From adf45d33eaac9ccd84c316af755bf8ee263b978c Mon Sep 17 00:00:00 2001 From: Xi Chen Date: Fri, 8 Mar 2024 23:52:44 +0800 Subject: [PATCH] reorganised the data folder --- data/{ => BD}/BD_CLS1.txt | 0 data/{ => BD}/BD_CLS2.txt | 0 data/{ => BD}/BD_CLS3.txt | 0 ...-Cell-Analysis-System-Instrument_UG_EN.pdf | Bin ...X_BD-Rhapsody-WTA-alpha-Protocol_UG_EN.pdf | Bin .../drop_chip_barcode_adapters.txt | 0 data/{ => Drop-ChIP}/grep.png | Bin data/{ => High-Content-sci}/s3_Tn5.svg | 0 data/{ => High-Content-sci}/s3_oligos.xlsx | Bin ...ods_table_hydrop_oligonucleotide_list.xlsx | Bin .../HyDrop_20200130_plate-1-96_barcode.csv | 0 .../HyDrop_20200130_plate-2-96_barcode.csv | 0 ...op_20200130_plate-3-96-ATACseq_barcode.csv | 0 ...rop_20200130_plate-3-96-RNAseq_barcode.csv | 0 data/HyDrop/elife-73971-supp1-v4.docx | Bin 0 -> 19670 bytes data/HyDrop/elife-73971-supp2-v4.docx | Bin 0 -> 24806 bytes data/HyDrop/elife-73971-supp3-v4.docx | Bin 0 -> 26329 bytes data/MARS-seq/41596_2019_164_MOESM4_ESM.xlsx | Bin 0 -> 22544 bytes data/MARS-seq/41596_2019_164_MOESM5_ESM.xlsx | Bin 0 -> 11764 bytes data/MARS-seq/jaitin-sm.pdf | Bin 0 -> 144606 bytes .../1-s2.0-S0092867418301168-mmc1.xlsx | Bin 0 -> 16386 bytes .../1-s2.0-S0092867418301168-mmc2.xlsx | Bin 0 -> 12096 bytes data/{ => Microwell-seq}/Microwell_A1-A96.txt | 0 data/{ => Microwell-seq}/Microwell_B1-B96.txt | 0 data/{ => Microwell-seq}/Microwell_C1-C96.txt | 0 .../41598_2021_90255_MOESM4_ESM.xlsx | Bin data/{ => PIP-seq}/delley2021_barcode.txt | 0 data/{ => PIP-seq}/fb_v3_bc1.tsv | 0 data/{ => PIP-seq}/fb_v3_bc2.tsv | 0 data/{ => PIP-seq}/fb_v3_bc3.tsv | 0 data/{ => PIP-seq}/fb_v3_bc4.tsv | 0 data/{ => PIP-seq}/pip-seq_v2_bc1.tsv | 0 data/{ => PIP-seq}/pip-seq_v2_bc2.tsv | 0 data/{ => PIP-seq}/pip-seq_v2_bc3.tsv | 0 .../41594_2019_323_MOESM2_ESM.xlsx | Bin .../41594_2019_323_MOESM3_ESM.xlsx | Bin data/{ => Paired-seq}/Paired_seq_Tn5.svg | 0 .../Star_CB_UMI_Complex_Paired-seq.jpg | Bin data/{ => Paired-seq}/paired-seq_bc01.csv | 0 .../paired-seq_bc02-03-04.csv | 0 .../1-s2.0-S0092867420312538-mmc1.xlsx | Bin data/{ => SHARE-seq}/share-seq_ligationBC.csv | 0 data/{ => SPLiT-seq}/Round1_barcodes.txt | 0 data/{ => SPLiT-seq}/Round2_barcodes.txt | 0 data/{ => SPLiT-seq}/Round3_barcodes.txt | 0 data/{ => dscATAC}/dscATAC_Tn5.svg | 0 .../dscATAC_dsciATAC_bead_structures.xlsx | Bin 0 -> 29011 bytes ...1596_2017_BFnprot2016154_MOESM456_ESM.xlsx | Bin ...1596_2017_BFnprot2016154_MOESM457_ESM.xlsx | Bin .../Star_CB_UMI_Complex_inDrop.jpg | Bin data/{ => inDrop}/inDrop_Barcode1.csv | 0 data/{ => inDrop}/inDrop_Barcode2.csv | 0 data/{ => inDrop}/inDrop_barcode1_list.txt | 0 data/{ => inDrop}/inDrop_barcode2_list.txt | 0 .../41587_2018_BFnbt4038_MOESM11_ESM.xlsx | Bin .../41587_2019_290_MOESM6_ESM.xlsx | Bin data/{ => scTHS-seq}/scTHS_Tn5.svg | 0 .../sci-RNA-seq3_RT_bc.csv | 0 .../sci-RNA-seq3_hairpin_bc.csv | 0 .../sci-RNA-seq3_p5.csv | 0 .../sci-RNA-seq3_p7.csv | 0 .../scifi-ATAC-seq_Tn5.svg | 0 .../scifi-RNA-seq_suppl_table1.xlsx | Bin docs/source/ge/inDrop.md | 12 +++---- docs/source/multi/Paired-seq.md | 12 +++---- docs/source/multi/SHARE-seq.md | 8 ++--- methods_html/BD_Rhapsody.html | 4 +-- methods_html/CEL-seq_family.html | 4 +-- methods_html/CoBATCH.html | 4 +-- methods_html/DR-seq.html | 4 +-- methods_html/Delley2021.html | 6 ++-- methods_html/Drop-ChIP.html | 12 +++---- methods_html/Drop-seq.html | 6 ++-- methods_html/G_and_T_seq.html | 2 +- methods_html/HyDrop_ATAC.html | 8 ++--- methods_html/HyDrop_RNA.html | 8 ++--- methods_html/ISSAAC-seq.html | 2 +- methods_html/LIANTI.html | 2 +- methods_html/MALBAC.html | 12 +++---- methods_html/MARS-seq.html | 14 ++++---- methods_html/Microwell-seq.html | 8 ++--- methods_html/PIP-seq.html | 32 +++++++++--------- methods_html/PIP-seq_v1p.html | 2 +- methods_html/Paired-seq.html | 6 ++-- methods_html/Quartz-seq_family.html | 6 ++-- methods_html/SCRB-seq.html | 2 +- methods_html/SHARE-seq.html | 4 +-- methods_html/SMART-seq_family.html | 20 +++++------ methods_html/SNARE-seq.html | 8 ++--- methods_html/SPLiT-seq_archive.html | 10 +++--- methods_html/STRT-seq_family.html | 4 +-- methods_html/SeqWell_S3.html | 4 +-- methods_html/VASA-seq.html | 2 +- methods_html/dscATAC.html | 14 ++++---- methods_html/inDrop.html | 10 +++--- methods_html/itChIP-seq.html | 12 +++---- methods_html/plate_and_piATAC-seq.html | 2 +- methods_html/s3-ATAC.html | 8 ++--- methods_html/s3-WGS.html | 4 +-- methods_html/scBS-seq.html | 4 +-- methods_html/scCAT-seq.html | 2 +- methods_html/scDNase_scMNase.html | 2 +- methods_html/scDamID.html | 2 +- methods_html/scDamT-seq.html | 6 ++-- methods_html/scMT-seq.html | 2 +- methods_html/scMandT.html | 2 +- methods_html/scNMT-seq.html | 2 +- methods_html/scNOMe_scCOOL.html | 2 +- methods_html/scRRBS.html | 4 +-- methods_html/scTHS-seq.html | 10 +++--- methods_html/scTrio-seq.html | 2 +- methods_html/scifi-ATAC-seq.html | 4 +-- methods_html/scifi-RNA-seq.html | 2 +- methods_html/tang2009.html | 4 +-- 114 files changed, 164 insertions(+), 164 deletions(-) rename data/{ => BD}/BD_CLS1.txt (100%) rename data/{ => BD}/BD_CLS2.txt (100%) rename data/{ => BD}/BD_CLS3.txt (100%) rename data/{ => BD}/GMX_BD-Rhapsody-Single-Cell-Analysis-System-Instrument_UG_EN.pdf (100%) rename data/{ => BD}/GMX_BD-Rhapsody-WTA-alpha-Protocol_UG_EN.pdf (100%) rename data/{ => Drop-ChIP}/drop_chip_barcode_adapters.txt (100%) rename data/{ => Drop-ChIP}/grep.png (100%) rename data/{ => High-Content-sci}/s3_Tn5.svg (100%) rename data/{ => High-Content-sci}/s3_oligos.xlsx (100%) rename data/{ => HyDrop}/20210712_supp_methods_table_hydrop_oligonucleotide_list.xlsx (100%) rename data/{ => HyDrop}/HyDrop_20200130_plate-1-96_barcode.csv (100%) rename data/{ => HyDrop}/HyDrop_20200130_plate-2-96_barcode.csv (100%) rename data/{ => HyDrop}/HyDrop_20200130_plate-3-96-ATACseq_barcode.csv (100%) rename data/{ => HyDrop}/HyDrop_20200130_plate-3-96-RNAseq_barcode.csv (100%) create mode 100644 data/HyDrop/elife-73971-supp1-v4.docx create mode 100644 data/HyDrop/elife-73971-supp2-v4.docx create mode 100644 data/HyDrop/elife-73971-supp3-v4.docx create mode 100644 data/MARS-seq/41596_2019_164_MOESM4_ESM.xlsx create mode 100644 data/MARS-seq/41596_2019_164_MOESM5_ESM.xlsx create mode 100644 data/MARS-seq/jaitin-sm.pdf create mode 100644 data/Microwell-seq/1-s2.0-S0092867418301168-mmc1.xlsx create mode 100644 data/Microwell-seq/1-s2.0-S0092867418301168-mmc2.xlsx rename data/{ => Microwell-seq}/Microwell_A1-A96.txt (100%) rename data/{ => Microwell-seq}/Microwell_B1-B96.txt (100%) rename data/{ => Microwell-seq}/Microwell_C1-C96.txt (100%) rename data/{ => PIP-seq}/41598_2021_90255_MOESM4_ESM.xlsx (100%) rename data/{ => PIP-seq}/delley2021_barcode.txt (100%) rename data/{ => PIP-seq}/fb_v3_bc1.tsv (100%) rename data/{ => PIP-seq}/fb_v3_bc2.tsv (100%) rename data/{ => PIP-seq}/fb_v3_bc3.tsv (100%) rename data/{ => PIP-seq}/fb_v3_bc4.tsv (100%) rename data/{ => PIP-seq}/pip-seq_v2_bc1.tsv (100%) rename data/{ => PIP-seq}/pip-seq_v2_bc2.tsv (100%) rename data/{ => PIP-seq}/pip-seq_v2_bc3.tsv (100%) rename data/{ => Paired-seq}/41594_2019_323_MOESM2_ESM.xlsx (100%) rename data/{ => Paired-seq}/41594_2019_323_MOESM3_ESM.xlsx (100%) rename data/{ => Paired-seq}/Paired_seq_Tn5.svg (100%) rename data/{ => Paired-seq}/Star_CB_UMI_Complex_Paired-seq.jpg (100%) rename data/{ => Paired-seq}/paired-seq_bc01.csv (100%) rename data/{ => Paired-seq}/paired-seq_bc02-03-04.csv (100%) rename data/{ => SHARE-seq}/1-s2.0-S0092867420312538-mmc1.xlsx (100%) rename data/{ => SHARE-seq}/share-seq_ligationBC.csv (100%) rename data/{ => SPLiT-seq}/Round1_barcodes.txt (100%) rename data/{ => SPLiT-seq}/Round2_barcodes.txt (100%) rename data/{ => SPLiT-seq}/Round3_barcodes.txt (100%) rename data/{ => dscATAC}/dscATAC_Tn5.svg (100%) create mode 100644 data/dscATAC/dscATAC_dsciATAC_bead_structures.xlsx rename data/{ => inDrop}/41596_2017_BFnprot2016154_MOESM456_ESM.xlsx (100%) rename data/{ => inDrop}/41596_2017_BFnprot2016154_MOESM457_ESM.xlsx (100%) rename data/{ => inDrop}/Star_CB_UMI_Complex_inDrop.jpg (100%) rename data/{ => inDrop}/inDrop_Barcode1.csv (100%) rename data/{ => inDrop}/inDrop_Barcode2.csv (100%) rename data/{ => inDrop}/inDrop_barcode1_list.txt (100%) rename data/{ => inDrop}/inDrop_barcode2_list.txt (100%) rename data/{ => scTHS-seq}/41587_2018_BFnbt4038_MOESM11_ESM.xlsx (100%) rename data/{ => scTHS-seq}/41587_2019_290_MOESM6_ESM.xlsx (100%) rename data/{ => scTHS-seq}/scTHS_Tn5.svg (100%) rename data/{ => sci-RNA-seq_family}/sci-RNA-seq3_RT_bc.csv (100%) rename data/{ => sci-RNA-seq_family}/sci-RNA-seq3_hairpin_bc.csv (100%) rename data/{ => sci-RNA-seq_family}/sci-RNA-seq3_p5.csv (100%) rename data/{ => sci-RNA-seq_family}/sci-RNA-seq3_p7.csv (100%) rename data/{ => scifi-ATAC-seq}/scifi-ATAC-seq_Tn5.svg (100%) rename data/{ => scifi-RNA-seq}/scifi-RNA-seq_suppl_table1.xlsx (100%) diff --git a/data/BD_CLS1.txt b/data/BD/BD_CLS1.txt similarity index 100% rename from data/BD_CLS1.txt rename to data/BD/BD_CLS1.txt diff --git a/data/BD_CLS2.txt b/data/BD/BD_CLS2.txt similarity index 100% rename from data/BD_CLS2.txt rename to data/BD/BD_CLS2.txt diff --git a/data/BD_CLS3.txt b/data/BD/BD_CLS3.txt similarity index 100% rename from data/BD_CLS3.txt rename to data/BD/BD_CLS3.txt diff --git a/data/GMX_BD-Rhapsody-Single-Cell-Analysis-System-Instrument_UG_EN.pdf b/data/BD/GMX_BD-Rhapsody-Single-Cell-Analysis-System-Instrument_UG_EN.pdf similarity index 100% rename from data/GMX_BD-Rhapsody-Single-Cell-Analysis-System-Instrument_UG_EN.pdf rename to data/BD/GMX_BD-Rhapsody-Single-Cell-Analysis-System-Instrument_UG_EN.pdf diff --git a/data/GMX_BD-Rhapsody-WTA-alpha-Protocol_UG_EN.pdf b/data/BD/GMX_BD-Rhapsody-WTA-alpha-Protocol_UG_EN.pdf similarity index 100% rename from data/GMX_BD-Rhapsody-WTA-alpha-Protocol_UG_EN.pdf rename to data/BD/GMX_BD-Rhapsody-WTA-alpha-Protocol_UG_EN.pdf diff --git a/data/drop_chip_barcode_adapters.txt b/data/Drop-ChIP/drop_chip_barcode_adapters.txt similarity index 100% rename from data/drop_chip_barcode_adapters.txt rename to data/Drop-ChIP/drop_chip_barcode_adapters.txt diff --git a/data/grep.png b/data/Drop-ChIP/grep.png similarity index 100% rename from data/grep.png rename to data/Drop-ChIP/grep.png diff --git a/data/s3_Tn5.svg b/data/High-Content-sci/s3_Tn5.svg similarity index 100% rename from data/s3_Tn5.svg rename to data/High-Content-sci/s3_Tn5.svg diff --git a/data/s3_oligos.xlsx b/data/High-Content-sci/s3_oligos.xlsx similarity index 100% rename from data/s3_oligos.xlsx rename to data/High-Content-sci/s3_oligos.xlsx diff --git a/data/20210712_supp_methods_table_hydrop_oligonucleotide_list.xlsx b/data/HyDrop/20210712_supp_methods_table_hydrop_oligonucleotide_list.xlsx similarity index 100% rename from data/20210712_supp_methods_table_hydrop_oligonucleotide_list.xlsx rename to 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zCWeDv&;+?3ESr~AAb50BVdg)-V|lph5D(V{#}p5|oON#9NZ1U}xCgNOhk38YZCvl! z{=dip&bGcDN15x^2yh1d(B5Eztng+mIi*hxU-TSJ{nK2opV_;DnL)1V>sV2ok8?|t zH{Ez8(|Dy|l8Yhl6PQQ)1l{jx)EAP=m<^jfb8I4Gn$;-*P_DceOcyI^Ay|HAx<9m|#Ph3UtCedc zPq`dEFDC#}6^KU`obemvbks|JUvumHYPz4i?V_U5uNAO3*JGF6tTzDKTnfp=ULXb9_bGG%ocT_lg(&v#Ft^ap6_O)QjWNg0@i@ zhI5yH_K&T~4?LZb`VukbJN$=EAvQYNLG!KFtI+UgkJKWu&%+HK?_ATTq>==#FI6fL}LSZ95WvrUFOq*>6Se`0JLRy^i(3nu-73`9mIueAmlvBs#i% zn_tMC$+ueUI^SSCaK{CC0P;tU?*<@j$=KREv+n`q> These options specify the locations of cell barcode and UMI in the 2nd fastq files we passed to `--readFilesIn`. In this case, it is __Read 2__. Read the [STAR manual](https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf) for more details. I have drawn a picture to help myself decide the exact parameters. There are some freedom here depending on what you are using as anchors. in __inDrop V1 & 2__, the __Barcode1__ has variable lengths, the absolute positions of __Barcode2__ and __UMI__ are variable. Therefore, using Read start as anchor will not work for them. We need to use the adaptor as the anchor, and specify the positions relative to the anchor. See the image: -![](https://teichlab.github.io/scg_lib_structs/data/Star_CB_UMI_Complex_inDrop.jpg) +![](https://teichlab.github.io/scg_lib_structs/data/inDrop/Star_CB_UMI_Complex_inDrop.jpg) `--soloCBwhitelist` diff --git a/docs/source/multi/Paired-seq.md b/docs/source/multi/Paired-seq.md index e37855e..ff0964c 100644 --- a/docs/source/multi/Paired-seq.md +++ b/docs/source/multi/Paired-seq.md @@ -186,7 +186,7 @@ After that, we are ready to begin the preprocessing. ## Prepare Whitelist -Cells are barcoded for the first time by either barcoded (3 bp) Tn5 for DNA or oligo-dT primers, followed by three more rounds of ligation. Each round will add 7-bp __Ligation Barcode__ to the molecules. There are 96 different __Ligation Barcodes__ in each round. The same set of 96 __Ligation Barcodes__ are used in each round. Single cells can be identified by the combination of themselves. Here is the information from the [Supplementary Table 2](https://teichlab.github.io/scg_lib_structs/data/41594_2019_323_MOESM3_ESM.xlsx) from the [__Paired-seq__ paper](https://www.nature.com/articles/s41594-019-0323-x): +Cells are barcoded for the first time by either barcoded (3 bp) Tn5 for DNA or oligo-dT primers, followed by three more rounds of ligation. Each round will add 7-bp __Ligation Barcode__ to the molecules. There are 96 different __Ligation Barcodes__ in each round. The same set of 96 __Ligation Barcodes__ are used in each round. Single cells can be identified by the combination of themselves. Here is the information from the [Supplementary Table 2](https://teichlab.github.io/scg_lib_structs/data/Paired-seq/41594_2019_323_MOESM3_ESM.xlsx) from the [__Paired-seq__ paper](https://www.nature.com/articles/s41594-019-0323-x): __Round 1 barcodes (eight 3-bp Tn5 or oligo-dT barcodes)__ @@ -304,15 +304,15 @@ __Round 2, 3 and 4 barcodes (7 bp)__ I have prepared the above two tables as `csv` files for you, and you can download them: -[paired-seq_bc01.csv](https://teichlab.github.io/scg_lib_structs/data/paired-seq_bc01.csv) -[paired-seq_bc02-03-04.csv](https://teichlab.github.io/scg_lib_structs/data/paired-seq_bc02-03-04.csv) +[paired-seq_bc01.csv](https://teichlab.github.io/scg_lib_structs/data/Paired-seq/paired-seq_bc01.csv) +[paired-seq_bc02-03-04.csv](https://teichlab.github.io/scg_lib_structs/data/Paired-seq/paired-seq_bc02-03-04.csv) Since during each ligation round, the same set of __Ligation Barcodes__ (96) are used. Therefore, the whitelist is basically the combination of those 96 barcodes themselves for three times and with those 8 barcodes in the first round: a total of __96 * 96 * 96 * 8 = 7,077,888__ barcodes. Since the barcodes are sequenced as __Read 2__, which uses the top strand as the template, we should use the barcode sequences as they are to construct the whitelist. In addition, the order of the barcodes in __Read 2__ is Round 4 -> Round 3 -> Round 2 -> Round 1. Therefore, we need to generate the whitelist in this order. Again, if you are confused, check the [Paired-seq GitHub page](https://teichlab.github.io/scg_lib_structs/methods_html/Paired-seq.html). ```bash # download the barcode files -wget -P paired-seq/data https://teichlab.github.io/scg_lib_structs/data/paired-seq_bc01.csv \ - https://teichlab.github.io/scg_lib_structs/data/paired-seq_bc02-03-04.csv +wget -P paired-seq/data https://teichlab.github.io/scg_lib_structs/data/Paired-seq/paired-seq_bc01.csv \ + https://teichlab.github.io/scg_lib_structs/data/Paired-seq/paired-seq_bc02-03-04.csv # generate whitelist for chromap for w in $(tail -n +2 paired-seq/data/paired-seq_bc02-03-04.csv | cut -f 2 -d,); do @@ -424,7 +424,7 @@ If you understand the __Paired-seq__ experimental procedures described in [this >> These options specify the locations of cell barcode and UMI in the 2nd fastq files we passed to `--readFilesIn`. In this case, it is __Read 2__. Read the [STAR manual](https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf) for more details. I have drawn a picture to help myself decide the exact parameters. There are some freedom here depending on what you are using as anchors. Due to the 3 random bases in the middle, using Read start as anchor will not work for the barcodes in the middle. We need to use the adapter as the anchor, and specify the positions relative to the anchor. See the image: -![](https://teichlab.github.io/scg_lib_structs/data/Star_CB_UMI_Complex_Paired-seq.jpg) +![](https://teichlab.github.io/scg_lib_structs/data/Paired-seq/Star_CB_UMI_Complex_Paired-seq.jpg) `--soloCBwhitelist` diff --git a/docs/source/multi/SHARE-seq.md b/docs/source/multi/SHARE-seq.md index d09b098..c57ffe1 100644 --- a/docs/source/multi/SHARE-seq.md +++ b/docs/source/multi/SHARE-seq.md @@ -27,7 +27,7 @@ The 2nd read (__i7__) has the following information, which will be used to ident |--------|---------------------------------------------------------------------------------------------------------------------| | 99 bp | TCGGACGATCATGGG + 8 bp `LB` + CAAGTATGCAGCGCGCTCAAGCACGTGGAT + 8 bp `LB` AGTCGTACGCCGATGCGAAACATCGGCCAC + 8 bp `LB` | -After sequencing, you need to run `bcl2fastq` by yourself with a `SampleSheet.csv`. Here is an example of `SampleSheet.csv` of a NextSeq run with two different samples using the `Ad1.09` and `Ad1.17` primers from the [Supplementary Table 1](https://teichlab.github.io/scg_lib_structs/data/1-s2.0-S0092867420312538-mmc1.xlsx) from the [__SHARE-seq__ paper](https://www.sciencedirect.com/science/article/pii/S0092867420312538) as sample and modality index: +After sequencing, you need to run `bcl2fastq` by yourself with a `SampleSheet.csv`. Here is an example of `SampleSheet.csv` of a NextSeq run with two different samples using the `Ad1.09` and `Ad1.17` primers from the [Supplementary Table 1](https://teichlab.github.io/scg_lib_structs/data/SHARE-seq/1-s2.0-S0092867420312538-mmc1.xlsx) from the [__SHARE-seq__ paper](https://www.sciencedirect.com/science/article/pii/S0092867420312538) as sample and modality index: ```text [Header],,,,,,,,,,, @@ -186,7 +186,7 @@ After that, we are ready to begin the preprocessing. ## Prepare Whitelist -There are three rounds of ligation. Each round will add 8-bp __Ligation Barcode__ to the molecules. There are 96 different __Ligation Barcodes__ in each round. The same set of 96 __Ligation Barcodes__ are used in each round. Single cells can be identified by the combination of themselves. Here is the information from the [Supplementary Table 1](https://teichlab.github.io/scg_lib_structs/data/1-s2.0-S0092867420312538-mmc1.xlsx) from the [__SHARE-seq__ paper](https://www.sciencedirect.com/science/article/pii/S0092867420312538): +There are three rounds of ligation. Each round will add 8-bp __Ligation Barcode__ to the molecules. There are 96 different __Ligation Barcodes__ in each round. The same set of 96 __Ligation Barcodes__ are used in each round. Single cells can be identified by the combination of themselves. Here is the information from the [Supplementary Table 1](https://teichlab.github.io/scg_lib_structs/data/SHARE-seq/1-s2.0-S0092867420312538-mmc1.xlsx) from the [__SHARE-seq__ paper](https://www.sciencedirect.com/science/article/pii/S0092867420312538): | WellPosition | Name | Sequence | Reverse complement | |--------------|---------------|----------|:------------------:| @@ -287,11 +287,11 @@ There are three rounds of ligation. Each round will add 8-bp __Ligation Barcode_ | G12 | Round1/2/3_95 | GATGAATC | GATTCATC | | H12 | Round1/2/3_96 | GCCAAGAC | GTCTTGGC | -Since during each ligation round, the same set of __Ligation Barcodes__ (96) are used. Therefore, the whitelist is basically the combination of those 96 barcodes themselves for three times: a total of __96 * 96 * 96 = 884736__ barcodes. Since the barcodes are sequenced as the `i7` index, which uses the bottom strand as the template, we should use the reverse complement to construct the whitelist. Again, if you are confused, check the [SHARE-seq GitHub page](https://teichlab.github.io/scg_lib_structs/methods_html/SHARE-seq.html). I have put the above table into a `csv` file so that you can download by [__click here__](https://teichlab.github.io/scg_lib_structs/data/share-seq_ligationBC.csv). +Since during each ligation round, the same set of __Ligation Barcodes__ (96) are used. Therefore, the whitelist is basically the combination of those 96 barcodes themselves for three times: a total of __96 * 96 * 96 = 884736__ barcodes. Since the barcodes are sequenced as the `i7` index, which uses the bottom strand as the template, we should use the reverse complement to construct the whitelist. Again, if you are confused, check the [SHARE-seq GitHub page](https://teichlab.github.io/scg_lib_structs/methods_html/SHARE-seq.html). I have put the above table into a `csv` file so that you can download by [__click here__](https://teichlab.github.io/scg_lib_structs/data/SHARE-seq/share-seq_ligationBC.csv). ```bash # download the ligation barcode file -wget -P share-seq/data https://teichlab.github.io/scg_lib_structs/data/share-seq_ligationBC.csv +wget -P share-seq/data https://teichlab.github.io/scg_lib_structs/data/SHARE-seq/share-seq_ligationBC.csv # generate whitelist for x in $(tail -n +2 share-seq/data/share-seq_ligationBC.csv | cut -f 4 -d,); do diff --git a/methods_html/BD_Rhapsody.html b/methods_html/BD_Rhapsody.html index c8d6331..db9a9e5 100644 --- a/methods_html/BD_Rhapsody.html +++ b/methods_html/BD_Rhapsody.html @@ -9,7 +9,7 @@

