Skip to content

Latest commit

 

History

History
260 lines (196 loc) · 13.7 KB

README.md

File metadata and controls

260 lines (196 loc) · 13.7 KB
Raw

Introgression Viewer Saulo Aflitos - 2013-2015 [email protected] Cluster Bioinformatics Plant Research International - PRI Wageningen University and Research Centre - WageningenUR

Travis Continuous Integration Build Status

Manual and Wiki: https://github.com/sauloal/introgressionbrowser/wiki

  1. Introduction

  2. Methodology

  3. Installation 3.1. Clone Introgression browser 3.1.1. Processing 3.1.2. Server 3.2. Install dependencies 3.2.2. Server 3.2.3. Server add-ons 3.2.4. System

  4. Run 4.1. Processing 4.2. Server

  5. Run 5.1. Processing 5.2. Server

  6. Acknowledgements

  7. Introduction

  8. Methodology This set of scripts takes as input a series of Variant Call Files (VCF) of species mapped against a single reference.

After a series of conversions, all homozigous Single Nucleotide Polymorphisms (SNP) are extracted while ignoring heterozigous SNPS (hetSNP), Multiple Nucleotide Polymorphisms (MNP) and Insertion/Deletion events (InDel).

For each individual, the reference's nucleotide will be assigned unless a SNP is presented. If any individual has a MNP, hetSNP or InDel at a given position, this position is skipped entirely.

A General Feature Format (GFF) describing coordinates is used to split the genome into segments. Those segments can be genes, even sized fragments (10kb, 50kb, etc) or particular segments of interest as long as the coordinates are the same as the VCF files. A auxiliary script is provided to generate evenly sized segments.

For each selected segment a fasta file will be generated and FastTree will create a distance matrix and a Newick Tree. After all data has been processed, the three files (fasta, matrix and newick) will be read and converted to a database.

The webserver scripts will read and serve the data to a web browser. There are three scripts, a main script serves the data and two auxiliary servers to perform on-the-fly clustering and image conversion (from SVG to PNG).

  1. Installation 3.1. Clone Introgression browser Clone or download Introgression Browser.

3.1.1. Processing Add your files under data subfolder. Add vcfmerger subfolder to your path or create symbolic links inside the data folder.

3.1.2. Server Get a database and modify config.py accordingly. The databases are are multiplatform.

3.2. Install dependencies 3.2.1. Processing Check if FastTree runs in your machine (Linux only) Install python dependencies: sqlalchemy Install pypy (Optional but speeds up analysis)

3.2.2. Server Install python Install python dependencies: sqlalchemy - already in virtualenv flask - already in virtualenv #py-editdist - already in vcfmerger/aux/ - wget 'https://py-editdist.googlecode.com/files/py-editdist-0.3.tar.gz' #fastcluster pymix

3.2.3. Server add-ons Install python dependencies: on-the-fly clustering: numpy scipy PNG download: wand

3.2.4. System apt-get update apt-get install build-essential checkinstall apt-get install python-setuptools python-dev apt-get install python-numpy python-scipy python-matplotlib python-pandas python-sympy apt-get install libmagickwand-dev apt-get install sqlite3 libsqlite3-dev

easy_install pip
easy_install flask
easy_install sqlalchemy
easy_install wand

3.2.5. Vistualization 3.2.5.1. VirtualBox share your data folder as "data" shared folder wget http://download.virtualbox.org/virtualbox/4.3.6/VBoxGuestAdditions_4.3.6.iso mkdir vbox mount VBoxGuestAdditions_4.3.6.iso vbox cd vbox ./VBoxLinuxAdditions.run cd .. umount vbox edit /etc/fstab adding data /media/data vboxsf re 0 0 mount -a ls /media/data

3.2.5.1. VMware share your data folder as "data" shared folder attach the VMware tools mkdir /mnt/cdrom mount /dev/cdrom /mnt/cdrom mkdir ~/vm cd ~/vm tar xvf /dev/cdrom/VMwareTools-9.6.1-1378637.tar.gz cd vmware-tools-distrib ./vmware-install.pl -d cd ../.. rm -rf vm ls /mnt/hgfs/data

  1. Configuration 4.1. Processing Inside the "data" folder create a tab delimited file containing path to the VCF files and the "pretty" name for them. The first column is ignored. 1 input/RF_001_SZAXPI008746-45.vcf.gz Moneymaker (001) 0 input/RF_002_SZAXPI009284-57.vcf.gz Alisa Craig (002) 0 input/RF_003_SZAXPI009285-62.vcf.gz Gardeners Delight (003)

4.2. Server Edit config.py to create users, configure the server's port, the encryption key, describe available databases and decide whether to use SQL ot RAM database.

