @@ -1,25 +0,0 @@ -# Test data - -**1. Install dependencies.** - -```bash -# create & activate new env -conda env create -n utils -f environment.yaml -conda activate utils -``` -**2. Download the hs37d5 reference genome.** - -```bash -wget ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5.fa.gz -gzip -d hs37d5.fa.gz -``` - -**3. Generate mappability track.** - -```bash -./create_test_data.sh hs37d5.fa 100 test_data -``` - -**4. Generate artificial short-read alignments with SVs.** - -https://github.com/GooglingTheCancerGenome/sv-gen diff --git a/scripts/utils/bedpe_to_vcf.py b/scripts/utils/bedpe_to_vcf.py new file mode 100755 index 00000000..d75ca40c --- /dev/null +++ b/scripts/utils/bedpe_to_vcf.py @@ -0,0 +1,229 @@ +#!/usr/bin/env python3 + +""" +Adapted from https://git.wur.nl/wijfj001/hecaton/-/blob/master/scripts/convert/bedpe_to_vcf.py +Convert BEDPE file containing SV calls to VCF +""" + +import argparse +import datetime +import pandas as pd +import sys +import os +import twobitreader as twobit + +working_dir = '/Users/lsantuari/Documents/Projects/GTCG/sv-channels/manuscript/Figures/F2/' + +rev_bases = {'A': 'T', 'T': 'A', 'G': 'C', 'C': 'G'} + + +def parse_cl_args(in_args): + """ + Parse command line arguments + + :param in_args: All command line arguments + :return: None + """ + description = "Convert BEDPE file containing CNV calls to VCF" + parser = argparse.ArgumentParser(description=description) + parser.add_argument("-i", "--input_bedpe", type=str, + default=os.path.join(working_dir, 'simulated-data-results/coverage/cov30/hmz-sv/sv-channels.split_reads.bedpe'), + #default=os.path.join(working_dir, + # 'simulated-data-results/coverage/cov30/hmz-sv/hmz-sv.bedpe'), + help="Path to BEDPE file") + parser.add_argument("-b", "--twobit", type=str, + default=os.path.join(working_dir, 'helpers/aux_files/hs37d5.2bit'), + help="Path to 2bit file") + parser.add_argument("-o", "--output_vcf", type=str, + default=os.path.join(working_dir, 'simulated-data-results/coverage/cov30/hmz-sv/sv-channels.split_reads.vcf'), + #default=os.path.join(working_dir, + # 'simulated-data-results/coverage/cov30/hmz-sv/hmz-sv.new.vcf'), + help="Name of VCF output file") + parser.add_argument("-s", "--sample_name", type=str, + default='HTZ-SV', + help="Name of the sample") + args = parser.parse_args(in_args) + return args + + +def convert_bedpe(input_bedpe_fn, output_vcf_fn, sample_name, genome): + """Convert BEDPE file containing SV calls to VCF + + :param input_bedpe_fn: Path to input BEDPE file + :param output_vcf_fn: Path to output VCF file + :param sample_name: Name of the sample + :return: 0 (integer) + """ + + # write VCF header to output file + vcf_header_elems = ["##fileformat=VCFv4.2", + "##fileDate={}".format(datetime.date.today().strftime("%Y%m%d")), + "##FILTER=<ID=PASS,Description=\"All filters passed\">", + "##ALT=<ID=DEL,Description=\"Deletion\">", + "##ALT=<ID=INS,Description=\"Novel sequence insertion\">", + "##ALT=<ID=INV,Description=\"Inversion\">", + "##ALT=<ID=DUP,Description=\"Duplication\">", + "##ALT=<ID=BND,Description=\"Inter-chromosomal translocation\">", + "##FORMAT=<ID=SVTYPE,Number=1,Type=String,Description=\"Type of structural variant\">", + "##FORMAT=<ID=CHR2,Number=1,Type=Float,Description=\"Stop chromosome of the interval\">", + "##FORMAT=<ID=END,Number=1,Type=Float,Description=\"Stop position of the interval\">", + "##INFO=<ID=MATEID,Number=1,Type=String,Description=\"ID of partner breakend\">", + "##INFO=<ID=END,Number=1,Type=Integer,Description=\"Stop