-

BD Rhapsody WTA is a nanowell-based commercial system like Microwell-seq. They use similar split-pool approach to generate their oligos on magnetic beads. The first publication (V1 version) is in Nature Methods 16, 75-78 (2019). In 2022, BD introduced a new version of beads , called Enhanced Beads, which have slightly different oilgo design. The barcodes are the same, but the linker sequences are much shorter than the V1 version. Note: I don't have the full sequence details from each step of the protocol. Sequences presented here are based on educational guess from the sequencing data. The final library structure should be accurate though. Click here for the guide to use the machine, and click here to see the off-machine protocol.

+

BD Rhapsody WTA is a nanowell-based commercial system like Microwell-seq. They use similar split-pool approach to generate their oligos on magnetic beads. The first publication (V1 version) is in Nature Methods 16, 75-78 (2019). In 2022, BD introduced a new version of beads , called Enhanced Beads, which have slightly different oilgo design. The barcodes are the same, but the linker sequences are much shorter than the V1 version. Note: I don't have the full sequence details from each step of the protocol. Sequences presented here are based on educational guess from the sequencing data. The final library structure should be accurate though. Click here for the guide to use the machine, and click here to see the off-machine protocol.


@@ -20,7 +20,7 @@

Sequence used during the experiment:

Enhanced Bead (introduced in 2022): |--5'-CCCCCCTCTCTCTCTACACGACGCTCTTCCGATCT[VB][CLS1]GTGA[CLS2]GACA[CLS3][8-bp UMI](T)18 -3'

-- Cells are determined as different combination of [CLS1], [CLS2] and [CLS3], each is 9-bp long.

-- There are 97 different sequences each, so you have a total of 97x97x97 = 912,673 different combinations.

-

-- Click here to see the sequences of CLS1, CLS2, and CLS3.

+

-- Click here to see the sequences of CLS1, CLS2, and CLS3.

-- VB means "Variable Bases" which only exists in the Enhanced Beads. It has four possible bases: None, A, GT or TCA.

Randomer in the kit: 5'- TCAGACGTGTGCTCTTCCGATCTNNNNNNNNN -3'

Pre-amp forward primer: 5'- GACGCTCTTCCGATCT -3'

diff --git a/methods_html/CEL-seq_family.html b/methods_html/CEL-seq_family.html index 63a3944..d78f38d 100644 --- a/methods_html/CEL-seq_family.html +++ b/methods_html/CEL-seq_family.html @@ -9,10 +9,10 @@

CEL-seq / CEL-seq2

-

CEL-seq2 is an improved version of CEL-seq, the main differences are:

+

CEL-seq2 is an improved version of CEL-seq, the main differences are:

(1) CEL-seq2 uses UMI; CEL-seq does not.

(2) CEL-seq2 uses random priming for reverse transcription after IVT amplification; CEL-seq uses RNA adapter ligation and then uses the primer annealed to the ligated adapter for reverse transcription after IVT amplification.

-

CEL-seq used some homemade oligo sequence design and one of Illumina's kit. The protocol in the publication was not entirely clear, so I guess it was Illumina Truseq Small RNA-seq kit based on the oligo names (In the CEL-seq2 publication, they confirmed this is the case). I still put CEL-seq here to get a historic view of how methods evolve.

 +

CEL-seq used some homemade oligo sequence design and one of Illumina's kit. The protocol in the publication was not entirely clear, so I guess it was Illumina Truseq Small RNA-seq kit based on the oligo names (In the CEL-seq2 publication, they confirmed this is the case). I still put CEL-seq here to get a historic view of how methods evolve.


diff --git a/methods_html/CoBATCH.html b/methods_html/CoBATCH.html index 107192c..4f042e8 100644 --- a/methods_html/CoBATCH.html +++ b/methods_html/CoBATCH.html @@ -9,11 +9,11 @@

CoBATCH

-

The combinatorial barcoding and targeted chromatin release (CoBATCH) uses the same strategy (combinatorial indexing) and oligo design as sci-ATAC-seq. This method is kind of a combination of sci-ATAC-seq and CUT&TAG, where they used a protein A-Tn5 fusion protein to tether the pA-Tn5 to specific genomic loci bound by proteins of interest using antibodies.

+

The combinatorial barcoding and targeted chromatin release (CoBATCH) uses the same strategy (combinatorial indexing) and oligo design as sci-ATAC-seq. This method is kind of a combination of sci-ATAC-seq and CUT&TAG, where they used a protein A-Tn5 fusion protein to tether the pA-Tn5 to specific genomic loci bound by proteins of interest using antibodies.

For a brief introduction and backgound about the related methods, I have written a blog post. Click here to have a look!

-

Since CoBATCH uses the same oligo design as sci-ATAC-seq, for the step-by-step library generation, it is the same as sci-ATAC-seq. Click the sci-ATAC-seq page for details.

 +

Since CoBATCH uses the same oligo design as sci-ATAC-seq, for the step-by-step library generation, it is the same as sci-ATAC-seq. Click the sci-ATAC-seq page for details.

diff --git a/methods_html/DR-seq.html b/methods_html/DR-seq.html index 799fe6c..198fbc4 100644 --- a/methods_html/DR-seq.html +++ b/methods_html/DR-seq.html @@ -9,7 +9,7 @@

DR-seq

-

DR-seq was published by Dey et al. in Nature Biotechnology 33, 285-289. It is a method to simultaneously analyse gDNA and mRNA without upfront physical separation, though it perform library part separately for gDNA and mRNA in the later part of the protocol. It uses MALBAC type of apmification to preamplify gDNA and cDNA together. Then, PCR is used to construct gDNA library and IVT is used to construct the mRNA library. There will be some mRNA reads in the gDNA portion of the library.

+

DR-seq was published by Dey et al. in Nature Biotechnology 33, 285-289. It is a method to simultaneously analyse gDNA and mRNA without upfront physical separation, though it perform library part separately for gDNA and mRNA in the later part of the protocol. It uses MALBAC type of apmification to preamplify gDNA and cDNA together. Then, PCR is used to construct gDNA library and IVT is used to construct the mRNA library. There will be some mRNA reads in the gDNA portion of the library.


@@ -121,7 +121,7 @@

4.2) For mRNA libary, the procedure is the same as Standard sequencing workflow is used, and you can check any other pages.

+

Standard sequencing workflow is used, and you can check any other pages.

diff --git a/methods_html/Delley2021.html b/methods_html/Delley2021.html index 51400c0..90be6d7 100644 --- a/methods_html/Delley2021.html +++ b/methods_html/Delley2021.html @@ -9,9 +9,9 @@

Delley2021

-

This method does not have a name. It was published in Scientific Reports: Modular barcode beads for microfluidic single cell genomics [Delley2021].. This method builds the foundation for PIP-seq. The complete full list of oligo sequences can be found in the Supplementary Tables from the Delley2021 paper: 41598_2021_90255_MOESM4_ESM.xlsx.