  1. Run 5.1. Processing Merge VCF files: vcfmerger/vcfmerger.py short.lst OUTPUT: short.lst.vcf.gz #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT FILENAMES SL2.40ch00 280 . A C . PASS NV=1;NW=1;NS=1;NT=1;NU=1 FI S cheesemaniae (055) SL2.40ch00 284 . A G . PASS NV=1;NW=1;NS=1;NT=1;NU=1 FI S cheesemaniae (054) SL2.40ch00 316 . C T . PASS NV=1;NW=1;NS=1;NT=1;NU=1 FI S arcanum (059) SL2.40ch00 323 . C T . PASS NV=1;NW=1;NS=1;NT=1;NU=1 FI S arcanum (059) SL2.40ch00 332 . A T . PASS NV=1;NW=1;NS=1;NT=1;NU=1 FI S pimpinellifolium (047) SL2.40ch00 362 . G T . PASS NV=1;NW=1;NS=1;NT=1;NU=1 FI S galapagense (104) SL2.40ch00 385 . A C . PASS NV=1;NW=1;NS=1;NT=1;NU=1 FI S neorickii (056) SL2.40ch00 391 . C T . PASS NV=1;NW=1;NS=6;NT=6;NU=6 FI S chiemliewskii (052),S neorickii (056),S arcanum (059),S habrochaites glabratum (066),S habrochaites glabratum (067),S habrochaites (072)

    Simplify merged VCF deleting hetSNP, MNP and InDels: vcfmerger/vcfsimplify.py short.lst.vcf.gz OUTPUT: short.lst.vcf.gz.filtered.vcf.gz SL2.40ch00 391 . C T . PASS NV=1;NW=1;NS=6;NT=6;NU=6 FI S arcanum (059),S chiemliewskii (052),S habrochaites (072),S habrochaites glabratum (066),S habrochaites glabratum (067),S neorickii (056) SL2.40ch00 416 . T A . PASS NV=1;NW=1;NS=6;NT=6;NU=6 FI S arcanum (059),S chiemliewskii (052),S habrochaites (072),S habrochaites glabratum (066),S habrochaites glabratum (067),S neorickii (056) SL2.40ch00 424 . C T . PASS NV=1;NW=1;NS=5;NT=5;NU=5 FI LA0113 (039),S cheesemaniae (054),S pimpinellifolium (044),S pimpinellifolium unc (045),S pimpinellifolium (047)

    Generate even sized fragments (if needed): vcfmerger/aux/fasta_spacer.py GENOME.fa 50000 OUTPUT: GENOME.fa.50000.gff SL2.40ch00 . fragment_10000 1 10000 . . . Alias=Frag_SL2.40ch00g10000_1;ID=fragment:Frag_SL2.40ch00g10000_1;Name=Frag_SL2.40ch00g10000_1;length=10000;csize=21805821 SL2.40ch00 . fragment_10000 10001 20000 . . . Alias=Frag_SL2.40ch00g10000_2;ID=fragment:Frag_SL2.40ch00g10000_2;Name=Frag_SL2.40ch00g10000_2;length=10000;csize=21805821

    Filter with gff: vcfmerger/vcffiltergff.py -k -f PROJNAME -g GENOME.fa_50000.gff -i short2.lst.vcf.gz.simplified.vcf.gz 2>&1 | tee short2.lst.vcf.gz.simplified.vcf.gz.log OUTPUT: #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT FILENAMES SL2.40ch00 391 . C T . PASS NV=1;NW=1;NS=6;NT=6;NU=6 FI S arcanum (059),S chiemliewskii (052),S habrochaites (072),S habrochaites glabratum (066),S habrochaites glabratum (067),S neorickii (056)

