Explore Workflows

# ChIP-Seq basic analysis workflow for single-read data Reads are aligned to the reference genome with [Bowtie](http://bowtie-bio.sourceforge.net/index.shtml). Results are saved as coordinate sorted [BAM](http://samtools.github.io/hts-specs/SAMv1.pdf) alignment and index BAI files. Optionally, PCR duplicates can be removed. To obtain coverage in [bigWig](https://genome.ucsc.edu/goldenpath/help/bigWig.html) format, average fragment length is calculated by [MACS2](https://github.com/taoliu/MACS), and individual reads are extended to this length in the 3’ direction. Areas of enrichment identified by MACS2 are saved in ENCODE [narrow peak](http://genome.ucsc.edu/FAQ/FAQformat.html#format12) or [broad peak](https://genome.ucsc.edu/FAQ/FAQformat.html#format13) formats. Called peaks together with the nearest genes are saved in TSV format. In addition to basic statistics (number of total/mapped/multi-mapped/unmapped/duplicate reads), pipeline generates several quality control measures. Base frequency plots are used to estimate adapter contamination, a frequent occurrence in low-input ChIP-Seq experiments. Expected distinct reads count from [Preseq](http://smithlabresearch.org/software/preseq/) can be used to estimate read redundancy for a given sequencing depth. Average tag density profiles can be used to estimate ChIP enrichment for promoter proximal histone modifications. Use of different parameters for different antibodies (calling broad or narrow peaks) is possible. Additionally, users can elect to use BAM file from another experiment as control for MACS2 peak calling. ## Cite as *Kartashov AV, Barski A. BioWardrobe: an integrated platform for analysis of epigenomics and transcriptomics data. Genome Biol. 2015;16(1):158. Published 2015 Aug 7. [doi:10.1186/s13059-015-0720-3](https://www.ncbi.nlm.nih.gov/pubmed/26248465)* ## Software versions - Bowtie 1.2.0 - Samtools 1.4 - Preseq 2.0 - MACS2 2.1.1.20160309 - Bedtools 2.26.0 - UCSC userApps v358 ## Inputs | ID | Label | Description | Required | Default | Upstream analyses | | ------------------------- | ---------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- | :------: | ------- | ------------------------------- | | **fastq\_file** | FASTQ file | Single-read sequencing data in FASTQ format (fastq, fq, bzip2, gzip, zip) | + | | | | **indices\_folder** | Genome indices | Directory with the genome indices generated by Bowtie | + | | genome\_indices/bowtie\_indices | | **annotation\_file** | Genome annotation file | Genome annotation file in TSV format | + | | genome\_indices/annotation | | **genome\_size** | Effective genome size | The length of the mappable genome (hs, mm, ce, dm or number, for example 2.7e9) | + | | genome\_indices/genome\_size | | **chrom\_length** | Chromosome lengths file | Chromosome lengths file in TSV format | + | | genome\_indices/chrom\_length | | **broad\_peak** | Call broad peaks | Make MACS2 call broad peaks by linking nearby highly enriched regions | + | | | | **control\_file** | Control ChIP-Seq single-read experiment | Indexed BAM file from the ChIP-Seq single-read experiment to be used as a control for MACS2 peak calling | | Null | control\_file/bambai\_pair | | **exp\_fragment\_size** | Expected fragment size | Expected fragment size for read extenstion towards 3' end if *force\_fragment\_size* was set to True or if calculated by MACS2 fragment size was less that 80 bp | | 150 | | | **force\_fragment\_size** | Force peak calling with expected fragment size | Make MACS2 don't build the shifting model and use expected fragment size for read extenstion towards 3' end | | False | | | **clip\_3p\_end** | Clip from 3' end | Number of base pairs to clip from 3' end | | 0 | | | **clip\_5p\_end** | Clip from 5' end | Number of base pairs to clip from 5' end | | 0 | | | **remove\_duplicates** | Remove PCR duplicates | Remove PCR duplicates from sorted BAM file | | False | | | **threads** | Number of threads | Number of threads for those steps that support multithreading | | 2 | | ## Outputs | ID | Label | Description | Required | Visualization | | ------------------------ | ---------------------------------- | ------------------------------------------------------------------------------------ | :------: | ------------------------------------------------------------------ | | **fastx\_statistics** | FASTQ quality statistics | FASTQ quality statistics in TSV format | + | *Base Frequency* and *Quality Control* plots in *QC Plots* tab | | **bambai\_pair** | Aligned reads | Coordinate sorted BAM alignment and index BAI files | + | *Nucleotide Sequence Alignments* track in *IGV Genome Browser* tab | | **bigwig** | Genome coverage | Genome coverage in bigWig format | + | *Genome Coverage* track in *IGV Genome Browser* tab | | **iaintersect\_result** | Gene annotated peaks | MACS2 peak file annotated with nearby genes | + | *Peak Coordinates* table in *Peak Calling* tab | | **atdp\_result** | Average Tag Density Plot | Average Tag Density Plot file in TSV format | + | *Average Tag Density Plot* in *QC Plots* tab | | **macs2\_called\_peaks** | Called peaks | Called peaks file with 1-based coordinates in XLS format | + | | | **macs2\_narrow\_peaks** | Narrow peaks | Called peaks file in ENCODE narrow peak format | | *Narrow peaks* track in *IGV Genome Browser* tab | | **macs2\_broad\_peaks** | Broad peaks | Called peaks file in ENCODE broad peak format | | *Broad peaks* track in *IGV Genome Browser* tab | | **preseq\_estimates** | Expected Distinct Reads Count Plot | Expected distinct reads count file from Preseq in TSV format | | *Expected Distinct Reads Count Plot* in *QC Plots* tab | | **workflow\_statistics** | Workflow execution statistics | Overall workflow execution statistics from bowtie\_aligner and samtools\_rmdup steps | + | *Overview* tab and experiment's preview | | **bowtie\_log** | Read alignment log | Read alignment log file from Bowtie | + | |