+

This method does not have a name. It was published in Scientific Reports: Modular barcode beads for microfluidic single cell genomics [Delley2021].. This method builds the foundation for PIP-seq. The complete full list of oligo sequences can be found in the Supplementary Tables from the Delley2021 paper: 41598_2021_90255_MOESM4_ESM.xlsx.

-

As the paper suggested, this is not really about scRNA-seq or any specific assays. Instead, it provides a modular procedures to prepare barcoded beads for high-throughput droplet single-cell assays for anything you are interested in (RNA, DNA, chromatin accessibility etc.). The point here is that you use Steps (1) - (7) described below to prepare bead barcodes. In the last step, you use pBB4 and pBB5 to "functionalise" the beads depending on what you need. If you want to investigate gene expressions, your pBB5 should contains UMI and oligo-dT, which is demonstrate on this page. If you are interested in chromatin accessibility, you can change pBB5 by putting Nextera sequence on it. You can customise pBB5 based on your own need.

+

As the paper suggested, this is not really about scRNA-seq or any specific assays. Instead, it provides a modular procedures to prepare barcoded beads for high-throughput droplet single-cell assays for anything you are interested in (RNA, DNA, chromatin accessibility etc.). The point here is that you use Steps (1) - (7) described below to prepare bead barcodes. In the last step, you use pBB4 and pBB5 to "functionalise" the beads depending on what you need. If you want to investigate gene expressions, your pBB5 should contains UMI and oligo-dT, which is demonstrate on this page. If you are interested in chromatin accessibility, you can change pBB5 by putting Nextera sequence on it. You can customise pBB5 based on your own need.


@@ -27,7 +27,7 @@

Adapter and primer sequences:

plate-3-Sp (this is Supp. Table 6): 5'- /5Phos/[8-bp barcode3 rc] -3'

pBB4: 5'- CTCGAATAGG -3'

pBB5: 5'- /5Phos/TTCGAG[8-bp UMI]TTTTTTTTTTTTTTTTTTTTTTTTV -3'

-

* Plain barcodes (96) can be found here: delley2021_barcode.txt. The actual cell barcodes are the combinations of the same set of 96 barcodes.

+

* Plain barcodes (96) can be found here: delley2021_barcode.txt. The actual cell barcodes are the combinations of the same set of 96 barcodes.


diff --git a/methods_html/Drop-ChIP.html b/methods_html/Drop-ChIP.html index 34850dc..cd9dbf5 100644 --- a/methods_html/Drop-ChIP.html +++ b/methods_html/Drop-ChIP.html @@ -9,12 +9,12 @@

Drop-ChIP

-

Drop-ChIP was pulished in Nature Biotechnology 33, 165–1172 (2015), by Assaf Rotem, Oren Ram and Noam Shoresh in the labs of David Weitz and Bradely Bernstein. This page will only demonstrate the molecular biology of library construction. To look at the real equipment and how the experiments were carried out, see the Supplementary Information from the original publication and the Drop-ChIP webpage.

+

Drop-ChIP was published in Nature Biotechnology 33, 165-1172 (2015), by Assaf Rotem, Oren Ram and Noam Shoresh in the labs of David Weitz and Bradely Bernstein. This page will only demonstrate the molecular biology of library construction. To look at the real equipment and how the experiments were carried out, see the Supplementary Information from the original publication and the Drop-ChIP webpage.


Adapter and primer sequences:

-

Barcoded adapters (BA). These are double stranded DNA and there are 1,152 of them. Click here to see all of them:

+

Barcoded adapters (BA). These are double stranded DNA and there are 1,152 of them. Click here to see all of them:

 
 5'- TTAANNNNNNNNGTATCCGGGGGACCTTAATTAAGGTGGGGGGGATACNNNNNNNNTTAA -3'
@@ -24,7 +24,7 @@ 
Adapter and primer sequences:
-

*Illumina Truseq forked adapters:

+

*Illumina Truseq forked adapters:

 
         5'- AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGAC
@@ -34,7 +34,7 @@ 
Adapter and primer sequences:
-

Other adapters/primers used:

+

Other adapters/primers used:

SC-PCR1: 5'- TAAGGTGGGGGGGATAC -3'

@@ -47,8 +47,8 @@

Adapter and primer sequences:

 -

* It was not entirely clear what adapters were used in the protocol. These are educational guess, not confirmed!!! However, based on the sequenicng data, these are likely to be the case. See the sequence immediately after the 'grep'ed sequence, one of them (GATCGGAAGAGCACA...) that's Truseq adapters (the others are probably bacoded adapters (BA) concatemers):

-grep sequence +

* It was not entirely clear what adapters were used in the protocol. These are educational guess, not confirmed!!! However, based on the sequenicng data, these are likely to be the case. See the sequence immediately after the 'grep'ed sequence, one of them (GATCGGAAGAGCACA...) that's Truseq adapters (the others are probably bacoded adapters (BA) concatemers):

+grep sequence
diff --git a/methods_html/Drop-seq.html b/methods_html/Drop-seq.html index 660c223..2c18b29 100644 --- a/methods_html/Drop-seq.html +++ b/methods_html/Drop-seq.html @@ -3,13 +3,13 @@ -Drop-seq +Drop-seq/Seq-Well

Drop-seq / Seq-Well

-

In the original pulication in Cell 161, 1202-1214 (2015), there are two batches of beads, with only two base pairs difference. Here, Beads-oligo-dT-seqA was used as demonstration. Seq-Well used exact the same oligo design with Drop-seq with Beads-oligo-dT-seqB, which was published in Nature Methods 14, 395–398 (2017).

+

In the original publication in Cell 161, 1202-1214 (2015), there are two batches of beads, with only two base pairs difference. Here, Beads-oligo-dT-seqA was used as demonstration. Seq-Well used exact the same oligo design with Drop-seq with Beads-oligo-dT-seqB, which was published in Nature Methods 14, 395-398 (2017).


@@ -18,7 +18,7 @@

Adapter and primer sequences:

Beads-oligo-dT-seqA: |--5'- TTTTTTTAAGCAGTGGTATCAACGCAGAGTACGT[12-bp cell barcode][8-bp UMI]TTTTTTTTTTTTTTTTTTTTTTTTTTTTTT -3'

Beads-oligo-dT-seqB: |--5'- TTTTTTTAAGCAGTGGTATCAACGCAGAGTAC[12-bp cell barcode][8-bp UMI]TTTTTTTTTTTTTTTTTTTTTTTTTTTTTT -3'

Template Switching Oligo (TSO): 5'- AAGCAGTGGTATCAACGCAGAGTGAATrGrGrG -3'

-

ISPCR: 5′- AAGCAGTGGTATCAACGCAGAGT -3′

+

ISPCR: 5'- AAGCAGTGGTATCAACGCAGAGT -3'

Nextera Tn5 binding site (19-bp Mosaic End (ME)): 5'- AGATGTGTATAAGAGACAG -3'

Nextera N/S5xx primer entry point (s5): 5'- TCGTCGGCAGCGTC -3'

Nextera N7xx primer entry point (s7): 5'- GTCTCGTGGGCTCGG -3'

diff --git a/methods_html/G_and_T_seq.html b/methods_html/G_and_T_seq.html index e2299d1..e09da61 100644 --- a/methods_html/G_and_T_seq.html +++ b/methods_html/G_and_T_seq.html @@ -9,7 +9,7 @@

G&T-seq

-

The genome and transcriptome sequencing (G&T-seq) uses oligo-dT beads to physically separate mRNA and gDNA from single celss. For mRNA-seq, it uses the SMART-seq method to construct sequencing library; for gDNA, you can choose MALBAC, LIANTI or other methods to construct gDNA sequencing library. Click the links to those methods to see how exactly libraries are made.

+

The genome and transcriptome sequencing (G&T-seq) uses oligo-dT beads to physically separate mRNA and gDNA from single celss. For mRNA-seq, it uses the SMART-seq method to construct sequencing library; for gDNA, you can choose MALBAC, LIANTI or other methods to construct gDNA sequencing library. Click the links to those methods to see how exactly libraries are made.

diff --git a/methods_html/HyDrop_ATAC.html b/methods_html/HyDrop_ATAC.html index 8ef66d0..8fab174 100644 --- a/methods_html/HyDrop_ATAC.html +++ b/methods_html/HyDrop_ATAC.html @@ -8,17 +8,17 @@

HyDrop-ATAC

-

The HyDrop protocols were published in eLife on Feb 23th, 2022 (De Rop et al. eLife 11:e73971). The authors developed an open source droplet system, HyDrop, as a hybrid method between inDrop and Drop-Seq. They optimised various steps during the library construction procedures and utilised a dissolvable hydrogel beads to improve barcoded primer release and diffusion.

+

The HyDrop protocols were published in eLife on Feb 23th, 2022 (De Rop et al. eLife 11:e73971). The authors developed an open source droplet system, HyDrop, as a hybrid method between inDrop and Drop-Seq. They optimised various steps during the library construction procedures and utilised a dissolvable hydrogel beads to improve barcoded primer release and diffusion.

-

Both scRNA-seq and scATAC-seq can be performed on the HyDrop platform. In this page, Hydrop-ATAC is presented. This page is basically a recreation of the Supplementary Files 1 & 2 from the publication. Detailed protocol can be found here: protocols.io.

+

Both scRNA-seq and scATAC-seq can be performed on the HyDrop platform. In this page, Hydrop-ATAC is presented. This page is basically a recreation of the Supplementary Files 1 & 2 from the publication. Detailed protocol can be found here: protocols.io.

-

For scRNA-seq on the HyDrop platform, go to the HyDrop-RNA page for a detailed step-by-step illustration.

+

For scRNA-seq on the HyDrop platform, go to the HyDrop-RNA page for a detailed step-by-step illustration.


Adapter and primer sequences:

-

* The complete full list of oligo sequences can be found in this spreadsheet from their protocols.io page: 20210712_supp_methods_table_hydrop_oligonucleotide_list.xlsx.

+

* The complete full list of oligo sequences can be found in this spreadsheet from their protocols.io page: 20210712_supp_methods_table_hydrop_oligonucleotide_list.xlsx.


diff --git a/methods_html/HyDrop_RNA.html b/methods_html/HyDrop_RNA.html index 8fb726d..200371e 100644 --- a/methods_html/HyDrop_RNA.html +++ b/methods_html/HyDrop_RNA.html @@ -8,17 +8,17 @@

HyDrop-RNA

-

The HyDrop protocols were published in eLife on Feb 23th, 2022 (De Rop et al. eLife 11:e73971). The authors developed an open source droplet system, HyDrop, as a hybrid method between inDrop and Drop-Seq. They optimised various steps during the library construction procedures and utilised a dissolvable hydrogel beads to improve barcoded primer release and diffusion.

+

The HyDrop protocols were published in eLife on Feb 23th, 2022 (De Rop et al. eLife 11:e73971). The authors developed an open source droplet system, HyDrop, as a hybrid method between inDrop and Drop-Seq. They optimised various steps during the library construction procedures and utilised a dissolvable hydrogel beads to improve barcoded primer release and diffusion.

-

Both scRNA-seq and scATAC-seq can be performed on the HyDrop platform. In this page, Hydrop-RNA is presented. This page is basically a recreation of the Supplementary Files 1 & 3 from the publication. Detailed protocol can be found here: protocols.io.

+

Both scRNA-seq and scATAC-seq can be performed on the HyDrop platform. In this page, Hydrop-RNA is presented. This page is basically a recreation of the Supplementary Files 1 & 3 from the publication. Detailed protocol can be found here: protocols.io.

-

For scATAC-seq on the HyDrop platform, go to the HyDrop-ATAC page for a detailed step-by-step illustration.

+

For scATAC-seq on the HyDrop platform, go to the HyDrop-ATAC page for a detailed step-by-step illustration.


Adapter and primer sequences:

-

* The complete full list of oligo sequences can be found in this spreadsheet from their protocols.io page: 20210712_supp_methods_table_hydrop_oligonucleotide_list.xlsx.

+

* The complete full list of oligo sequences can be found in this spreadsheet from their protocols.io page: 20210712_supp_methods_table_hydrop_oligonucleotide_list.xlsx.


diff --git a/methods_html/ISSAAC-seq.html b/methods_html/ISSAAC-seq.html index ae6e2ef..6c5e923 100644 --- a/methods_html/ISSAAC-seq.html +++ b/methods_html/ISSAAC-seq.html @@ -10,7 +10,7 @@

ISSAAC-seq FACS (in plates) / ISSAAC-seq Droplet

-

ISSAAC-seq (In Situ SHERRY After ATAC-seq) is a multiomics technique that performs ATAC and RNA from the same cells. It combines SHERRY and ATAC in situ, followed by single nulcei isolation and library preparation. There are two workflows: FACS and Droplet. The sequence design of FACS and Droplet workflows is extermely similar.

+

ISSAAC-seq (In Situ SHERRY After ATAC-seq) is a multiomics technique that performs ATAC and RNA from the same cells. It combines SHERRY and ATAC in situ, followed by single nuclei isolation and library preparation. There are two workflows: FACS and Droplet. The sequence design of FACS and Droplet workflows is extremely similar.


diff --git a/methods_html/LIANTI.html b/methods_html/LIANTI.html index dddac15..b2d004e 100644 --- a/methods_html/LIANTI.html +++ b/methods_html/LIANTI.html @@ -8,7 +8,7 @@

LIANTI

-

The Linear Amplification via Transposon Insertion (LIANTI) combines Tn5 transposition and T7 in vitro transcription for linear amplification of genomic DNA from single cells. It does not have the limitaion used in the traditional Tn5 where only fragments with different ends can be amplified. In LIANTI, due to T7 in vitro transcription, every single cutting events are amplifiable. This greatly improves the

+

The Linear Amplification via Transposon Insertion (LIANTI) combines Tn5 transposition and T7 in vitro transcription for linear amplification of genomic DNA from single cells. It does not have the limitation used in the traditional Tn5 where only fragments with different ends can be amplified. In LIANTI, due to T7 in vitro transcription, every single cutting events are amplifiable. This greatly improves the


diff --git a/methods_html/MALBAC.html b/methods_html/MALBAC.html index 4c60abe..7f13f99 100644 --- a/methods_html/MALBAC.html +++ b/methods_html/MALBAC.html @@ -55,10 +55,10 @@

(3) There are two types of templates at this stage:

-

(4) Again, melt DNA at 94 ℃, quench at 0 ℃ for the MALBAC random primers anealling, and extension at 68 ℃ (both the original genomic DNA and the Semi-amplicon will be used as templates):

+

(4) Again, melt DNA at 94 ℃, quench at 0 ℃ for the MALBAC random primers annealing, and extension at 68 ℃ (both the original genomic DNA and the Semi-amplicon will be used as templates):

 
- Origincal genomic DNA as template (will generate new Semi-amplicon):
+ Original genomic DNA as template (will generate new Semi-amplicon):
 
 5'- GTGAGTGATGGTTGAGGTAGTGTGGAG          5'- GTGAGTGATGGTTGAGGTAGTGTGGAG          5'- GTGAGTGATGGTTGAGGTAGTGTGGAG
                                NNNNNNNN-------------------------------->NNNNNNNN-------------------------------->NNNNNNNN----------->
@@ -76,7 +76,7 @@ 
(4) Again, melt DNA at 94 ℃, quench at 0 ℃ for the MALBAC random
 
(5) There are three types of DNA at this stage:

 
 
- Origincal genomic DNA:
+ Original genomic DNA:
 
 3'- XXXXXXXXXXXXXXXXXX...XXXXXXXXXXXXXXXXXX -5'
 
@@ -124,14 +124,14 @@ 
(8) Final product of the amplified gDNA, and this can be used as the staring
 
(9) Final library structure (depends on the library preparation kits used, two most popular ones are shown below):

 
 
-Uisng Nextera:
+Using Nextera:
 
 5'- AATGATACGGCGACCACCGAGATCTACACNNNNNNNNTCGTCGGCAGCGTCAGATGTGTATAAGAGACAGXXXXXXXX...XXXXXXXXCTGTCTCTTATACACATCTCCGAGCCCACGAGACNNNNNNNNATCTCGTATGCCGTCTTCTGCTTG
     TTACTATGCCGCTGGTGGCTCTAGATGTGNNNNNNNNAGCAGCCGTCGCAGTCTACACATATTCTCTGTCXXXXXXXX...XXXXXXXXGACAGAGAATATGTGTAGAGGCTCGGGTGCTCTGNNNNNNNNTAGAGCATACGGCAGAAGACGAAC -5'
            Illumina P5              i5         s5              ME                gDNA                ME               s7          i7            Illumina P7
 
 
-Uisng TruSeq:
+Using TruSeq:
 
 5'- AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCTXXX...XXXAGATCGGAAGAGCACACGTCTGAACTCCAGTCACNNNNNNNNATCTCGTATGCCGTCTTCTGCTTG -3'
 3'- TTACTATGCCGCTGGTGGCTCTAGATGTGAGAAAGGGATGTGCTGCGAGAAGGCTAGAXXX...XXXTCTAGCCTTCTCGTGTGCAGACTTGAGGTCAGTGNNNNNNNNTAGAGCATACGGCAGAAGACGAAC -5'
@@ -144,7 +144,7 @@ 
(9) Final library structure (depends on the library preparation kits used, t
 
 
Library sequencing:

 
-
Standard sequencing workflow is used, and you can check any other pages.