    Concatenate the SNPs of each fragment into FASTA: find PROJNAME -name '*.vcf.gz' | xargs -I{} -P50 bash -c 'vcfmerger/vcfconcat.py -f -i {} 2>&1 | tee {}.concat.log' OUTPUT: PROJNAME/CHROMOSOME/short2.lst.vcf.gz.simplified.vcf.gz.filtered.vcf.gz.SL2.40ch01.000090300001-000090310000.Frag_SL2.40ch01g10000_9031.vcf.gz.SL2.40ch01.fasta >Moneymaker_001 ATAATCTAGCTGGAACCCTTGTTTTTCTCGCGATTGGGGTTCAAGTGCACACCACATGTC AGGGA >Alisa_Craig_002 ATAATCTAGCTGGAACCCTTGTTTTTCTTGCGATTGGGGTTCAAGTGCGCGCTGCGTGAC AGGAA

    Run FastTree in each of the FASTA files: export OMP_NUM_THREADS=3 find PROJNAME -name '*.fasta' | sort | xargs -I{} -P30 bash -c 'vcfmerger/aux/FastTreeMP -fastest -gamma -nt -bionj -boot 100 -log {}.tree.log -out {}.tree {}' OUTPUT: PROJNAME/CHROMOSOME/short2.lst.vcf.gz.simplified.vcf.gz.filtered.vcf.gz.SL2.40ch01.000090300001-000090310000.Frag_SL2.40ch01g10000_9031.vcf.gz.SL2.40ch01.fasta.tree ((((Dana_018:0.0,Belmonte_033:0.0):0.00054,((TR00026_102:0.01587,(PI272654_023:0.03426,(((S_huaylasense_063:0.00054,((Lycopersicon_sp_025:0.0,S_chilense_065:0.0):0.00054,S_chilense_064:0.01555)0.780:0.01548)0.860:0.01547,((S_peruvianum_new_049:0.0,S_chiemliewskii_051:0.0,S_chiemliewskii_052:0.0,S_cheesemaniae_053:0.0,S_cheesemaniae_054:0.0,S_neorickii_056:0.0,S_neorickii_057:0.0,S_peruvianum_060:0.0,S_habrochaites_glabratum_066:0.0,S_habrochaites_glabratum_068:0.0,S_habrochaites_070:0.0,S_habrochaites_071:0.0,S_habrochaites_072:0.0,S_pennellii_073:0.0,S_pennellii_074:0.0,TR00028_LA1479_105:0.0,ref:0.0):0.00054,((S_arcanum_058:0.01482,(S_huaylasense_062:0.08258,S._arcanum_new_075:0.00054)0.880:0.03260)0.960:0.04917,(((Gardeners_Delight_003:0.00054,(Katinka_Cherry_007:0.0,Trote_Beere_016:0.0,Winter_Tipe_031:0.0):0.01559)0.900:0.03206,(PI129097_022:0.00054,(S_galapagense_104:0.04782,(LA0113_039:0.01223,((S_pimpinellifolium_047:0.01628,(S_arcanum_059:0.00055,(S_habrochaites_glabratum_067:0.01562,S_habrochaites_glabratum_069:0.01562)1.000:0.08287)0.920:0.04857)0.670:0.01186,S_habrochaites_042:0.03551)0.990:0.12956)0.960:0.06961)0.710:0.00054)0.800:0.01578)0.760:0.01558,(T1039_017:0.08246,S_pimpinellifolium_044:0.00054)0.980:0.08153)0.230:0.00053)0.910:0.00055)0.910:0.00054)0.830:0.01549,S_pimpinellifolium_046:0.00054)0.980:0.08610)0.660:0.01369)0.530:0.04644,(TR00027_103:0.00054,(PI365925_037:0.04936,S_cheesemaniae_055:0.03179)0.650:0.08462)1.000:0.41706)0.650:0.00296)0.940:0.01555,(The_Dutchman_028:0.00053,(((Polish_Joe_026:0.0,Brandywine_089:0.0):0.00054,((((Porter_078:0.01608,Kentucky_Beefsteak_093:0.01542)0.880:0.03271,(Thessaloniki_096:0.08543,Bloodt_Butcher_088:0.03267)0.700:0.01564)0.800:0.01585,(Giant_Belgium_091:0.01562,(Moneymaker_001:0.00054,(Dixy_Golden_Giant_090:0.01579,(Large_Red_Cherry_077:0.03276,Momatero_015:0.04969)0.720:0.01528)0.870:0.01570)0.850:0.01556)0.480:0.00055)0.930:0.03157,Marmande_VFA_094:0.03158)0.970:0.00053)0.880:0.00053,Watermelon_Beefsteak_097:0.01555)0.890:0.01559)0.970:0.03159)0.950:0.00054,PI169588_041:0.00054,((Sonato_012:0.11798,(((All_Round_011:0.01555,Chih-Mu-Tao-Se_038:0.00054)0.180:0.00054,(((Jersey_Devil_024:0.0,Chag_Li_Lycopersicon_esculentum_032:0.0,S_pimpinellifolium_unc_043:0.0):0.00054,(((PI311117_036:0.04839,((Taxi_006:0.0,Tiffen_Mennonite_034:0.0):0.00054,(Cal_J_TM_VF_027:0.00053,(Lycopersicon_esculentum_828_021:0.00054,(Black_Cherry_029:0.03245,(Galina_005:0.00054,S_pimpinellifolium_unc_045:0.01559)0.880:0.03248)0.770:0.01547)0.950:0.03179)0.160:0.01560)0.840:0.01563)0.420:0.00054,Lycopersicon_esculentum_825_020:0.00054)0.860:0.01556,((Cross_Country_013:0.0,ES_58_Heinz_040:0.0):0.00054,(Rutgers_004:0.01554,Lidi_014:0.04758)0.900:0.00054)0.880:0.00054)0.860:0.01558)0.080:0.01560,(Alisa_Craig_002:0.01560,John_s_big_orange_008:0.00054)1.000:0.00054)0.840:0.01558)0.800:0.01566,(Large_Pink_019:0.01555,Anto_030:0.00054)0.140:0.00054)0.920:0.01555)0.680:0.00054,Wheatley_s_Frost_Resistant_035:0.03155)0.950:0.00054);