https://github.com/datirium/workflows.git

Path: workflows/chipseq-se.cwl

Branch/Commit ID: 4a5c59829ff8b9f3c843e66e3c675dcd9c689ed5

Cross-Linking ImmunoPrecipitation ================================= `CLIP` (`cross-linking immunoprecipitation`) is a method used in molecular biology that combines UV cross-linking with immunoprecipitation in order to analyse protein interactions with RNA or to precisely locate RNA modifications (e.g. m6A). (Uhl|Houwaart|Corrado|Wright|Backofen|2017)(Ule|Jensen|Ruggiu|Mele|2003)(Sugimoto|König|Hussain|Zupan|2012)(Zhang|Darnell|2011) (Ke| Alemu| Mertens| Gantman|2015) CLIP-based techniques can be used to map RNA binding protein binding sites or RNA modification sites (Ke| Alemu| Mertens| Gantman|2015)(Ke| Pandya-Jones| Saito| Fak|2017) of interest on a genome-wide scale, thereby increasing the understanding of post-transcriptional regulatory networks. The identification of sites where RNA-binding proteins (RNABPs) interact with target RNAs opens the door to understanding the vast complexity of RNA regulation. UV cross-linking and immunoprecipitation (CLIP) is a transformative technology in which RNAs purified from _in vivo_ cross-linked RNA-protein complexes are sequenced to reveal footprints of RNABP:RNA contacts. CLIP combined with high-throughput sequencing (HITS-CLIP) is a generalizable strategy to produce transcriptome-wide maps of RNA binding with higher accuracy and resolution than standard RNA immunoprecipitation (RIP) profiling or purely computational approaches. The application of CLIP to Argonaute proteins has expanded the utility of this approach to mapping binding sites for microRNAs and other small regulatory RNAs. Finally, recent advances in data analysis take advantage of cross-link–induced mutation sites (CIMS) to refine RNA-binding maps to single-nucleotide resolution. Once IP conditions are established, HITS-CLIP takes ~8 d to prepare RNA for sequencing. Established pipelines for data analysis, including those for CIMS, take 3–4 d. Workflow -------- CLIP begins with the in-vivo cross-linking of RNA-protein complexes using ultraviolet light (UV). Upon UV exposure, covalent bonds are formed between proteins and nucleic acids that are in close proximity. (Darnell|2012) The cross-linked cells are then lysed, and the protein of interest is isolated via immunoprecipitation. In order to allow for sequence specific priming of reverse transcription, RNA adapters are ligated to the 3' ends, while radiolabeled phosphates are transferred to the 5' ends of the RNA fragments. The RNA-protein complexes are then separated from free RNA using gel electrophoresis and membrane transfer. Proteinase K digestion is then performed in order to remove protein from the RNA-protein complexes. This step leaves a peptide at the cross-link site, allowing for the identification of the cross-linked nucleotide. (König| McGlincy| Ule|2012) After ligating RNA linkers to the RNA 5' ends, cDNA is synthesized via RT-PCR. High-throughput sequencing is then used to generate reads containing distinct barcodes that identify the last cDNA nucleotide. Interaction sites can be identified by mapping the reads back to the transcriptome.