+
Standard sequencing workflow is used, and you can check any other pages.

 
 
 
diff --git a/methods_html/MARS-seq.html b/methods_html/MARS-seq.html
index 81838f2..3ed09e6 100644
--- a/methods_html/MARS-seq.html
+++ b/methods_html/MARS-seq.html
@@ -9,15 +9,15 @@
 
 
MARS-seq / MARS-seq2.0

 
-
 (1) I think the sequence of "2nd RT Primer" in Supplementary Table S7 in the original publication (Science 343, 776-779 (2014)) might be in the wrong orientation (i.e. they showed the sequence from 3' -> 5').

+
 (1) I think the sequence of "2nd RT Primer" in Supplementary Table S7 in the original publication (Science 343, 776-779 (2014)) might be in the wrong orientation (i.e. they showed the sequence from 3' -> 5').

 
-
 (2) The author claimed in the Supplementary Method (Page 8) that UMIs are 4-8 bp in length, but from the oligo sequence in Supplementary Table 7, they are only 4 bp in length. 4 bp were drawn in this workflow here.

+
 (2) The author claimed in the Supplementary Method (Page 8) that UMIs are 4-8 bp in length, but from the oligo sequence in Supplementary Table S7, they are only 4 bp in length. 4 bp were drawn in this workflow here.

 
-
 (3) The author claimed in the Supplementary Method (Page 8) that plate barcodes are 6 bp in length, but from the oligo sequence in Supplementary Table 8, they seem to be 7 bp in length (4bp + 3 Ns). Maybe only 6 bp was only used to identify a plate. I'm not entirely sure about this.

+
 (3) The author claimed in the Supplementary Method (Page 8) that plate barcodes are 6 bp in length, but from the oligo sequence in Supplementary Table S8, they seem to be 7 bp in length (4bp + 3 Ns). Maybe only 6 bp was only used to identify a plate. I'm not entirely sure about this.

 
-
 (4) In May, 2019, MARS-seq2.0 was published in Nature Protocol, and the oligos used in the protocol is almost the same to the original MARS-seq. The cell barcodes and UMIs are longer in MARS-seq2.0. The improvements are mainly related to throughput, robustness, noise reduction and costs. Check the publication for more details.

+
 (4) In May, 2019, MARS-seq2.0 was published in Nature Protocol, and the oligos used in the protocol is almost the same to the original MARS-seq. The cell barcodes and UMIs are longer in MARS-seq2.0. The improvements are mainly related to throughput, robustness, noise reduction and costs. Check the publication for more details. The exact sequences for the RT1 primers and ligation adaptor primers can be found in the Supplementary Table 1 and Supplementary Table 2 of the Nature Protocols paper.

 
-
 (5) Oligos used in the original MARS-seq were shown in this workflow.

+
 (5) Oligos used in the original MARS-seq were shown in this workflow.

 
 

 
@@ -36,8 +36,8 @@ 
Adapter and primer sequences:

 
2nd RT primer: 5'- CTACACGACGCTCTTCCGATCT -3'

 
P5_Rd1_PCR primer: 5'- AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT -3'

 
P7_Rd2_PCR primer: 5'- CAAGCAGAAGACGGCATACGAGATGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT -3'

-
Illumina Truse Read1 primer: 5'- ACACTCTTTCCCTACACGACGCTCTTCCGATCT -3'

-
Illumina Truse Read2 primer: 5'- GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT -3'

+
Illumina TruSeq Read1 primer: 5'- ACACTCTTTCCCTACACGACGCTCTTCCGATCT -3'

+
Illumina TruSeq Read2 primer: 5'- GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT -3'

 
Illumina P5 adapter: 5'- AATGATACGGCGACCACCGAGATCTACAC -3'

 
Illumina P7 adapter: 5'- CAAGCAGAAGACGGCATACGAGAT -3'

 
diff --git a/methods_html/Microwell-seq.html b/methods_html/Microwell-seq.html
index 5f91f96..46d9b61 100644
--- a/methods_html/Microwell-seq.html
+++ b/methods_html/Microwell-seq.html
@@ -9,7 +9,7 @@
 
 
Microwell-seq

 
-
Microwell-seq is like a well-based inDrop system. According to their Supplementary Table S2, there are two batches of beads-oligos: indexed bead seqA and indexed bead seqB. There are only two bases difference. Based on the sequence of A1-A96 in their Supplementary Table S1, it seems indexed bead seqA was used.

+
Microwell-seq is like a well-based inDrop system. According to their Supplementary Table S2, there are two batches of beads-oligos: indexed bead seqA and indexed bead seqB. There are only two bases difference. Based on the sequence of A1-A96 in their Supplementary Table S1, it seems indexed bead seqA was used.

 
 

 
@@ -32,9 +32,9 @@ 
Sequence used during the experiment:

 
 
Sequence used for Indexed-Beads-seqA/B generation before the experiment:

 
-
A1-A96 (96 of them in 96 individual wells): 5'- TTTAGGGATAACAGGGTAATAAGCAGTGGTATCAACGCAGAGTACGT[barcode1]CGACTCACTACAGGG -3', for the sequences of 96 different barcode1, see Microwell_A1-A96.txt.

-
B1-B96 (96 of them in 96 individual wells): 5'- CGATCGTGTCACCGA[barcode2]CCCTGTAGTGAGTCG -3', for the sequences of 96 different barcode2, see Microwell_B1-B96.txt.

-
C1-C96 (96 of them in 96 individual wells): 5'- (A)30[6-bp UMI][barcode3]CGATCGTGTCACCGA -3', for the sequences of 96 different barcode3, see Microwell_C1-C96.txt.

+
A1-A96 (96 of them in 96 individual wells): 5'- TTTAGGGATAACAGGGTAATAAGCAGTGGTATCAACGCAGAGTACGT[barcode1]CGACTCACTACAGGG -3', for the sequences of 96 different barcode1, see Microwell_A1-A96.txt.

+
B1-B96 (96 of them in 96 individual wells): 5'- CGATCGTGTCACCGA[barcode2]CCCTGTAGTGAGTCG -3', for the sequences of 96 different barcode2, see Microwell_B1-B96.txt.

+
C1-C96 (96 of them in 96 individual wells): 5'- (A)30[6-bp UMI][barcode3]CGATCGTGTCACCGA -3', for the sequences of 96 different barcode3, see Microwell_C1-C96.txt.

 
 
 

diff --git a/methods_html/PIP-seq.html b/methods_html/PIP-seq.html
index ead3c4b..c3b7fd9 100644
--- a/methods_html/PIP-seq.html
+++ b/methods_html/PIP-seq.html
@@ -8,21 +8,21 @@
 
 
 
PIP-seq

-
The PIP-seq method was published in Nature Biotechnology on 2023 March 06: Clark et al. Nat Biotechnol. 2023 [Clark2023]. It can be used to perform high-throughput droplet single cell RNA-seq without microfluidic device (well ... you still need one to generate the hydrogel beads). Mix your cells, RT reagents, barcoded hydrogel beads and oil in a tube, then a simple vortex. You are done with droplets generation and cell encapsulation. It is for real!

+
The PIP-seq method was published in Nature Biotechnology on 2023 March 06: Clark et al. Nat Biotechnol. 2023 [Clark2023]. It can be used to perform high-throughput droplet single cell RNA-seq without microfluidic device (well ... you still need one to generate the hydrogel beads). Mix your cells, RT reagents, barcoded hydrogel beads and oil in a tube, then a simple vortex. You are done with droplets generation and cell encapsulation. It is for real!

 
-
Before you start to look at the library construction procedures, there are a few technical notes you need to know. Basically, there are a few different versions of PIP-seq, and they are quite different!

+
Before you start to look at the library construction procedures, there are a few technical notes you need to know. Basically, there are a few different versions of PIP-seq, and they are quite different!

 
-
NOTE 1: In the Clark2023 paper, the authors indicated that the barcode design is based on the previous study published in Scientific Reports: Modular barcode beads for microfluidic single cell genomics [Delley2021].. However, they change the oligo design in PIP-seq. If you want to see how the method in the Delley2021 paper works, go to this page.

+
NOTE 1: In the Clark2023 paper, the authors indicated that the barcode design is based on the previous study published in Scientific Reports: Modular barcode beads for microfluidic single cell genomics [Delley2021].. However, they change the oligo design in PIP-seq. If you want to see how the method in the Delley2021 paper works, go to this page.

 
-
NOTE 2: If you look into the FastQ files in the Clark2023 paper, you will realise that there are at least three different library structures. The first type, which I call v1prototype, was used in the experiments of looking at the single-cell transcriptional responses of two cancer cell lines (H1975 and PC9) to gefitinibis, e.g. SRR19086115, SRR19086119 and a few additional samples. This library type is probably obsolete now and no longer used. Its linker sequence and barcode configuration resembles those in inDrop V2. The library structure is so different from the latest PIP-seq libraries, so I put it into a different page for the sake of record keeping. You can visit this page to have a look.

+
NOTE 2: If you look into the FastQ files in the Clark2023 paper, you will realise that there are at least three different library structures. The first type, which I call v1prototype, was used in the experiments of looking at the single-cell transcriptional responses of two cancer cell lines (H1975 and PC9) to gefitinibis, e.g. SRR19086115, SRR19086119 and a few additional samples. This library type is probably obsolete now and no longer used. Its linker sequence and barcode configuration resembles those in inDrop V2. The library structure is so different from the latest PIP-seq libraries, so I put it into a different page for the sake of record keeping. You can visit this page to have a look.

 
-
NOTE 3: The second type, which I call PIP-seq V2 here, was used in the species mixing experiment (for example, SRR19180490) in the Clark2023 paper. The cell barcodes consists of three rounds of split-pool. It is probably obsolete as well, but it is similar to the latest PIP-seq version, so I show it here on this page.

+
NOTE 3: The second type, which I call PIP-seq V2 here, was used in the species mixing experiment (for example, SRR19180490) in the Clark2023 paper. The cell barcodes consists of three rounds of split-pool. It is probably obsolete as well, but it is similar to the latest PIP-seq version, so I show it here on this page.

 
-
NOTE 4: The third type, called FluentBio PIPseq™ V3.0, was used in many samples in the Clark2023 paper (for example, SRR19184609, SRR21853664 and many others). FluentBio is the company that provides the commercial version of the PIP-seq method. FluentBio PIPseq™ V3.0 was the main library structure back in 2022. This is shown in this page and should be your starting point if you want to analyse the PIP-seq data by yourself.

+
NOTE 4: The third type, called FluentBio PIPseq™ V3.0, was used in many samples in the Clark2023 paper (for example, SRR19184609, SRR21853664 and many others). FluentBio is the company that provides the commercial version of the PIP-seq method. FluentBio PIPseq™ V3.0 was the main library structure back in 2022. This is shown in this page and should be your starting point if you want to analyse the PIP-seq data by yourself.

 
-
NOTE 5: Early 2023, the company announced FluentBio PIPseq™ V4.0. There is no example data at this time of writing (2023 May 27). You can check their news from their website. When example data are available, I will update here. Update (2023-July-02): the V4 FASTQ files are available on the website, the whitelists are exactly the same as V3. The library structure is also extremely similar to V3, except there are 0 - 3 bases at the beginning of Read 1 in V4 to desync the sequencing cycles. See this twitter thread for more details.

+
NOTE 5: Early 2023, the company announced FluentBio PIPseq™ V4.0. There is no example data at this time of writing (2023 May 27). You can check their news from their website. When example data are available, I will update here. Update (2023-July-02): the V4 FASTQ files are available on the website, the whitelists are exactly the same as V3. The library structure is also extremely similar to V3, except there are 0 - 3 bases at the beginning of Read 1 in V4 to desync the sequencing cycles. See this twitter thread for more details.

 
-
NOTE 6: About the name: PIP-seq or PIPseq™ ? Well ... PIP-seq is from the Clark2023 paper, and PIPseq™ is the commerical product.

+
NOTE 6: About the name: PIP-seq or PIPseq™ ? Well ... PIP-seq is from the Clark2023 paper, and PIPseq™ is the commerical product.

 
 

 
@@ -37,9 +37,9 @@ 
Adapter and primer sequences:

 
 
*  The oligo sequences presented here are based on Read 1 FastQ from SRR19180490 and the method section of the Clark2023 paper, so the final sequences and library structures should be correct (the exact procedures to produce the beads and library might not be). The cell barcodes are the combination of three rounds of split-pool ligations. Plain files for the barcodes in each round can be downloaded as follows:

 
-
PIP-seq V2 Round 1 Barcodes (96)

-
PIP-seq V2 Round 2 Barcodes (96)

-
PIP-seq V2 Round 3 Barcodes (96)

+
PIP-seq V2 Round 1 Barcodes (96)

+
PIP-seq V2 Round 2 Barcodes (96)

+
PIP-seq V2 Round 3 Barcodes (96)

 
 
Sequence used for barcoded bead generation (before the PIP-seq experiment):

 
@@ -261,12 +261,12 @@ 
(3) Cluster regeneration, add Nextera Read 2 primer to sequence the second r
 
 
FluentBio PIPseq™ V3.0 & V4.0

 
-
* The commercial kit comes with barcoded hydrogel beads, so you do not need to worry about generating them yourself. We can start immediately with cell encapsulation. The sequences here are based on educational guesses based on the example data sets from their website. In this version, the cell barcodes are the combination of four rounds of split-pool ligations. The same set of four barcodes are used in V3.0 and V4.0. Therefore, the whitelist is the same for V3.0 and V4.0. Plain files for the barcodes in each round can be downloaded as follows:

+
* The commercial kit comes with barcoded hydrogel beads, so you do not need to worry about generating them yourself. We can start immediately with cell encapsulation. The sequences here are based on educational guesses based on the example data sets from their website. In this version, the cell barcodes are the combination of four rounds of split-pool ligations. The same set of four barcodes are used in V3.0 and V4.0. Therefore, the whitelist is the same for V3.0 and V4.0. Plain files for the barcodes in each round can be downloaded as follows:

 
-
FluentBio PIPseq™ V3.0 Round 1 Barcodes (8bp, 96)

-
FluentBio PIPseq™ V3.0 Round 2 Barcodes (6bp, 96)

-
FluentBio PIPseq™ V3.0 Round 3 Barcodes (6bp, 96)

-
FluentBio PIPseq™ V3.0 Round 4 Barcodes (8bp, 96)

+
FluentBio PIPseq™ V3.0 Round 1 Barcodes (8bp, 96)

+
FluentBio PIPseq™ V3.0 Round 2 Barcodes (6bp, 96)

+
FluentBio PIPseq™ V3.0 Round 3 Barcodes (6bp, 96)

+
FluentBio PIPseq™ V3.0 Round 4 Barcodes (8bp, 96)

 
 
Adapter and primer sequences:

     
diff --git a/methods_html/PIP-seq_v1p.html b/methods_html/PIP-seq_v1p.html
index acc0c69..16b2d96 100644
--- a/methods_html/PIP-seq_v1p.html
+++ b/methods_html/PIP-seq_v1p.html
@@ -10,7 +10,7 @@
 
 
PIP-seq V1 Prototype

 
-
This library structure was only used in some of the samples, such as SRR19086115, SRR19086119 and a few additional samples in the gefitinibis response experiments. The beads generation procedures should be similar to inDrop and Delley2021. There is very little information about this library structure, so this page is based on educational guesses.

+
This library structure was only used in some of the samples, such as SRR19086115, SRR19086119 and a few additional samples in the gefitinibis response experiments. The beads generation procedures should be similar to inDrop and Delley2021. There is very little information about this library structure, so this page is based on educational guesses.

 
 

 
diff --git a/methods_html/Paired-seq.html b/methods_html/Paired-seq.html
index 937f41f..119fe67 100644
--- a/methods_html/Paired-seq.html
+++ b/methods_html/Paired-seq.html
@@ -9,7 +9,7 @@
 
 
Paired-seq

 
-
The Paired-seq method is developed based on the idea of combinatorial indexing stratgy that is used in sci-RNA-seq and SPLiT-seq to simultaneously tag both the open chromatin fragments generated by the Tn5 transposases and the cDNA molecules generated from reverse transcription.

+
The Paired-seq method is developed based on the idea of combinatorial indexing strategy that is used in sci-RNA-seq and SPLiT-seq to simultaneously tag both the open chromatin fragments generated by the Tn5 transposases and the cDNA molecules generated from reverse transcription.

 
 

 
@@ -38,7 +38,7 @@ 
Adapter and primer sequences:

 
Read 2 sequencing primer: 5'- GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT -3'

 
Illumina P5 adapter: 5'- AATGATACGGCGACCACCGAGATCTACAC -3'
 
Illumina P7 adapter: 5'- CAAGCAGAAGACGGCATACGAGAT -3'
-
*There are 8 BC#01 barcodes, 96 BC#02_well_barcodes, 96 BC#03_well_barcodes and 96 BC#04_well_barcodes. To see the full sequence details, check the Supplementary Table S1 and Supplementary Table S2 from the Paired-seq publication. PB means "phase block" which are variable length DNA bases to increase the complexity of each sequencing cycle.