position of the interval\">", + '##INFO=<ID=SVLEN,Number=1,Type=Integer,Description=' + + '"Difference in length between REF and ALT alleles">', + "##INFO=<ID=SVTYPE,Number=1,Type=String,Description=\"Type of structural variant\">"] + + for k in sorted(genome.keys()): + vcf_header_elems.append("##contig=<ID={},length={}>".format(k, len(genome[k]))) + + vcf_header_elems.append("#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\t{}".format(sample_name)) + + vcf_header = "\n".join(vcf_header_elems) + + output_vcf = open(output_vcf_fn, "w") + output_vcf.write(vcf_header) + output_vcf.write("\n") + + # load bedpe file + calls_index = 1 + + if os.path.exists(input_bedpe_fn): + input_bedpe = pd.read_csv(input_bedpe_fn, sep="\t", header=None) + else: + print('{} does not exist!'.format(input_bedpe_fn)) + exit() + + # loop through bedpe file writing each variant to the vcf + for sv_calls in input_bedpe.itertuples(index=False, name='Pandas'): + + # print(sv_calls) + + chrom = str(sv_calls[0]) + start = str(sv_calls[1]) + + chrom2 = str(sv_calls[3]) + end = str(sv_calls[4]) + + svlen = sv_calls[4] - sv_calls[1] + + svtype = str(sv_calls[6]) + svtype = svtype.split('_')[1] if svtype not in ['DEL', 'INS', 'INV', 'DUP', 'CTX', 'TRA'] else svtype + + qual = str(sv_calls[7]) if len(sv_calls) > 7 else '1' + + filtered = "PASS" + + # extract format field elements + format_field = "GT" + sample_field_elems = ["1/1"] + sample_field = ":".join(sample_field_elems) + + ref = genome[str(chrom)][sv_calls[1]].upper() + + if svtype not in ['TRA', 'CTX']: + + identifier = sample_name+'_' + str(calls_index) + + alt = '<' + svtype + '>' + + if svtype == 'INS': + info_field = "SVTYPE={};END={};SVLEN={}".format(svtype, str(sv_calls[1]+1), svlen) + else: + info_field = "SVTYPE={};END={};SVLEN={}".format(svtype, end, svlen) + + # create new line for variant + variant_line_elems = [chrom, start, identifier, ref, alt, qual, + filtered, info_field, format_field, sample_field] + variant_line = "\t".join(variant_line_elems) + output_vcf.write(variant_line) + output_vcf.write("\n") + + calls_index += 1 + + else: + + def get_identifier(calls_index, j): + identifier = '_'.join((sample_name, str(calls_index), str(j))) + return identifier + + # Writing a breakpoint of an inter-chromosomal translocation as in BND format + + j = 1 + + bp1_pos = sv_calls[1] + bp2_pos = sv_calls[4] + svlen = abs(bp2_pos-bp1_pos) if chrom == chrom2 else -1 + + brkt_fw = ']' + brkt_bw = '[' + + bp1_ref_fw = genome[str(chrom)][bp1_pos].upper() + bp1_ref_rv = rev_bases[bp1_ref_fw] + + bp1_alt_1 = bp1_ref_fw + brkt_bw + chrom2 + ':' + str(bp2_pos) + brkt_bw + bp1_alt_2 = brkt_fw + chrom2 + ':' + str(bp2_pos) + brkt_fw + bp1_ref_rv + + # bp1_1 + bp1_1_info_field = "SVTYPE={};MATEID={}".format('BND', get_identifier(calls_index, 4)) + variant_line_elems = [chrom, start, get_identifier(calls_index, j), bp1_ref_fw, bp1_alt_1, qual, + filtered, bp1_1_info_field, format_field, sample_field] + variant_line = "\t".join(variant_line_elems) + output_vcf.write(variant_line) + output_vcf.write("\n") + + j += 1 + + # bp1_2 + bp1_2_info_field = "SVTYPE={};MATEID={}".format('BND', get_identifier(calls_index, 3)) + variant_line_elems = [chrom, start, get_identifier(calls_index, j), bp1_ref_rv, bp1_alt_2, qual, + filtered, bp1_2_info_field, format_field, sample_field] + variant_line = "\t".join(variant_line_elems) + output_vcf.write(variant_line) + output_vcf.write("\n") + + j += 1 + + bp2_ref_fw = genome[str(chrom2)][bp2_pos].upper() + bp2_ref_rv = rev_bases[bp2_ref_fw] + + bp2_alt_1 = bp2_ref_rv + brkt_bw + chrom + ':' + str(bp1_pos) + brkt_bw + bp2_alt_2 = brkt_fw + chrom + ':' + str(bp1_pos) + brkt_fw + bp1_ref_fw + + # bp2_1 + bp2_1_info_field = "SVTYPE={};MATEID={}".format('BND', get_identifier(calls_index, 2)) + variant_line_elems = [chrom2, end, get_identifier(calls_index, j), bp2_ref_rv, bp2_alt_1, qual, + filtered, bp2_1_info_field, format_field, sample_field] + variant_line = "\t".join(variant_line_elems) + output_vcf.write(variant_line) + output_vcf.write("\n") + + j += 1 + + # bp2_2 + bp2_2_info_field = "SVTYPE={};MATEID={}".format('BND', get_identifier(calls_index, 1)) + variant_line_elems = [chrom2, end, get_identifier(calls_index, j), bp2_ref_fw, bp2_alt_2, qual, + filtered, bp2_2_info_field, format_field, sample_field] + variant_line = "\t".join(variant_line_elems) + output_vcf.write(variant_line) + output_vcf.write("\n") + + j += 1 + + calls_index += 1 + + output_vcf.close() + + return 0 + + +def main(): + args = parse_cl_args(sys.argv[1:]) + + genome = twobit.TwoBitFile(args.twobit) + + # convert vcf file + convert_bedpe(args.input_bedpe, args.output_vcf, args.sample_name, genome) + + +if __name__ == "__main__": + main() diff --git a/scripts/utils/split_vcf_by_sr.py b/scripts/utils/split_vcf_by_sr.py new file mode 100644 index 00000000..d62a6a9c --- /dev/null +++ b/scripts/utils/split_vcf_by_sr.py @@ -0,0 +1,242 @@ +#!/usr/bin/env python3 + +""" +Split VCF into SVs that overlaps SR positions and SVs that do not +""" + +import argparse +import os +import sys +import gzip +from intervaltree import IntervalTree +from collections import defaultdict +from time import time + +sys.path.append('../genome_wide/') + +from functions import * +from label_windows import * + + +def parse_cl_args(in_args, caller): + """ + Parse command line arguments + + :param in_args: All command line arguments + :return: None + """ + + if caller == 'test': + mypath = os.path.join(pathout, 'test.bedpe') + else: + mypath = os.path.join(pathout, caller + '.vcf') + description = "Split VCF by overlap on split read positions" + parser = argparse.ArgumentParser(description=description) + parser.add_argument("-i", "--input", type=str, + default=mypath, + help="VCF input file") + parser.add_argument("-o", "--output", type=str, + default=os.path.join(pathout, caller+'_sr.vcf'), + help="VCF output file") + parser.add_argument("-n", "--output_nosr", type=str, + default=os.path.join(pathout, caller+'_nosr.vcf'), + help="VCF output file") + parser.add_argument("-w", "--win", type=int, + default=200, + help="VCF output file") + parser.add_argument("-s", "--split_reads", type=str, + default=os.path.join(pathout, 'split_reads.bedpe.gz'), + help="BEDPE file with split read positions") + parser.add_argument('-sv', + '--svtype', + type=str, + default='DEL', + help="Specify SV type") + args = parser.parse_args(in_args) + return args + + +def get_srpos_from_bedpe(inbedpe): + + # Check file existence + assert os.path.isfile(inbedpe), inbedpe + ' not found!' + # Dictionary with chromosome keys to store SVs + srpos = [] + + with gzip.GzipFile(inbedpe, 'rb') as fin: + + for line in fin.readlines(): + columns = line.decode('utf8').rstrip().split("\t") + # print(columns) + chrom1, pos1_start, pos1_end = str(columns[0]), int( + columns[1]), int(columns[2]) + chrom2, pos2_start, pos2_end = str(columns[3]), int( + columns[4]), int(columns[5]) + svtype = columns[6] + # print('{}:{}-{}:{}'.format(chrom1, pos1_start, chrom2, pos2_start)) + srpos.append((chrom1, pos1_start, chrom2, pos2_start)) + + # print('counting {} split read positions'.format(len(srpos))) + # print(sv_list) + + return srpos + + +def create_gtrees(sv_list): + + print('Building SV GenomicTrees...') + # Tree with windows for candidate positions + trees_start = defaultdict(IntervalTree) + trees_end = defaultdict(IntervalTree) + + # Populate tree + for i, sv in enumerate(sv_list, start=0): + chrom1, pos1_start, pos1_end, chrom2, pos2_start, pos2_end, svtype = sv + sv_id = '_'.join( + (chrom1, str(pos1_start), chrom2, str(pos2_start))) + + trees_start[chrom1][pos1_start:pos1_end] = (i, sv_id) + trees_end[chrom2][pos2_start:pos2_end] = (i, sv_id) + + # print('Tree start') + # for k in trees_start.keys(): + # print('{} : {}'.format( k, len(trees_start[k]))) + # print('Tree end') + # for k in trees_end.keys(): + # print('{} : {}'.format( k, len(trees_end[k]))) + + return trees_start, trees_end + + +def search_tree_with_bedpe(cpos, trees_start, trees_end, win_hlen): + + print('Searching SV GenomicTrees with candidate positions...') + + lookup_start = [] + lookup_end = [] + + # Log info every n_r times + n_r = 10**4 + last_t = time() + + for i, p in enumerate(cpos, start=1): + + # if not i % n_r: + # now_t = time() + # # print(type(now_t)) + # print( + # "%d candidate positions processed (%f positions / s)" % + # (i, n_r / (now_t - last_t))) + # last_t = time() + + chrom1, pos1, chrom2, pos2 = p + lookup_start.append(trees_start[chrom1][pos1 - win_hlen:pos1 + win_hlen + 1]) + lookup_end.append(trees_end[chrom2][pos2 - win_hlen:pos2 + win_hlen + 1]) + + return lookup_start, lookup_end + + +def main(): + + srpos = get_srpos_from_bedpe(os.path.join(pathout, 'split_reads.bedpe.gz')) + + for caller in ['manta', 'gridss', 'lumpy', 'delly', 'test']: + #for caller in ['manta']: + + print('Considering {}:'.format(caller)) + + args = parse_cl_args(sys.argv[1:], caller) + + win_hlen = int(args.win / 2) if args.win % 2 == 0 else int((args.win + 1) / 2) + + filename, file_extension = os.path.splitext(args.input) + + if file_extension == '.vcf': + sv_list = read_vcf(args.input) + sv_list = [i for i in sv_list if i[-1] == args.svtype] + elif file_extension == '.bedpe': + sv_list = read_bedpe(args.input, args.svtype) + + # print(len(sv_list)) + trees_start, trees_end = create_gtrees(sv_list) + + #print(trees_start) + #print(trees_end) + + lookup_start, lookup_end = search_tree_with_bedpe(srpos, trees_start, trees_end, win_hlen) + + #print(lookup_start) + + idx = [] + + for i, x in enumerate(zip(srpos, lookup_start, lookup_end), start=0): + + p, lu_start, lu_end = x + + l1 = len(lu_start) + l2 = len(lu_end) + + #print('{} <=> {}' .format(l1, l2)) + + if l1 > 0 and l2 > 0: + + lu_start_set = [] + lu_end_set = [] + + for s in lu_start: + lu_start_elem_start, lu_start_elem_end, lu_start_elem_data = s + lu_start_i, lu_start_elem_svid = lu_start_elem_data + lu_start_set.append(lu_start_i) + + for s in lu_end: + lu_end_elem_start, lu_end_elem_end, lu_end_elem_data = s + lu_end_i, lu_end_elem_svid = lu_end_elem_data + lu_end_set.append(lu_end_i) + + olap = set(lu_start_set) & set(lu_end_set) + + if len(olap) > 0: + # print('{} => {}'.format(p, lu_start_set & lu_end_set)) + idx.extend(olap) + + print('list of indices: {}'.format(len(idx))) + + out = open(args.output, 'w') + out_nosr = open(args.output_nosr, 'w') + j = 0 + sr_lines = nosr_lines = 0 + + with