https://github.com/datirium/workflows.git

Path: workflows/clipseq-se.cwl

Branch/Commit ID: b1a5dabeeeb9079b30b2871edd9c9034a1e00c1c

Devel version of Single-Cell Cell Ranger Aggregate ================================================== Workflow calls \"cellranger aggr\" command to combine output files from \"cellranger count\" (the molecule_info.h5 file from each run) into a single feature-barcode matrix containing all the data. When combining multiple GEM wells, the barcode sequences for each channel are distinguished by a GEM well suffix appended to the barcode sequence. Each GEM well is a physically distinct set of GEM partitions, but draws barcode sequences randomly from the pool of valid barcodes, known as the barcode whitelist. To keep the barcodes unique when aggregating multiple libraries, we append a small integer identifying the GEM well to the barcode nucleotide sequence, and use that nucleotide sequence plus ID as the unique identifier in the feature-barcode matrix. For example, AGACCATTGAGACTTA-1 and AGACCATTGAGACTTA-2 are distinct cell barcodes from different GEM wells, despite having the same barcode nucleotide sequence. This number, which tells us which GEM well this barcode sequence came from, is called the GEM well suffix. The numbering of the GEM wells will reflect the order that the GEM wells were provided in the \"molecule_info_h5\" and \"gem_well_labels\" inputs. When combining data from multiple GEM wells, the \"cellranger aggr\" pipeline automatically equalizes the average read depth per cell between groups before merging. This approach avoids artifacts that may be introduced due to differences in sequencing depth. It is possible to turn off normalization or change the way normalization is done through the \"normalization_mode\" input. The \"none\" value may be appropriate if you want to maximize sensitivity and plan to deal with depth normalization in a downstream step.

https://github.com/datirium/workflows.git

Path: workflows/cellranger-aggr.cwl

Branch/Commit ID: 7fb8a1ebf8145791440bc2fed9c5f2d78a19d04c

https://github.com/common-workflow-language/cwl-v1.1.git

Path: tests/scatter-valuefrom-wf6.cwl

Branch/Commit ID: 0e37d46e793e72b7c16b5ec03e22cb3ce1f55ba3

Differential Binding Analysis of ChIP-Seq Peak Data --------------------------------------------------- DiffBind processes ChIP-Seq data enriched for genomic loci where specific protein/DNA binding occurs, including peak sets identified by ChIP-Seq peak callers and aligned sequence read datasets. It is designed to work with multiple peak sets simultaneously, representing different ChIP experiments (antibodies, transcription factor and/or histone marks, experimental conditions, replicates) as well as managing the results of multiple peak callers. For more information please refer to: ------------------------------------- Ross-Innes CS, Stark R, Teschendorff AE, Holmes KA, Ali HR, Dunning MJ, Brown GD, Gojis O, Ellis IO, Green AR, Ali S, Chin S, Palmieri C, Caldas C, Carroll JS (2012). “Differential oestrogen receptor binding is associated with clinical outcome in breast cancer.” Nature, 481, -4.

https://github.com/datirium/workflows.git

Path: workflows/diffbind.cwl

Branch/Commit ID: 8a92669a566589d80fde9d151054ffc220ed4ddd

https://github.com/nci-gdc/gdc-dnaseq-cwl.git

Path: workflows/bamfastq_align/transform.cwl

Branch/Commit ID: dd7f86b3cc10eb1cda07dc2fc279ba2529c8ad61

Workflow makes indices for [STAR](https://github.com/alexdobin/STAR) v2.5.3a (03/17/2017) PMID: [23104886](https://www.ncbi.nlm.nih.gov/pubmed/23104886). It performs the following steps: 1. Runs `STAR --runMode genomeGenerate` to generate indices, based on [FASTA](http://zhanglab.ccmb.med.umich.edu/FASTA/) and [GTF](http://mblab.wustl.edu/GTF2.html) input files, returns results as an array of files 2. Transforms array of files into [Direcotry](http://www.commonwl.org/v1.0/CommandLineTool.html#Directory) data type 3. Separates *chrNameLength.txt* file as an output

https://github.com/datirium/workflows.git

Path: workflows/star-index.cwl

Branch/Commit ID: 9bf0aa495735f8081bb5870cb32fc898b9e6eb22

https://github.com/common-workflow-language/cwltool.git

Path: cwltool/schemas/v1.0/v1.0/count-lines12-wf.cwl

Branch/Commit ID: e835bc0487fe42fb330b6222c9be65d18dd81ec9

Outputs a message using echo

https://github.com/common-workflow-language/cwltool.git

Path: tests/wf/hello-workflow.cwl

Branch/Commit ID: f207d168f4e7eb4dd2279840d4062ba75d9c79c3

https://github.com/nci-gdc/gdc-dnaseq-cwl.git

Path: workflows/bamfastq_align/extract_readgroup_fastq_pe.cwl

Branch/Commit ID: dd7f86b3cc10eb1cda07dc2fc279ba2529c8ad61