+
*There are 8 BC#01 barcodes, 96 BC#02_well_barcodes, 96 BC#03_well_barcodes and 96 BC#04_well_barcodes. To see the full sequence details, check the Supplementary Table S1 and Supplementary Table S2 from the Paired-seq publication. PB means "phase block" which are variable length DNA bases to increase the complexity of each sequencing cycle.

 
 
 
@@ -70,7 +70,7 @@ 
(1) Prepare ligation adapters by annealing barcodes with correponding linker
 

 
 
(2) Assemble transposase with Barcoded_Tn5_top (BC#01 DNA) and ME_bottom to tag open chromatin DNA:

-Tn5 dimer
+Tn5 dimer
 
 
 mRNA (unaffected, but cytoplasmic mRNA will probably leak out):
diff --git a/methods_html/Quartz-seq_family.html b/methods_html/Quartz-seq_family.html
index 73b8de5..ce70928 100644
--- a/methods_html/Quartz-seq_family.html
+++ b/methods_html/Quartz-seq_family.html
@@ -10,7 +10,7 @@
 
Quartz-seq
 / Quartz-seq2

 
-
The detailed step-by-step experimental protocols of Quartz-seq and Quartz-seq2 can be found at the RIKEN's website. Quartz-seq cosntructs the library of each single cell spearately, so it has low plexity. The indices are from Illumina Truseq adapters. The presence of T7 promoter sequence is only for Quartz-chip. Quartz-seq2 increased the throughput by using barcoded RT primer (1536 barcoded RT primers) to profile 3' end of cDNA. The cell barcode sequence can be found here.

+
The detailed step-by-step experimental protocols of Quartz-seq and Quartz-seq2 can be found at the RIKEN's website. Quartz-seq constructs the library of each single cell separately, so it has low plexity. The indices are from Illumina Truseq adapters. The presence of T7 promoter sequence is only for Quartz-chip. Quartz-seq2 increased the throughput by using barcoded RT primer (1536 barcoded RT primers) to profile 3' end of cDNA. The cell barcode sequence can be found here.

 
 

 
@@ -20,7 +20,7 @@ 
Adapter and primer sequences:

 
 
RT primer (WTA): 5'- TATAGAATTCGCGGCCGCTCGCGATAATACGACTCACTATAGGGCGTTTTTTTTTTTTTTTTTTTTTTTT -3'

 
Tagging primer: 5'- TATAGAATTCGCGGCCGCTCGCGATTTTTTTTTTTTTTTTTTTTTTTT -3'

-
Suppression primer: 5′- (NH2)-GTATAGAATTCGCGGCCGCTCGCGAT -3′

+
Suppression primer: 5'- (NH2)-GTATAGAATTCGCGGCCGCTCGCGAT -3'

 
TRSU: 5'- AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATC*T -3'

 
TRSI (indexed oligo): 5'- GATCGGAAGAGCACACGTCTGAACTCCAGTCACNNNNNNATCTCGTATGCCGTCTTCTGCTT*G -3'

 
@@ -114,7 +114,7 @@ 
(5) Products after second strand synthesis:

 
(6) Adding Suppression primer for single primer cDNA amplification:( i.e. semi-suppressive PCR )

 
 
-5′- (NH2)-GTATAGAATTCGCGGCCGCTCGCGAT---------->
+5'- (NH2)-GTATAGAATTCGCGGCCGCTCGCGAT---------->
        5'- TATAGAATTCGCGGCCGCTCGCGA(dT)XXXXX...XXXXX(pA)CGCCCTATAGTGAGTCGTATTATCGCGAGCGGCCGCGAATTCTATA
            ATATCTTAAGCGCCGGCGAGCGCT(pA)XXXXX...XXXXX(dT)GCGGGATATCACTCAGCATAATAGCGCTCGCCGGCGCTTAAGATAT -5'
 					                           <---------TAGCGCTCGCCGGCGCTTAAGATATG-(NH2) -5'
diff --git a/methods_html/SCRB-seq.html b/methods_html/SCRB-seq.html
index e55e02e..d44cb5e 100644
--- a/methods_html/SCRB-seq.html
+++ b/methods_html/SCRB-seq.html
@@ -10,7 +10,7 @@
 
SCRB-seq
 / mcSCRB-seq

 
-
mcSCRB-seq is an improvement over the original SCRB-seq by using a crowding reagent during RT and other stuff. Check the manuscripts for more details. The libray construction strategy is the same.

+
mcSCRB-seq is an improvement over the original SCRB-seq by using a crowding reagent during RT and other stuff. Check the manuscripts for more details. The library construction strategy is the same.

 
 

 
diff --git a/methods_html/SHARE-seq.html b/methods_html/SHARE-seq.html
index 9454855..681d0a9 100644
--- a/methods_html/SHARE-seq.html
+++ b/methods_html/SHARE-seq.html
@@ -9,7 +9,7 @@
 
 
SHARE-seq

 
-
The SHARE-seq method is developed based on the idea of combinatorial indexing stratgy that is used in sci-RNA-seq and SPLiT-seq. The method introduced three rounds of barcodes by ligating barcoded adaptors to both RNA (gene expression) and tagmented DNA (chromatin accessibility) to achieve the multiomic profiling from the same single cells.

+
The SHARE-seq method is developed based on the idea of combinatorial indexing strategy that is used in sci-RNA-seq and SPLiT-seq. The method introduced three rounds of barcodes by ligating barcoded adaptors to both RNA (gene expression) and tagmented DNA (chromatin accessibility) to achieve the multiomic profiling from the same single cells.

 
 

 
@@ -34,7 +34,7 @@ 
Adapter and primer sequences:

 
Read 2 sequencing primer: 5'- GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG -3'

 
Illumina P5 adapter: 5'- AATGATACGGCGACCACCGAGATCTACAC -3'
 
Illumina P7 adapter: 5'- CAAGCAGAAGACGGCATACGAGAT -3'
-
*Ther are 96 barcodes per round, to see the full sequence, check the Supplementary Table S1 from the SHARE-seq publication.

+
*Ther are 96 barcodes per round, to see the full sequence, check the Supplementary Table S1 from the SHARE-seq publication.

 
 
 
diff --git a/methods_html/SMART-seq_family.html b/methods_html/SMART-seq_family.html
index cf77bae..3ff0c88 100644
--- a/methods_html/SMART-seq_family.html
+++ b/methods_html/SMART-seq_family.html
@@ -13,15 +13,15 @@ 
SMART-seq
 / SMART-seq3xpress
 / FLASH-seq

 
-
The protocols of SMART-seq and SMART-seq2 are almost the same. SMART-seq2 is an improved version of SMART-seq. The authors performed 457 optimisation experiments to test conditions. Two key parameters are: 

-
(1) exchanging the last guanylate for a locked nucleic acid (LNA) at the 3' end of TSO.

-
(2) Include methyl group donor betaine in combination with higher MgCl2 concentrations.

-
SMART-seq3 is a further improvement comparing to SMART-seq/SMART-seq2, and it has a different oligo design compared to SMART-seq/SMART-seq2. Major improvements include:

-
(1) Use Maxima H- reverse transcriptase.

-
(2) Use NaCl (instead of the common KCl) during reverse transcription.

-
(3) Use 5% PEG as a crowding reagent during reverse transcription, like mcSCRB-seq.

-
(4) Use a unique 11-bp tag and UMI in the TSO so that the 5' read can be distinguished from the internal reads.

-
Built upon SMART-seq3, SMART-seq3xpress and FLASH-seq made some further improvements in terms of the processing speed and reagent cost by removing some intermediate purification steps and reduce the reaction volume to nanolitre scale.

+
The protocols of SMART-seq and SMART-seq2 are almost the same. SMART-seq2 is an improved version of SMART-seq. The authors performed 457 optimisation experiments to test conditions. Two key parameters are: 

+
(1) exchanging the last guanylate for a locked nucleic acid (LNA) at the 3' end of TSO.

+
(2) Include methyl group donor betaine in combination with higher MgCl2 concentrations.

+
SMART-seq3 is a further improvement comparing to SMART-seq/SMART-seq2, and it has a different oligo design compared to SMART-seq/SMART-seq2. Major improvements include:

+
(1) Use Maxima H- reverse transcriptase.

+
(2) Use NaCl (instead of the common KCl) during reverse transcription.

+
(3) Use 5% PEG as a crowding reagent during reverse transcription, like mcSCRB-seq.

+
(4) Use a unique 11-bp tag and UMI in the TSO so that the 5' read can be distinguished from the internal reads.

+
Built upon SMART-seq3, SMART-seq3xpress and FLASH-seq made some further improvements in terms of the processing speed and reagent cost by removing some intermediate purification steps and reduce the reaction volume to nanolitre scale.

 
 

 
@@ -218,7 +218,7 @@ 
(4) Add read 2 sequencing primer to sequence the second read (top strand as
 
 
SMART-seq3 / SMART-seq3xpress / FLASH-seq

 
-
NOTE: These three methods differ on the experimental details of how the protocol is executed. The library generation procedures are the same, and the final libraries are ALMOST the same. The only differences are that they used different variation of TSO, which makes the 5' fragment a bit different. Here, only SMART-seq3 procedures are shown. The final libraries of these three methods are shown at the end of the workflow.

+
NOTE: These three methods differ on the experimental details of how the protocol is executed. The library generation procedures are the same, and the final libraries are ALMOST the same. The only differences are that they used different variation of TSO, which makes the 5' fragment a bit different. Here, only SMART-seq3 procedures are shown. The final libraries of these three methods are shown at the end of the workflow.

 
 

 
diff --git a/methods_html/SNARE-seq.html b/methods_html/SNARE-seq.html
index 19627db..d4c73ab 100644
--- a/methods_html/SNARE-seq.html
+++ b/methods_html/SNARE-seq.html
@@ -9,7 +9,7 @@
 
 
SNARE-seq

 
-
SNARE-seq was published by Chen et al. in Nature Biotechnology 37, 1452-1457. It was a droplet based technology to profile chromatin accessibility and gene expression from the same cells. The droplet beads are the same as version B of the beads from the Drop-seq method. After tagmentation, it uses a splint oligo, named "Nextera-R1-rc-polyA" to capture the tagmented open chromatin DNA to the beads. Half of the splint oligo is poly A, which is reverse complementary to the bead oligos (oligo-dT); the other half of the splint oligo is reverse complentary to the tagmentation sequence.

+
SNARE-seq was published by Chen et al. in Nature Biotechnology 37, 1452-1457. It was a droplet based technology to profile chromatin accessibility and gene expression from the same cells. The droplet beads are the same as version B of the beads from the Drop-seq method. After tagmentation, it uses a splint oligo, named "Nextera-R1-rc-polyA" to capture the tagmented open chromatin DNA to the beads. Half of the splint oligo is poly A, which is reverse complementary to the bead oligos (oligo-dT); the other half of the splint oligo is reverse complementary to the tagmentation sequence.

 
 

 
@@ -20,7 +20,7 @@ 
Adapter and primer sequences:

 
Nextera-R1-bk: 5'-/phos/ TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG /3InvdT/-3'

 
Nextera-Ad2-bk: 5'-/phos CCGAGCCCACGAGAC /3InvdT/-3'

 
Template Switching Oligo (TSO): 5'- AAGCAGTGGTATCAACGCAGAGTGAATrGrGrG -3'

-
TSO PCR: 5′- AAGCAGTGGTATCAACGCAGAGT -3′

+
TSO PCR: 5'- AAGCAGTGGTATCAACGCAGAGT -3'

 
Nextera Tn5 binding site (19-bp Mosaic End (ME)): 5'- AGATGTGTATAAGAGACAG -3'

 
Nextera N/S5xx primer entry point (s5): 5'- TCGTCGGCAGCGTC -3'

 
Nextera N7xx primer entry point (s7): 5'- GTCTCGTGGGCTCGG -3'

@@ -86,7 +86,7 @@ 
(2) Droplet encapsulation of nuclei, cell lysis, Tn5 release, mRNA capture a
 
(3) Break droplets (i.e. droplets are merely used as a cell capture chamber), add Nextera-R1-bk and Nextera-Ad2-bk to block free oligos (not shown here), reverse transcription and ligation of chromatin DNA to the beads. Since the reverse transcription and ligation are done in the same reaction, and the reverse transcriptase has intrinsic DNA dependent DNA polymerase activity, the gaps of the captured chromatin DNA will be filled in (I think ... not entirely sure though):

 
 
- mRNA (the terminal tranferase acitivity of MMLV adds extra Cs, which allows TSO to be incoporated):
+ mRNA (the terminal transferase activity of MMLV adds extra Cs, which allows TSO to be incorporated):
 
      |--5'- TTTTTTTAAGCAGTGGTATCAACGCAGAGTAC[12-bp cell barcode][8-bp UMI](dT)XXX...XXXCCC----->
                                                                   <-------(pA)XXX...XXXGGGTAAGTGAGACGCAACTATGGTGACGAA -5'
@@ -106,7 +106,7 @@ 
(3) Break droplets (i.e. droplets are merely used as a cell capture chamber)
 
(4) Note for the ATAC library, there is no phosphate at the 5' end of s5, so the top strand will have a gap. There is a phosphate at the 5' end of the Nextera-R1-rc-polyA. Therefore, the gap at the bottom strand will be filled by extension and ligation. The bottom strand will serve as the template for PCR in the next step. Thank Luciano Martelotto and Song Chen for the clarification:

 
 
- mRNA (the terminal tranferase acitivity of MMLV adds extra Cs, which allows TSO to be incoporated):
+ mRNA (the terminal transferase activity of MMLV adds extra Cs, which allows TSO to be incorporated):
 
      |--5'- TTTTTTTAAGCAGTGGTATCAACGCAGAGTAC[12-bp cell barcode][8-bp UMI](dT)XXX...XXXCCCATTCACTCTGCGTTGATACCACTGCTT
             AAAAAAATTCGTCACCATAGTTGCGTCTCATG[12-bp cell barcode][8-bp UMI](pA)XXX...XXXGGGTAAGTGAGACGCAACTATGGTGACGAA -5'
diff --git a/methods_html/SPLiT-seq_archive.html b/methods_html/SPLiT-seq_archive.html
index 8907c3a..594cb49 100644
--- a/methods_html/SPLiT-seq_archive.html
+++ b/methods_html/SPLiT-seq_archive.html
@@ -9,9 +9,9 @@
 
 
SPLiT-seq

 
-
The SPLiT-seq uses the combinatorial indexing to identify single cells without single cell isolation. Multi-level indexing can be performed by ligations. The workflow described here is based on the version 2 of the protocol (published on their google website on 30-Mar-2017, click here), and the strand blocking step is omitted here as I haven't figured out what exactly it does. Three-level indexing strategy is used in the protocol (three rounds of ligation).

+
The SPLiT-seq uses the combinatorial indexing to identify single cells without single cell isolation. Multi-level indexing can be performed by ligations. The workflow described here is based on the version 2 of the protocol (published on their google website on 30-Mar-2017, click here), and the strand blocking step is omitted here as I haven't figured out what exactly it does. Three-level indexing strategy is used in the protocol (three rounds of ligation).

   
-
There are different verrsions of the protocol, and they use slightly different oligo sequences. The basic idea of the method does not change. Check this thread for more information.

+
There are different versions of the protocol, and they use slightly different oligo sequences. The basic idea of the method does not change. Check this thread for more information.

   
 

 
@@ -38,9 +38,9 @@ 
Adapter and primer sequences:

 
Index 1 seuencing primer: 5'- GATCGGAAGAGCACACGTCTGAACTCCAGTCAC -3'

 
Read 2 sequencing primer: 5'- GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT -3'

 
*Ther are 96 barcodes per round, to see the full sequence, click below:

-
Round1_barcodes.txt

-
Round2_barcodes.txt

-
Round3_barcodes.txt

+
Round1_barcodes.txt

+
Round2_barcodes.txt

+
Round3_barcodes.txt

 
 
 
diff --git a/methods_html/STRT-seq_family.html b/methods_html/STRT-seq_family.html
index 5e66ae3..1eb5fae 100644
--- a/methods_html/STRT-seq_family.html
+++ b/methods_html/STRT-seq_family.html
@@ -14,13 +14,13 @@ 
STRT-seq
 

 
 
STRT-seq

-
STRT-seq was originally pulished in Genome Res. 21: 1160-1167 (2011). One year after that, the authors published the detailed protocol in Nature Protocols 7, 813-828 (2012), which is only slightly different from the orginal paper. The workflow shown here is based on the protocol in Nature Protocols 7, 813-828 (2012).

+
STRT-seq was originally pulished in Genome Res. 21: 1160-1167 (2011). One year after that, the authors published the detailed protocol in Nature Protocols 7, 813-828 (2012), which is only slightly different from the original paper. The workflow shown here is based on the protocol in Nature Protocols 7, 813-828 (2012).