open(args.input, 'r') as fin: + + for line in fin.readlines(): + if line[0] == '#': + out.write(line) + out_nosr.write(line) + else: + if j in set(idx): + out.write(line) + sr_lines += 1 + else: + out_nosr.write(line) + nosr_lines += 1 + j += 1 + + + print('{} SVs => with SR:{}({}%); without:{}({}%)'.format( + sr_lines+nosr_lines, + sr_lines, + sr_lines/(sr_lines+nosr_lines)*100, + nosr_lines, + nosr_lines / (sr_lines + nosr_lines)*100, + )) + + out.close() + out_nosr.close() + + +if __name__ == "__main__": + + global pathout + pathout = '/Users/lsantuari/Documents/Processed/SPIDER/split_reads/NA12878' + + main() diff --git a/scripts/utils/visualization/plot_window.py b/scripts/utils/visualization/plot_window.py new file mode 100644 index 00000000..7b00e27a --- /dev/null +++ b/scripts/utils/visualization/plot_window.py @@ -0,0 +1,120 @@ +import numpy as np +import pandas as pd +from sklearn.preprocessing import minmax_scale +from matplotlib import colors +import matplotlib.pyplot as plt +import argparse +from collections import Counter +import os + + +def get_data(windows_list): + + X = [] + y = [] + win_ids = [] + + for t in windows_list: + + print('Loading data from {}...'.format(t)) + + npzfile = np.load(t, allow_pickle=True) + + X.extend(npzfile['data']) + labels = npzfile['labels'] + labels = labels.item() + + y.extend(labels.values()) + win_ids.extend(labels.keys()) + + print('Data from {} loaded'.format(t)) + + X = np.stack(X, axis=0) + + print('X shape:{}'.format(X.shape)) + print('y:{}'.format(Counter(y))) + + return X, y, win_ids + + +def plot_window(X, y, w, idx, outdir): + + W_i = minmax_scale(X[idx, :, :], feature_range=(0, 1), axis=0, copy=True) + + df = pd.DataFrame(W_i, columns=np.arange(W_i.shape[1])) + # print(df) + ax = df.plot(subplots=True, + figsize=(20, 5), + kind='line', + legend=False, + color='black') + + for i, a in enumerate(ax): + if i != W_i.shape[1] - 1: + a.spines['bottom'].set_color('white') + if i != 0: + a.spines['top'].set_color('white') + a.set_yticks([]) + a.set_ylim(0, 1) + + plt.tight_layout() + + img_type = 'jpg' + figname = '.'.join([w[idx], y[idx], img_type]) + plt.savefig(os.path.join(outdir, figname), dpi=300, format=img_type) + + +def main(): + + default_win = 200 + default_path = os.path.join('../genome_wide/cnn/win'+str(default_win), 'split_reads') + def_windows_file = os.path.join(default_path, 'windows', 'DEL', 'windows_en.npz') + + parser = argparse.ArgumentParser(description='Use model to predict') + + parser.add_argument('-i', + '--input', + type=str, + default=def_windows_file, + help="Specify list of windows" + ) + parser.add_argument('-j', + '--index', + type=int, + default=1, + help="Specify index of window to plot" + ) + parser.add_argument('-s', + '--svtype', + type=str, + default='DEL', + help="Specify SV type") + parser.add_argument('-c', + '--class_label', + type=str, + default='DEL', + help="Specify class") + parser.add_argument('-o', + '--output_dir', + type=str, + default='plots', + help="Specify output directory") + + args = parser.parse_args() + + #grep 'DEL' test.bedpe | awk '{print $1"\t"$2"\t"$3"\n"$4"\t"$5"\t"$6}' > test.igv.DEL.bed + + os.makedirs(os.path.join(args.output_dir, args.svtype), exist_ok=True) + + X, y, win_ids = get_data([args.input]) + + sv_i = [i for i in np.arange(len(y)) if y[i] == args.class_label] + + for i in sv_i: + if win_ids[i] == '12_1053781_12_1054233_+-': + plot_window(X, y, win_ids, i, args.output_dir) + + +if __name__ == "__main__": + main() +