     find PROJNAME -name '*.fasta' | sort | xargs -I{} -P30 bash -c 'vcfmerger/aux/FastTreeMP -nt -makematrix {} > {}.matrix'
     OUTPUT: PROJNAME/CHROMOSOME/short2.lst.vcf.gz.simplified.vcf.gz.filtered.vcf.gz.SL2.40ch01.000090300001-000090310000.Frag_SL2.40ch01g10000_9031.vcf.gz.SL2.40ch01.fasta.matrix
         Moneymaker_001 0.000000 0.134437 0.345611 0.134437  0.321609
         Alisa_Craig_002 0.134437 0.000000 0.211925 0.064210
         Gardeners_Delight_003 0.345611 0.211925 0.000000 0.211925

    Process the data into memory dump database (pyckle): vcf_walk_ram.py --pickle PROJNAME OUTPUT: walk_out_10k.db walk_out_10k_SL2.40ch00.db walk_out_10k_SL2.40ch01.db walk_out_10k_SL2.40ch02.db walk_out_10k_SL2.40ch03.db walk_out_10k_SL2.40ch04.db walk_out_10k_SL2.40ch05.db walk_out_10k_SL2.40ch06.db walk_out_10k_SL2.40ch07.db walk_out_10k_SL2.40ch08.db walk_out_10k_SL2.40ch09.db walk_out_10k_SL2.40ch10.db walk_out_10k_SL2.40ch11.db walk_out_10k_SL2.40ch12.db

    Convert (pickle) database to SQLite (if dependencies installed): vcf_walk_sql.py PROJNAME OUTPUT: walk_out_10k.sql.db

5.2. Server Run vcf_walk_server.py

Run svg_server.py (if dependencis are installed)

Run cluster_server.py (if dependencis are installed)
  1. Acknowledgements The co-authors: Dick de Ridder Eric M. Schranz Gabino Perez Hans de Jong Sander Peters Paul Franz The beta testers: Remco Stam Ruud de Maagd Yongfeng Zhou

Manual: http://sauloal.github.io/introgressionbrowser