 
 
Adapter and primer sequences:

 
 
oligo-dTVN: 5'-Bio- TTAAGCAGTGGTATCAACGCAGAGTCGACTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTVN -3'

 
Barcoded Template Switching Oligo (TSO): 5'- AAGCAGTGGTATCAACGCAGAGTGCAGTGCT[6-bp cell barcode]rGrGrG -3'

-
ISPCR: 5′-Bio- AAGCAGTGGTATCAACGCAGAGT -3′

+
ISPCR: 5'-Bio- AAGCAGTGGTATCAACGCAGAGT -3'

 
*Solexa P1 adapter: 5'- AATGATACGGCGACCACCGA -3'

 
*Solexa P2 adapter: 5'- CAAGCAGAAGACGGCATACGA -3'

 
Library PCR primer 1: 5'- AATGATACGGCGACCACCGAGATCTAAGCAGTGGTATCAACGCAGAGT -3'

diff --git a/methods_html/SeqWell_S3.html b/methods_html/SeqWell_S3.html
index 0c11fa9..eca9115 100644
--- a/methods_html/SeqWell_S3.html
+++ b/methods_html/SeqWell_S3.html
@@ -9,7 +9,7 @@
 
 
Seq-Well S3

 
-
Seq-Well S3 is an improved method over the original Seq-Well protocol. The "S3" in the name means "Second-Strand Synthesis". In the original Seq-Well workflow, the Template Switching Oligo was used for full-length cDNA synthesis and ampification, which may result in the loss of cDNAs that are partially generated (not reaching the 5' end of the gene). The new Seq-Well S3 method used random priming and primer extension for the second strand synthesis, which seems to improve the sensitivity a lot.

+
Seq-Well S3 is an improved method over the original Seq-Well protocol. The "S3" in the name means "Second-Strand Synthesis". In the original Seq-Well workflow, the Template Switching Oligo was used for full-length cDNA synthesis and amplification, which may result in the loss of cDNAs that are partially generated (not reaching the 5' end of the gene). The new Seq-Well S3 method used random priming and primer extension for the second strand synthesis, which seems to improve the sensitivity a lot.

 
 

 
@@ -18,7 +18,7 @@ 
Adapter and primer sequences:

 
Beads-oligo-dT-seqB: |--5'- TTTTTTTAAGCAGTGGTATCAACGCAGAGTAC[12-bp cell barcode][8-bp UMI]TTTTTTTTTTTTTTTTTTTTTTTTTTTTTT -3'

 
Template Switching Oligo (TSO): 5'- AAGCAGTGGTATCAACGCAGAGTGAATrGrGrG -3'

 
dN-Smart Randomer (dN-SMRT): 5'- AAGCAGTGGTATCAACGCAGAGTGANNNGGNNNB -3'

-
Smart PCR Primer (TSO_PCR): 5′- AAGCAGTGGTATCAACGCAGAGT -3′

+
Smart PCR Primer (TSO_PCR): 5'- AAGCAGTGGTATCAACGCAGAGT -3'

 
New-P5-SMART PCR Hybrid Oligo (P5-TSO_Hybrid):: 5'- AATGATACGGCGACCACCGAGATCTACACGCCTGTCCGCGGAAGCAGTGGTATCAACGCAGAGTAC -3'

 
Illumina Nextera N7xx primer: 5'- CAAGCAGAAGACGGCATACGAGAT[8-bp i7 index]GTCTCGTGGGCTCGG -3'

 
Custom Read 1 Primer (Read_1_Custom_SeqB): 5'- GCCTGTCCGCGGAAGCAGTGGTATCAACGCAGAGTAC -3'

diff --git a/methods_html/VASA-seq.html b/methods_html/VASA-seq.html
index e148073..48f5329 100644
--- a/methods_html/VASA-seq.html
+++ b/methods_html/VASA-seq.html
@@ -11,7 +11,7 @@
 
VASA-plate
   / VASA-drop

 
-
VASA-seq was published in Nat Biotechnol. 2022. It is a brilliant method that can profile full-length + total RNA from single cells by adding poly-A to fragmented RNA molecules. Then the rest of the procedures are the same as a regular scRNA-seq method. The method can be done in both plate (VASA-plate) and droplet (VASA-drop) formats. The plate-based VASA-seq used the CEL-seq2 strategy for the library preparation, while the droplet-based VASA-seq used the inDrop platform combined with a picoinjection technique to precisely deliver reagents into droplets.

+
VASA-seq was published in Nat Biotechnol. 2022. It is a brilliant method that can profile full-length + total RNA from single cells by adding poly-A to fragmented RNA molecules. Then the rest of the procedures are the same as a regular scRNA-seq method. The method can be done in both plate (VASA-plate) and droplet (VASA-drop) formats. The plate-based VASA-seq used the CEL-seq2 strategy for the library preparation, while the droplet-based VASA-seq used the inDrop platform combined with a picoinjection technique to precisely deliver reagents into droplets.

 
 
NOTE: Thank Jo (@Joachim De Jonghe) for providing the sequences of the VASA-drop library.

 
diff --git a/methods_html/dscATAC.html b/methods_html/dscATAC.html
index 1e2462d..ba0e27b 100644
--- a/methods_html/dscATAC.html
+++ b/methods_html/dscATAC.html
@@ -9,15 +9,15 @@
 
 
dscATAC-seq/dsciATAC-seq (SureCell ATAC-seq)

 
-
dscATAC-seq and dsciATAC-seq are droplet-based scATAC-seq methods. They were published in Nature Biotechnology on 2019 Jun 24 (Lareau et al. Nat Biotechnol. 37, 916-924.), and the kit for this method is SureCell ATAC-seq Library Prep Kit.

+
dscATAC-seq and dsciATAC-seq are droplet-based scATAC-seq methods. They were published in Nature Biotechnology on 2019 Jun 24 (Lareau et al. Nat Biotechnol. 37, 916-924.), and the kit for this method is SureCell ATAC-seq Library Prep Kit.

 
-
For beginners who are new to droplet technology, it might not be clear what improvement dscATAC-seq has made. I will briefly explain here. In the droplet technologies, you have beads with barcodes going into droplets, and this follows a Poisson distribution with a certain mean. You also have cells going into droplets, and this also follows a Poisson distribution with a different mean. These two loading processes are independent, so you will have droplets with no beads no cells, with beads but no cells, with cells but no beads. Only a small proportion of the droplets contain exactly one bead and one cell. That's why in many droplet technologies, you load 100k cells, but eventually only get back 1-2k cells after sequencing.

+
For beginners who are new to droplet technology, it might not be clear what improvement dscATAC-seq has made. I will briefly explain here. In the droplet technologies, you have beads with barcodes going into droplets, and this follows a Poisson distribution with a certain mean. You also have cells going into droplets, and this also follows a Poisson distribution with a different mean. These two loading processes are independent, so you will have droplets with no beads no cells, with beads but no cells, with cells but no beads. Only a small proportion of the droplets contain exactly one bead and one cell. That's why in many droplet technologies, you load 100k cells, but eventually only get back 1-2k cells after sequencing.

 
-
In 2009, Abates et al. showed that if you increase the size of the beads to a point that they are close-packed, you can insert a controllable number of beads into every droplet. inDrop and 10x Genomics used this type of "big" close-packed hydrogel beads to solve the bead loading problem, though the hydrogel beads used by inDrop and 10x Genomics are different. However, for small beads, such as those used in Drop-seq, Bio-Rad SureCell and many other home-made droplet device, this cannot be achieved.

+
In 2009, Abates et al. showed that if you increase the size of the beads to a point that they are close-packed, you can insert a controllable number of beads into every droplet. inDrop and 10x Genomics used this type of "big" close-packed hydrogel beads to solve the bead loading problem, though the hydrogel beads used by inDrop and 10x Genomics are different. However, for small beads, such as those used in Drop-seq, Bio-Rad SureCell and many other home-made droplet device, this cannot be achieved.

 
-
What Lareau et al. did was that they load many more (superload) beads than usual to significantly reduce the number of drops without beads. However, this also increase the number of drops with more than one bead. Here is the clever bit, if more than one bead end up in the same drop, they will share the same set of genomic fragments to start with the amplification from that drop. Therefore, those beads from the same drops should have much higher overlap of Tn5 insertion positions among each other than beads from different drops (see Supplementary Fig1 and 2 in the paper). Therefore, one can figure out whether beads are from the same drops by checking the extent of overlap of reads from different bead barcodes. If the cells are loaded at a normal concentration, one can make sure most drops have at most 1 cell as usual.

+
What Lareau et al. did was that they load many more (superload) beads than usual to significantly reduce the number of drops without beads. However, this also increase the number of drops with more than one bead. Here is the clever bit, if more than one bead end up in the same drop, they will share the same set of genomic fragments to start with the amplification from that drop. Therefore, those beads from the same drops should have much higher overlap of Tn5 insertion positions among each other than beads from different drops (see Supplementary Fig1 and 2 in the paper). Therefore, one can figure out whether beads are from the same drops by checking the extent of overlap of reads from different bead barcodes. If the cells are loaded at a normal concentration, one can make sure most drops have at most 1 cell as usual.

 
-
dsciATAC-seq takes a step further. It builds on top of dscATAC-seq, but take the idea of combinatorial indexing strategy, like that used in sci-ATAC-seq, and treat cells in different reactions with barcoded Tn5 and pooled all the reactions together before loading onto the machine to make emulsion. In this case, both beads and cells are overloaded. One can use the aformentioned strategy to figure out whether beads are from the same drop, and on top of that, use the Tn5 barcodes to figure out whether the reads are from the same single cells. In this way, the throughput becomes really high.

+
dsciATAC-seq takes a step further. It builds on top of dscATAC-seq, but take the idea of combinatorial indexing strategy, like that used in sci-ATAC-seq, and treat cells in different reactions with barcoded Tn5 and pooled all the reactions together before loading onto the machine to make emulsion. In this case, both beads and cells are overloaded. One can use the aforementioned strategy to figure out whether beads are from the same drop, and on top of that, use the Tn5 barcodes to figure out whether the reads are from the same single cells. In this way, the throughput becomes really high.

 
 

 
@@ -29,7 +29,7 @@ 
Adapter and primer sequences:

 
 
 
ATAC-v2.1 beads-p5-bc-nextera-read1: |--5'- TTTTTTTUUUTTTTTAATGATACGGCGACCACCGAGATCTACACGCCTGTCCGCGGAAGCAGTGGTATCAACGCAGAGTAC[7-bp barcode1][0-4bp Phase Block]TATGCATGAC[7-bp barcode2]AGTCACTGAG[7-bp barcode3]TCGTCGGCAGCGTC -3'

-
* The bead barcode in this protocol is based on the combination of the barcode1+barcode2+barcode3, 7-bp each, and 21-bp in total. The full oligos are generated in a split-pool manner. The Phase Block is there to make sure each sequencing cycle has decent base complexity. The full sequences (including different version history) can be found from this excel file from the bap GitHub repository.

+
* The bead barcode in this protocol is based on the combination of the barcode1+barcode2+barcode3, 7-bp each, and 21-bp in total. The full oligos are generated in a split-pool manner. The Phase Block is there to make sure each sequencing cycle has decent base complexity. The full sequences (including different version history) can be found from this excel file from the bap GitHub repository.

 
Nextera Tn5 binding site (19-bp Mosaic End (ME)): 5'- AGATGTGTATAAGAGACAG -3'

 
Nextera S5xx primer entry point (s5): 5'- TCGTCGGCAGCGTC -3'

 
Nextera N7xx primer entry point (s7): 5'- GTCTCGTGGGCTCGG -3'

@@ -45,7 +45,7 @@ 
Adapter and primer sequences:

 
 
Step-by-step library generation (only dscATAC-seq is shown here; dsciATAC-seq is the same, but with barcoded Tn5):

 
(1) Bulk Tn5 tagging by incubation of nuclei and Tn5:

-Tn5 dimer
+Tn5 dimer
 
 
(2) There are 3 different products after step (1) (will create 9 bp gap):

 
diff --git a/methods_html/inDrop.html b/methods_html/inDrop.html
index 22b922d..e0827d9 100644
--- a/methods_html/inDrop.html
+++ b/methods_html/inDrop.html
@@ -10,7 +10,7 @@
 
 
inDrop V1 / inDrop V2&3

 
-
inDrop was originally published in Cell 161, 1187-1201 (2015), which is the V1 version. Then, two years after that, the authors published the detailed protocol in Nature Protocols 12, 44-73 (2017), which seems to be the V2 version of the technology and has different primer design comparing to the original paper. According to the inDrop github repository, there is a version 3, but the oligos and library structures are exactly the same as version 2, except the sequencing mode has changed. In version 2, three different reads are generated; in version 3, four different reads are generated. See details below.

+
inDrop was originally published in Cell 161, 1187-1201 (2015), which is the V1 version. Then, two years after that, the authors published the detailed protocol in Nature Protocols 12, 44-73 (2017), which seems to be the V2 version of the technology and has different primer design comparing to the original paper. According to the inDrop github repository, there is a version 3, but the oligos and library structures are exactly the same as version 2, except the sequencing mode has changed. In version 2, three different reads are generated; in version 3, four different reads are generated. See details below.

 
 

 
@@ -39,8 +39,8 @@ 
Sequence used for barcoded oligo-dT19V generation (before the experiment):FAM-PE1 probe: 5'-/6-FAM/ AGATCGGAAGAGCGTCGTGTAGGGAAAGAG -3'

 
FAM-W1 probe: 5'-/6-FAM/ AAGGCGTCACAAGCAATCACTC -3'

 
FAM-BA19 probe: 5'-/6-FAM/ BAAAAAAAAAAAAAAAAAAA -3'

-
Barcode 1 plates (384 of them in 384 individual wells): 5'- AAGGCGTCACAAGCAATCACTC[barcode1]AGATCGGAAGAGCGTCGTGTAGGGAAAGAG -3', for the sequences of 384 different barcode1, see inDrop_barcode1_list.txt.

-
Barcode 2 plates (384 of them in 384 individual wells): 5'- BAAAAAAAAAAAAAAAAAAA[6-bp UMI][8-bp barcode2]AAGGCGTCACAAGCAATCACTC -3', for the sequences of 384 different barcode2 (8 bp), see inDrop_barcode2_list.txt.

+
Barcode 1 plates (384 of them in 384 individual wells): 5'- AAGGCGTCACAAGCAATCACTC[barcode1]AGATCGGAAGAGCGTCGTGTAGGGAAAGAG -3', for the sequences of 384 different barcode1, see inDrop_barcode1_list.txt.

+
Barcode 2 plates (384 of them in 384 individual wells): 5'- BAAAAAAAAAAAAAAAAAAA[6-bp UMI][8-bp barcode2]AAGGCGTCACAAGCAATCACTC -3', for the sequences of 384 different barcode2 (8 bp), see inDrop_barcode2_list.txt.

 
 
 

@@ -218,8 +218,8 @@ 
Sequence used for barcoded oligo-dT19V generation (before the experiment):FAM-PE1 probe: 5'-/6-FAM/ AGATCGGAAGAGCGTCGTGTAGGGAAAGAG -3'

 
FAM-W1 probe: 5'-/6-FAM/ AAGGCGTCACAAGCAATCACTC -3'

 
FAM-BA19 probe: 5'-/6-FAM/ BAAAAAAAAAAAAAAAAAAA -3'

-
Barcode 1 plates (384 of them in 384 individual wells): 5'- AAGGCGTCACAAGCAATCACTC[barcode1]AGATCGGAAGAGCGTCGTGTAGGGAAAGAG -3', for the sequences of 384 different barcode1, see inDrop_barcode1_list.txt.

-
Barcode 2 plates (384 of them in 384 individual wells): 5'- BAAAAAAAAAAAAAAAAAAA[6-bp UMI][8-bp barcode2]AAGGCGTCACAAGCAATCACTC -3', for the sequences of 384 different barcode2 (8 bp), see inDrop_barcode2_list.txt.

+
Barcode 1 plates (384 of them in 384 individual wells): 5'- AAGGCGTCACAAGCAATCACTC[barcode1]AGATCGGAAGAGCGTCGTGTAGGGAAAGAG -3', for the sequences of 384 different barcode1, see inDrop_barcode1_list.txt.

+
Barcode 2 plates (384 of them in 384 individual wells): 5'- BAAAAAAAAAAAAAAAAAAA[6-bp UMI][8-bp barcode2]AAGGCGTCACAAGCAATCACTC -3', for the sequences of 384 different barcode2 (8 bp), see inDrop_barcode2_list.txt.

 
 
 

diff --git a/methods_html/itChIP-seq.html b/methods_html/itChIP-seq.html
index fbd9feb..22c711f 100644
--- a/methods_html/itChIP-seq.html
+++ b/methods_html/itChIP-seq.html
@@ -9,9 +9,9 @@
 
 
itChIP-seq

 
-
The indexing and tagmentation-based ChIP-seq (itChIP-seq) method uses similar strategy as Drop-ChIP. Instead of using MNase and barcoding in droplets, itChIP-seq uses barcoded Tn5 to tag sorted single nuclei. After pooling all indexed nuclei, an immunoprecipitation is performed using an antibody against a protein of interest.

+
The indexing and tagmentation-based ChIP-seq (itChIP-seq) method uses similar strategy as Drop-ChIP. Instead of using MNase and barcoding in droplets, itChIP-seq uses barcoded Tn5 to tag sorted single nuclei. After pooling all indexed nuclei, an immunoprecipitation is performed using an antibody against a protein of interest.

 
-
The oligos used to generate barcoded Tn5 are the same as those used in sci-ATAC-seq. You can follow the step-by-step library construction in the sci-ATAC-seq webpage for details. However, if you construct the libary in that way, you have to do a customised sequencing run with customised sequencing primers and use an entire flow cell. This can be expensive if you don't have small machines like Miseq or NextSeq. In the itChIP-seq paper, the authors manupilated the oligos and make it compatible with Illumina standard workflow, which makes the sequeuncing more flexible. In this page, this new design is shown below. The detailed experimental procedures can be found from Protocol Exchange.

+
The oligos used to generate barcoded Tn5 are the same as those used in sci-ATAC-seq. You can follow the step-by-step library construction in the sci-ATAC-seq webpage for details. However, if you construct the libary in that way, you have to do a customised sequencing run with customised sequencing primers and use an entire flow cell. This can be expensive if you don't have small machines like Miseq or NextSeq. In the itChIP-seq paper, the authors manupilated the oligos and make it compatible with Illumina standard workflow, which makes the sequeuncing more flexible. In this page, this new design is shown below. The detailed experimental procedures can be found from Protocol Exchange.

 
 

 
@@ -47,10 +47,10 @@ 
Adapter and primer sequences:

 

 
 
Step-by-step library generation

-
(1) Anneal Barcoded Tn5 sequences s5/s7 and Tn5 binding site 19-bp Mosaic End (ME) bottom strand to assemble the barcoded Tn5 transposome, sort single nulcei into well plates, perform tagmentation to barcode each nulcei:

+
(1) Anneal Barcoded Tn5 sequences s5/s7 and Tn5 binding site 19-bp Mosaic End (ME) bottom strand to assemble the barcoded Tn5 transposome, sort single nuclei into well plates, perform tagmentation to barcode each nuclei:

 Tn5 dimer
 
-
(2) Sort limited nulcei into wells, and perform tagmentation using barcoded Tn5 transposome:

+
(2) Sort limited nuclei into wells, and perform tagmentation using barcoded Tn5 transposome:

 
 
 Product 1 (s5 at both ends, not amplifiable due to semi-suppressive PCR:
@@ -59,7 +59,7 @@ 
(2) Sort limited nulcei into wells, and perform tagmentation using barcoded
                                                         TCTACACATATTCTCTGTC         XXX...XXXXXXXXXXXXGACAGAGAATATGTGTAGACGGCAGGAGCTAGCG[8-bp Tn5 index]CGCACCTCTGCGACGGCTGCT -5'
 
 
-Product 2 (s7 at both ends, not amplifiable due to semi-suppressiev PCR):
+Product 2 (s7 at both ends, not amplifiable due to semi-suppressive PCR):
 
 5'- GTCTCGTGGGCTCGGCTGTCCCTGTCC[8-bp Tn5 index]CACCGTCTCCGCCTCAGATGTGTATAAGAGACAGXXXXXXXXXXXX...XXX         CTGTCTCTTATACACATCT
                                                               TCTACACATATTCTCTGTC         XXX...XXXXXXXXXXXXGACAGAGAATATGTGTAGACTCCGCCTCTGCCAC[8-bp Tn5 index]CCTGTCCCTGTCGGCTCGGGTGCTCTG -5'
@@ -72,7 +72,7 @@ 
(2) Sort limited nulcei into wells, and perform tagmentation using barcoded
 
 

 
-
(3) Pool all wells of barcoded nulcei, perform immunoprecipitations, reverse crosslinking, DNA purfication, and the use Connector Primers F and R to perform amplification:

+
(3) Pool all wells of barcoded nuclei, perform immunoprecipitations, reverse crosslinking, DNA purification, and the use Connector Primers F and R to perform amplification:

 
 
 5'- ACACTCTTTCCCTACACGACGCTCTTCCGATCTTCGTCGGCAGCGTCTCCACGC------>
diff --git a/methods_html/plate_and_piATAC-seq.html b/methods_html/plate_and_piATAC-seq.html
index 698c7fb..6b37af2 100644
--- a/methods_html/plate_and_piATAC-seq.html
+++ b/methods_html/plate_and_piATAC-seq.html
@@ -10,7 +10,7 @@
 
plate_scATAC-seq
 / Pi-ATAC-seq

 
-
It has been previously demonstrated that Tn5 transposase-mediated tagmentation contains two stages: (1) a tagging stage where the Tn5 transposome binds to DNA, and (2) a fragmentation stage where the Tn5 transposase is released from DNA using heat or denaturing agents, such as sodium dodecyl sulfate (SDS). As the Tn5 tagging does not fragment DNA, the nuclei would remain intact after incubation with the Tn5 transposome in an bulk ATAC-seq experiment. Both plate_scATAC-seq and Pi-ATAC-seq utilise this property of Tn5 tagmentation, and perform bulk tagging upfront, followed by FACS sorting of single nuclei into wells for library construction. FACS index sorting can also be reported. These methods are simple to follow and work robustly. Since each cells are processed separately, the library indices are single cell barcodes.

+
It has been previously demonstrated that Tn5 transposase-mediated tagmentation contains two stages: (1) a tagging stage where the Tn5 transposome binds to DNA, and (2) a fragmentation stage where the Tn5 transposase is released from DNA using heat or denaturing agents, such as sodium dodecyl sulfate (SDS). As the Tn5 tagging does not fragment DNA, the nuclei would remain intact after incubation with the Tn5 transposome in an bulk ATAC-seq experiment. Both plate_scATAC-seq and Pi-ATAC-seq utilise this property of Tn5 tagmentation, and perform bulk tagging upfront, followed by FACS sorting of single nuclei into wells for library construction. FACS index sorting can also be reported. These methods are simple to follow and work robustly. Since each cells are processed separately, the library indices are single cell barcodes.

 
 

 
diff --git a/methods_html/s3-ATAC.html b/methods_html/s3-ATAC.html
index 124c517..a48cd0d 100644
--- a/methods_html/s3-ATAC.html
+++ b/methods_html/s3-ATAC.html
@@ -9,15 +9,15 @@
 
 
s3-ATAC

 
-
Due to semi-suppressive PCR (click here to see a detailed explanation), only 50% of the tagmented material from the regular Tn5 are amplifiable. The s3-ATAC approach introduced uracil bases in the Tn5 adapters and used uracil intolerant and tolerant DNA polymerases for the gap fill-in and PCR amplification, respectively. In this way, every Tn5 cutting event is amplifiable. Combined with the combinatorial indexing strategy, it is a very clever technique that significantly increases the library complexities of many single cell libraries with a relatively easy workflow.

+
Due to semi-suppressive PCR (click here to see a detailed explanation), only 50% of the tagmented material from the regular Tn5 are amplifiable. The s3-ATAC approach introduced uracil bases in the Tn5 adapters and used uracil intolerant and tolerant DNA polymerases for the gap fill-in and PCR amplification, respectively. In this way, every Tn5 cutting event is amplifiable. Combined with the combinatorial indexing strategy, it is a very clever technique that significantly increases the library complexities of many single cell libraries with a relatively easy workflow.

 
 

 
 
Adapter and primer sequences:

 
-
* Detailed oligo sequences and names can be found in their Supplementary Tables 1-3 from the paper.

+
* Detailed oligo sequences and names can be found in their Supplementary Tables 1-3 from the paper.

 
Truseq_R2_SBS12_partial: 5'- CGTGTGCTCTTCCGATCT[8-bp Tn5 index]/ideoxyU/AGATGTGTATAAGAGACAG -3'

-
A14_ME_LNA (Nexetera_R1_A14 + U-ME): 5'- TCGTCGGCAGCGTCAGATGTGTA+TA+AG+AG+AC+AG/3InvdT/ -3'

+
A14_ME_LNA (Nextera_R1_A14 + U-ME): 5'- TCGTCGGCAGCGTCAGATGTGTA+TA+AG+AG+AC+AG/3InvdT/ -3'

 
Tn5 binding site 19-bp Mosaic End (ME) bottom: 5'- /Phos/CTGTCTCTTATACACATCT -3'

 
PCR_i5_primer: 5'- AATGATACGGCGACCACCGAGATCTACAC[i5]TCGTCGGCAGCGTC -3'

 
PCR_i7_primer: 5'- CAAGCAGAAGACGGCATACGAGAT[i7]GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT -3'

@@ -32,7 +32,7 @@ 
Adapter and primer sequences:

 
 
Step-by-step library generation

 
(1) Anneal Barcoded Truseq_R2_SBS12_partial and Tn5 binding site 19-bp Mosaic End (ME) bottom strand to assemble Tn5 transposome (Tn5 homodimer):

-Tn5 dimer
+Tn5 dimer
 
 
(2) Sort certain number of nuclei into wells, and perform tagmentation using barcoded Tn5 transposome:

 
diff --git a/methods_html/s3-WGS.html b/methods_html/s3-WGS.html
index fc5a03f..ea70388 100644
--- a/methods_html/s3-WGS.html
+++ b/methods_html/s3-WGS.html
@@ -9,9 +9,9 @@
 
 
s3-WGS

 
-
Due to semi-suppressive PCR (click here to see a detailed explanation), only 50% of the tagmented material from the regular Tn5 are amplifiable. The s3-WGS approach introduced uracil bases in the Tn5 adapters and used uracil intolerant and tolerant DNA polymerases for the gap fill-in and PCR amplification, respectively. In this way, every Tn5 cutting event is amplifiable. The procedure is very similar to s3-ATAC, except that s3-WGS is perform on crosslinked and nucleosome-depleted nuclei.

+
Due to semi-suppressive PCR (click here to see a detailed explanation), only 50% of the tagmented material from the regular Tn5 are amplifiable. The s3-WGS approach introduced uracil bases in the Tn5 adapters and used uracil intolerant and tolerant DNA polymerases for the gap fill-in and PCR amplification, respectively. In this way, every Tn5 cutting event is amplifiable. The procedure is very similar to s3-ATAC, except that s3-WGS is perform on crosslinked and nucleosome-depleted nuclei.

 
-
The library structure of s3-WGS is the same as s3-ATAC. Click here to see the detailed library generation steps.

+
The library structure of s3-WGS is the same as s3-ATAC. Click here to see the detailed library generation steps.

 
 
 
diff --git a/methods_html/scBS-seq.html b/methods_html/scBS-seq.html
index 668186f..fe74a94 100644
--- a/methods_html/scBS-seq.html
+++ b/methods_html/scBS-seq.html
@@ -9,9 +9,9 @@
 
 
scBS-seq

 
-
scBS-seq is a technique to profile DNA methylation at the single cell level. It is based on the principle of bisulfite conversion. The trick here is to add sequencing adapters after the bisulfite conversion, because bisulfite treatment is damaing to DNA, which often causes DNA fragmentation. The adapter addition is achieved by random priming, whic is similar to MALBAC, wel ... kind of ... .

+
scBS-seq is a technique to profile DNA methylation at the single cell level. It is based on the principle of bisulfite conversion. The trick here is to add sequencing adapters after the bisulfite conversion, because bisulfite treatment is damaing to DNA, which often causes DNA fragmentation. The adapter addition is achieved by random priming, whic is similar to MALBAC, wel ... kind of ... .

 
-
In the original publication in Nature Methods 11, 817–820. The authors used the iPCRtag primer for indexing. According to the Illumina Adapter sequence, the sequences used here is obsoltete. Check page 47 in the linked PDF. I still draw it here for historical reasons, but you can change that to modern Truseq structures, which can be easily found in other pages in this repository.

+
In the original publication in Nature Methods 11, 817-820. The authors used the iPCRtag primer for indexing. According to the Illumina Adapter sequence, the sequences used here is obsoltete. Check page 47 in the linked PDF. I still draw it here for historical reasons, but you can change that to modern Truseq structures, which can be easily found in other pages in this repository.

 
 

 
diff --git a/methods_html/scCAT-seq.html b/methods_html/scCAT-seq.html
index 01611d3..b0a6ec7 100644
--- a/methods_html/scCAT-seq.html
+++ b/methods_html/scCAT-seq.html
@@ -9,7 +9,7 @@
 
 
scCAT-seq

 
-
The single cell chromatin accessibility and transcriptome sequencing (scCAT-seq) physically separates mRNA and chromatin from single cells. The method uses gentle lysis to release mRNA into the supernatant, then transfer supernatant to new tubes and leave nuclei at the original tube. After that, mRNA and nulcei are processed separately. For mRNA-seq, it uses the SMART-seq method to construct sequencing library; for chromatin accessibility, it uses standard ATAC-seq workflow to construct sequencing library. Click the links to those methods in the main page to see how exactly libraries are made.

+
The single cell chromatin accessibility and transcriptome sequencing (scCAT-seq) physically separates mRNA and chromatin from single cells. The method uses gentle lysis to release mRNA into the supernatant, then transfer supernatant to new tubes and leave nuclei at the original tube. After that, mRNA and nuclei are processed separately. For mRNA-seq, it uses the SMART-seq method to construct sequencing library; for chromatin accessibility, it uses standard ATAC-seq workflow to construct sequencing library. Click the links to those methods in the main page to see how exactly libraries are made.

 
 
 
diff --git a/methods_html/scDNase_scMNase.html b/methods_html/scDNase_scMNase.html
index 6749f88..4df74bb 100644
--- a/methods_html/scDNase_scMNase.html
+++ b/methods_html/scDNase_scMNase.html
@@ -10,7 +10,7 @@
 
scDNase-seq
 / scMNase-seq

 
-
Both scDNase-seq and scMNase-seq are developed from Keji Zhao's lab. They use almost the same procudures to construct the library. One uses DNase to get DNA from open chromatin and the other uses MNase to get DNA from both open chromatin and nulcesomal regions. The methods utilise the traditional way of making libraries, which is ligating the sequencing adaptors to the DNase/MNase fragmented DNA. The trick is to add circular plasmid as carrier DNA during ligation and purification to reduce DNA loss. Therefore, the library structure is just standard Illumina sequencing libraries. Each cells are prepared separately, so the library index is the cell barcode.

+
Both scDNase-seq and scMNase-seq are developed from Keji Zhao's lab. They use almost the same procedures to construct the library. One uses DNase to get DNA from open chromatin and the other uses MNase to get DNA from both open chromatin and nucleosomal regions. The methods utilise the traditional way of making libraries, which is ligating the sequencing adaptors to the DNase/MNase fragmented DNA. The trick is to add circular plasmid as carrier DNA during ligation and purification to reduce DNA loss. Therefore, the library structure is just standard Illumina sequencing libraries. Each cells are prepared separately, so the library index is the cell barcode.

 
 

 
diff --git a/methods_html/scDamID.html b/methods_html/scDamID.html
index 422d1ed..6c98a73 100644
--- a/methods_html/scDamID.html
+++ b/methods_html/scDamID.html
@@ -9,7 +9,7 @@
 
 
scDamID

 
-
The scDamID method was published by Kind et al. in Cell 163, 134-147. It was based on the previous method called DamID invented by Bas van Steensel and Steven Henikoff in Nature Biotechnology 18, 424–428. DamID is based on the idea that the sequence GATC occurs frequently enough in the genome, and the methylation of adenine (mA) is really really rare in higher eukaryotic cells. Well, I'm not going to comment on whether mA really exists naturally in the mammalian genomes, because I'm not an expert on this. Anyway, GmATC is really rare. The idea of DamID is very clever and simple: by tethering E. coli DNA adenine methyltransferase (Dam) to a DNA/chromatin interacting protein, Dam can be targeted in vivo to native binding sites of that protein, resulting in local DNA methylation GmATC. By using the restriction enzyme Dpn I that specifically digest GmATC, one can find out where the protein of interest is bound. scDamID optimises each reaction step and keeps each reaction in one single tube before pre-amplification. Then it just uses standard Illumina library preparation procedures (end repair - A tailing - ligation of adapter - PCR). This enables DamID at the single cell level. It is mostly used to study lamina-DNA contacts.

+
The scDamID method was published by Kind et al. in Cell 163, 134-147. It was based on the previous method called DamID invented by Bas van Steensel and Steven Henikoff in Nature Biotechnology 18, 424–428. DamID is based on the idea that the sequence GATC occurs frequently enough in the genome, and the methylation of adenine (mA) is really really rare in higher eukaryotic cells. Well, I'm not going to comment on whether mA really exists naturally in the mammalian genomes, because I'm not an expert on this. Anyway, GmATC is really rare. The idea of DamID is very clever and simple: by tethering E. coli DNA adenine methyltransferase (Dam) to a DNA/chromatin interacting protein, Dam can be targeted in vivo to native binding sites of that protein, resulting in local DNA methylation GmATC. By using the restriction enzyme Dpn I that specifically digest GmATC, one can find out where the protein of interest is bound. scDamID optimises each reaction step and keeps each reaction in one single tube before pre-amplification. Then it just uses standard Illumina library preparation procedures (end repair - A tailing - ligation of adapter - PCR). This enables DamID at the single cell level. It is mostly used to study lamina-DNA contacts.

 
 

 
diff --git a/methods_html/scDamT-seq.html b/methods_html/scDamT-seq.html
index 68aa8cc..2687700 100644
--- a/methods_html/scDamT-seq.html
+++ b/methods_html/scDamT-seq.html
@@ -9,9 +9,9 @@
 
 
scDam&T-seq

 
-
The scDam&T-seq method was originally published in Nature Biotechnology 37, 766–772. Later on, a detailed protocol was published in Nature Protocols 15, 1922–1953. The procedures in this web page is based on the Nature Protocol version.

+
The scDam&T-seq method was originally published in Nature Biotechnology 37, 766-772. Later on, a detailed protocol was published in Nature Protocols 15, 1922-1953. The procedures in this web page is based on the Nature Protocol version.

 
-
The scDam&T-seq method is basically a clever combination of scDamID and CEL-seq to simualtaneously determine the transcriptional state and protein-DNA interaction in the same single cells.

+
The scDam&T-seq method is basically a clever combination of scDamID and CEL-seq to simualtaneously determine the transcriptional state and protein-DNA interaction in the same single cells.

 
 

 
@@ -117,7 +117,7 @@ 
(4) Ligate the DamID adaptor to the digested DNA (methyl marks will be remov
 
 

 
-
(5) Pool all wells and perform in vitro transcripiton to amplify both mRNA and gDNA:

+
(5) Pool all wells and perform in vitro transcription to amplify both mRNA and gDNA:

 
 
 mRNA:
diff --git a/methods_html/scMT-seq.html b/methods_html/scMT-seq.html
index 87e2a27..a6e371c 100644
--- a/methods_html/scMT-seq.html
+++ b/methods_html/scMT-seq.html
@@ -9,7 +9,7 @@
 
 
scMT-seq

 
-
The scMT-seq method was published by Hu et al. in Genome Biology 17, 88. This should not to be confused with scM&T-seq. The scMT-seq physically separates nuclei and the cytopalsm. The mRNA is in cytoplasm and SMART-seq2 method was used for scRNA-seq, click here to see the step-by-step protocol; the genomic DNA is in the nuclei, scRRBS method was used to profile methylation, and click here to see the step-by-step protocol.

+
The scMT-seq method was published by Hu et al. in Genome Biology 17, 88. This should not to be confused with scM&T-seq. The scMT-seq physically separates nuclei and the cytoplasm. The mRNA is in cytoplasm and SMART-seq2 method was used for scRNA-seq, click here to see the step-by-step protocol; the genomic DNA is in the nuclei, scRRBS method was used to profile methylation, and click here to see the step-by-step protocol.

 
 
 
diff --git a/methods_html/scMandT.html b/methods_html/scMandT.html
index 4d108f4..99ae708 100644
--- a/methods_html/scMandT.html
+++ b/methods_html/scMandT.html
@@ -9,7 +9,7 @@
 
 
scM&T-seq

 
-
The scM&T-seq method was published by Angermueller et al. in Nature Methods 13, 229–232. It uses oligo-dT magnetic beads to drag mRNA out to achieve phycical separation of mRNA and genomic DNA. For the mRNA part, SMART-seq2 method was used for scRNA-seq, click here to see the step-by-step protocol; for the genomic DNA part, scBS-seq method was used to profile methylation, and click here to see the step-by-step protocol.

+
The scM&T-seq method was published by Angermueller et al. in Nature Methods 13, 229-232. It uses oligo-dT magnetic beads to drag mRNA out to achieve phycical separation of mRNA and genomic DNA. For the mRNA part, SMART-seq2 method was used for scRNA-seq, click here to see the step-by-step protocol; for the genomic DNA part, scBS-seq method was used to profile methylation, and click here to see the step-by-step protocol.

 
 
 
diff --git a/methods_html/scNMT-seq.html b/methods_html/scNMT-seq.html
index 4f3af26..0aa0835 100644
--- a/methods_html/scNMT-seq.html
+++ b/methods_html/scNMT-seq.html
@@ -9,7 +9,7 @@
 
 
scNMT-seq

 
-
The scNMT-seq uses the similar principle as scNOMe-seq, where a purified GpC methylase (M.CviPI) was applied on permeabilised cells. The majority of the DNA is protected by nucleosomes and inaccessible to M.CviPI. Only GpC within accessible regions can be methylated by M.CviPI. Then, mRNA is dragged out by oligo-dT magnetic beads, and library preparation was done using the SMART-seq2 method, click here to see a step-by-step protocol of SMART-seq2. After bisulfite conversion of the gDNA, the scBS-seq method was used to prepare the library to look at methylation (CpG) and chromatin accessibility (GpC), click here to see a step-by-step protocol of scBS-seq.

+
The scNMT-seq uses the similar principle as scNOMe-seq, where a purified GpC methylase (M.CviPI) was applied on permeabilised cells. The majority of the DNA is protected by nucleosomes and inaccessible to M.CviPI. Only GpC within accessible regions can be methylated by M.CviPI. Then, mRNA is dragged out by oligo-dT magnetic beads, and library preparation was done using the SMART-seq2 method, click here to see a step-by-step protocol of SMART-seq2. After bisulfite conversion of the gDNA, the scBS-seq method was used to prepare the library to look at methylation (CpG) and chromatin accessibility (GpC), click here to see a step-by-step protocol of scBS-seq.

 
 
 
diff --git a/methods_html/scNOMe_scCOOL.html b/methods_html/scNOMe_scCOOL.html
index 798f20f..aaf014d 100644
--- a/methods_html/scNOMe_scCOOL.html
+++ b/methods_html/scNOMe_scCOOL.html
@@ -9,7 +9,7 @@
 
 
scNOMe-seq / scCOOL-seq

 
-
Both scNOMe-seq and scCOOL-seq use similar principles and strategies. In the mammalian genmoe, CpG methylation (mCG) is common, but GpC methylation (GmC) is absent. The methods apply a purified GpC methylase (M.CviPI) on nuclei. The majority of the DNA is protected by nucleosomes and inaccessible to M.CviPI. Only GpC within accessible regions can be methylated by M.CviPI. After bisulfite conversion and sequencing, one can identify DNA methylation by CpG methylation, and chromatin accessbility by GpC methylation. These two methods differ at the library preparation steps after bisulfite conversion. scNOMe-seq uses a comercial kit (Pico Methyl-seq Library prep Kit) to make libraries, while scCOOL-seq simply uses scBS-seq method to make libraries. Click here to see a step-by-step protocol of scBS-seq. The final library structures of scNOMe-seq and scCOOL-seq are the same. scCOOL-seq uses some extra analysis to investigate CNV and ploidy.

+
Both scNOMe-seq and scCOOL-seq use similar principles and strategies. In the mammalian genome, CpG methylation (mCG) is common, but GpC methylation (GmC) is absent. The methods apply a purified GpC methylase (M.CviPI) on nuclei. The majority of the DNA is protected by nucleosomes and inaccessible to M.CviPI. Only GpC within accessible regions can be methylated by M.CviPI. After bisulfite conversion and sequencing, one can identify DNA methylation by CpG methylation, and chromatin accessbility by GpC methylation. These two methods differ at the library preparation steps after bisulfite conversion. scNOMe-seq uses a commercial kit (Pico Methyl-seq Library prep Kit) to make libraries, while scCOOL-seq simply uses scBS-seq method to make libraries. Click here to see a step-by-step protocol of scBS-seq. The final library structures of scNOMe-seq and scCOOL-seq are the same. scCOOL-seq uses some extra analysis to investigate CNV and ploidy.

 
 
 
diff --git a/methods_html/scRRBS.html b/methods_html/scRRBS.html
index ba8991b..562a235 100644
--- a/methods_html/scRRBS.html
+++ b/methods_html/scRRBS.html
@@ -9,9 +9,9 @@
 
 
scRRBS

 
-
The scRRBS method is originally published by Guo et al. in Genome Research. Later on, the authors published a detailed step-by-step protocol in Nature Protocols 10, 645-659. This web page is created according to their Nature Protocols publication.

+
The scRRBS method is originally published by Guo et al. in Genome Research. Later on, the authors published a detailed step-by-step protocol in Nature Protocols 10, 645-659. This web page is created according to their Nature Protocols publication.

 
-
scRRBS is based on reduced representation bisulfite sequencing (RRBS), where the genome is fragmented by a restriction enzyme (often MspI), and methyl-C is investigated in the resulting fragments. It is not whole genome, but it is cost effective and generates high coverage data especiallhy around CpG islands.

+
scRRBS is based on reduced representation bisulfite sequencing (RRBS), where the genome is fragmented by a restriction enzyme (often MspI), and methyl-C is investigated in the resulting fragments. It is not whole genome, but it is cost effective and generates high coverage data especiallhy around CpG islands.

 
 

 
diff --git a/methods_html/scTHS-seq.html b/methods_html/scTHS-seq.html
index f660328..6ed35b8 100644
--- a/methods_html/scTHS-seq.html
+++ b/methods_html/scTHS-seq.html
@@ -9,23 +9,23 @@
 
 
scTHS-seq

 
-
THS-seq, originally published by Sos et al., uses similar principles as ATAC-seq to investigate chromatin accessibility. The key difference is that in THS-seq, a Tn5 transoposome homodimer loaded with T7 promoter sequence is used for the tagmentation, and the resulting fragments are amplified by in vitro transcription. In this way, every Tn5 cutting event is theoretically amplifiable, whereas in the widely used Illumina Tn5 transposomes only 50% of fragments with distinct adapters can be amplified.

+
THS-seq, originally published by Sos et al., uses similar principles as ATAC-seq to investigate chromatin accessibility. The key difference is that in THS-seq, a Tn5 transoposome homodimer loaded with T7 promoter sequence is used for the tagmentation, and the resulting fragments are amplified by in vitro transcription. In this way, every Tn5 cutting event is theoretically amplifiable, whereas in the widely used Illumina Tn5 transposomes only 50% of fragments with distinct adapters can be amplified.

 
-
The scTHS-seq takes the combinatorial indexing strategy to make the original THS-seq method to work at the single cell level.

+
The scTHS-seq takes the combinatorial indexing strategy to make the original THS-seq method to work at the single cell level.

 
 

 
 
Adapter and primer sequences:

 
 
scT7_r5{001..384}_i5_top: 5'- AATTAATACGACTCACTATAGGGAGATCCACGCGC[6-bp Tn5 barcode]TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG -3'

-
* There are a total of 384 barcoded scT7_r5 sequences. For the full list, check Supplementary Table 12. from the publication.

+
* There are a total of 384 barcoded scT7_r5 sequences. For the full list, check Supplementary Table 12. from the publication.

 
nXTv2_i7_top: 5'- GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG -3'

 
ME_bottom: 5'- /Phos/-CTGTCTCTTATACACATCT -3'

 
sss_scnXTv2: 5'- GGGAGATCCACGCGC -3'

 
T7 promoter: 5'- TAATACGACTCACTATAGGG -3'

 
Random hexamer for reverse transcription: 5'- [maybe there is an overhang]NNNNNN -3'

 
scT7_S5xx: 5'- AATGATACGGCGACCACCGAGATCTACAC[8-bp i5 index]GGGAGATCCACGCGC -3', these are similar to Nextera XT v2 S5xx primers.

-
* There are a total of 16 scT7_S5xx sequences. For the full list, check Supplementary Table 12. from the publication.

+
* There are a total of 16 scT7_S5xx sequences. For the full list, check Supplementary Table 12. from the publication.

 
Nextera (XT) N7xx Index primer: 5'- CAAGCAGAAGACGGCATACGAGAT[8-bp i7 index]GTCTCGTGGGCTCGG -3'

 
nXTv2_read1 sequencing primer: 5'- TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG -3'

 
nXTv2_i7_index_read sequencing primer: 5'- CTGTCTCTTATACACATCTCCGAGCCCACGAGAC -3'

@@ -37,7 +37,7 @@ 
Adapter and primer sequences:

 
 
Step-by-step generation of oligo-dT19V:

 
(1) Anneal the scT7_r5{001..384}_i5_top and ME_bottom sequences to assemble the Tn5 tranposome homodimer for the nulcei tagmentation in well plates. Sort ~960 nulcei per well and perform tagmentation:

-Tn5 dimer
+Tn5 dimer
 
 
 Since it is a Tn5 homodimer, there is only one product:
diff --git a/methods_html/scTrio-seq.html b/methods_html/scTrio-seq.html
index 6e0febb..0ceb0d0 100644
--- a/methods_html/scTrio-seq.html
+++ b/methods_html/scTrio-seq.html
@@ -9,7 +9,7 @@
 
 
scTrio-seq

 
-
The scTrio-seq method was published by Hou et al. in Cell Research 26, 304-319. It uses gentle lysis to separate cytoplasm (where most mRNA is located) and nulcei, which achieves phycical separation of mRNA and genomic DNA. For the mRNA part, the Tang 2009 method was used for scRNA-seq, click here to see the step-by-step protocol; for the genomic DNA analyses, scRRBS method was used, and click here to see the step-by-step protocol.

+
The scTrio-seq method was published by Hou et al. in Cell Research 26, 304-319. It uses gentle lysis to separate cytoplasm (where most mRNA is located) and nulcei, which achieves phycical separation of mRNA and genomic DNA. For the mRNA part, the Tang 2009 method was used for scRNA-seq, click here to see the step-by-step protocol; for the genomic DNA analyses, scRRBS method was used, and click here to see the step-by-step protocol.

 
 
 
diff --git a/methods_html/scifi-ATAC-seq.html b/methods_html/scifi-ATAC-seq.html
index 30c424c..84e6e34 100644
--- a/methods_html/scifi-ATAC-seq.html
+++ b/methods_html/scifi-ATAC-seq.html
@@ -9,7 +9,7 @@
 
 
scifi-ATAC-seq

 
-
The idea of scifi-ATAC-seq is very similar to dsciATAC-seq and txci-ATAC-seq. It works on the 10X Genomics Single Cell ATAC platform, but it does require custom sequencing primer and recipe. The procedure was based on the information from the preprint.

+
The idea of scifi-ATAC-seq is very similar to dsciATAC-seq and txci-ATAC-seq. It works on the 10X Genomics Single Cell ATAC platform, but it does require custom sequencing primer and recipe. The procedure was based on the information from the preprint.

 
 

 
@@ -36,7 +36,7 @@ 
Adapter and primer sequences:

 
 
Step-by-step library generation:

 
(1) Anneal "Tn5ME-bottom+Tn5-ME-A" and "Tn5ME-bottom+Tn5-ME-B" and use those to assemble the indexed Tn5 transposomes:

-Tn5 dimer
+Tn5 dimer
 
 
(2) Bulk nuclei tagging by the indexed Tn5 shown above. There are 3 different products (will create 9 bp gap):

 
diff --git a/methods_html/scifi-RNA-seq.html b/methods_html/scifi-RNA-seq.html
index 25afaec..748cd19 100644
--- a/methods_html/scifi-RNA-seq.html
+++ b/methods_html/scifi-RNA-seq.html
@@ -15,7 +15,7 @@ 
Adapter and primer sequences:

 
 
SCIFI_LIG384_{001..384}: 5'- /Phos/ACACTCTTTCCCTACACGACGCTCTTCCGATCT[8-bp UMI][13-bp RT barcode]TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTVN

-
* There are a tolal of 384 barcodes. Check the Supplementary Table 1 from the manuscript for a full list.

+
* There are a tolal of 384 barcodes. Check the Supplementary Table 1 from the manuscript for a full list.

 
Beads-oligo: |--5'- AATGATACGGCGACCACCGAGATCTACAC[16-bp bead barcode]TCGTCGGCAGCGTC -3'

 
Bridge-Oligo_truseq_ddC: 5'- CGTCGTGTAGGGAAAGAGTGTGACGCTGCCGACGA[ddC] -3'

 
Template-Switching-Oligo(TSO): 5'- AAGCAGTGGTATCAACGCAGAGTGAATrGrGrG -3'

diff --git a/methods_html/tang2009.html b/methods_html/tang2009.html
index 28f8349..b1fddb1 100644
--- a/methods_html/tang2009.html
+++ b/methods_html/tang2009.html
@@ -9,9 +9,9 @@
 
 
Tang 2009

 
-
In 2009, Tang et al. presented the first method to sequence the whole transcriptome (mRNA) of a single cell in Nature Methods 6, 377–382, which marks the start of the single cell genomics era. In 2010, the authors published a detailed step-by-step protocol in Nature Protocols, 5, 516-535. This webpage is based on the Nature Protocols paper. The method was designed to be sequenced on a SOLiD sequencer, that's why the sequencing adaptors are different from other methods for Illumina.

+
In 2009, Tang et al. presented the first method to sequence the whole transcriptome (mRNA) of a single cell in Nature Methods 6, 377-382, which marks the start of the single cell genomics era. In 2010, the authors published a detailed step-by-step protocol in Nature Protocols, 5, 516-535. This webpage is based on the Nature Protocols paper. The method was designed to be sequenced on a SOLiD sequencer, that's why the sequencing adaptors are different from other methods for Illumina.

 
-
I'm not very familiar with SOLiD sequencing, so there might be mistakes. If you spot any, please let me know.

+
I'm not very familiar with SOLiD sequencing, so there might be mistakes. If you spot any, please let me know.

BD Rhapsody WTA