Explore Workflows

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

Path: tests/wf/cache_test_workflow.cwl

Branch/Commit ID: 047e69bb169e79fad6a7285ee798c4ecec3b218b

DiffBind Multi-factor Analysis ------------------------------ 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-multi-factor.cwl

Branch/Commit ID: fa4f172486288a1a9d23864f1d6962d85a453e16

Creates indices for: * [STAR](https://github.com/alexdobin/STAR) v2.5.3a (03/17/2017) PMID: [23104886](https://www.ncbi.nlm.nih.gov/pubmed/23104886) * [bowtie](http://bowtie-bio.sourceforge.net/tutorial.shtml) v1.2.0 (12/30/2016) It performs the following steps: 1. `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. Outputs indices as [Direcotry](http://www.commonwl.org/v1.0/CommandLineTool.html#Directory) data type 3. Separates *chrNameLength.txt* file from Directory output 4. `bowtie-build` to generate indices requires genome [FASTA](http://zhanglab.ccmb.med.umich.edu/FASTA/) file as input, returns results as a group of main and secondary files

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

Path: workflows/genome-indices.cwl

Branch/Commit ID: d1bef74924efcb8bfaa00987b3f148d5a192b7a9

Motif Finding with HOMER with custom background regions --------------------------------------------------- HOMER contains a novel motif discovery algorithm that was designed for regulatory element analysis in genomics applications (DNA only, no protein). It is a differential motif discovery algorithm, which means that it takes two sets of sequences and tries to identify the regulatory elements that are specifically enriched in on set relative to the other. It uses ZOOPS scoring (zero or one occurrence per sequence) coupled with the hypergeometric enrichment calculations (or binomial) to determine motif enrichment. HOMER also tries its best to account for sequenced bias in the dataset. It was designed with ChIP-Seq and promoter analysis in mind, but can be applied to pretty much any nucleic acids motif finding problem. For more information please refer to: ------------------------------------- [Official documentation](http://homer.ucsd.edu/homer/motif/)

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

Path: workflows/homer-motif-analysis-bg.cwl

Branch/Commit ID: c0ca7b140d776eec223ceb1c620eda17281860c4

Workflow makes indices for [bowtie](http://bowtie-bio.sourceforge.net/tutorial.shtml) v1.2.0 (12/30/2016). Executes `bowtie-index` to generate indices requires genome [FASTA](http://zhanglab.ccmb.med.umich.edu/FASTA/) file as input, returns results as a directory

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

Path: workflows/bowtie-index.cwl

Branch/Commit ID: 9ee330737f4603e4e959ffe786fbb2046db70a00

https://github.com/NCI-GDC/gdc-dnaseq-cwl.git

Path: workflows/dnaseq/metrics.cwl

Branch/Commit ID: 469bbb5e318146b3096f307f5d8e9f72cbd6bc06

Runs ChIP-Seq BioWardrobe basic analysis with paired-end input data files.

https://github.com/Barski-lab/workflows.git

Path: workflows/trim-chipseq-pe.cwl

Branch/Commit ID: e89b2c17aa5efccef6ca424dec5a0a021bd8d20c

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

Path: tests/output-arrays-file-wf.cwl

Branch/Commit ID: e515226f8ac0f7985cd94dae4a301150adae3050

Sequence reads are first cleaned from adapters and transformed into fully bisulfite-converted forward (C->T) and reverse read (G->A conversion of the forward strand) versions, before they are aligned to similarly converted versions of the genome (also C->T and G->A converted). Sequence reads that produce a unique best alignment from the four alignment processes against the bisulfite genomes (which are running in parallel) are then compared to the normal genomic sequence and the methylation state of all cytosine positions in the read is inferred. A read is considered to align uniquely if an alignment has a unique best alignment score (as reported by the AS:i field). If a read produces several alignments with the same number of mismatches or with the same alignment score (AS:i field), a read (or a read-pair) is discarded altogether. On the next step we extract the methylation call for every single C analysed. The position of every single C will be written out to a new output file, depending on its context (CpG, CHG or CHH), whereby methylated Cs will be labelled as forward reads (+), non-methylated Cs as reverse reads (-). The output of the methylation extractor is then transformed into a bedGraph and coverage file. The bedGraph counts output is then used to generate a genome-wide cytosine report which reports the number on every single CpG (optionally every single cytosine) in the genome, irrespective of whether it was covered by any reads or not. As this type of report is informative for cytosines on both strands the output may be fairly large (~46mn CpG positions or >1.2bn total cytosine positions in the human genome).

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

Path: workflows/bismark-methylation-se.cwl

Branch/Commit ID: c602e3cdd72ff904dd54d46ba2b5146eb1c57022

# 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: 4dcc405133f22c63478b6091fb5f591b6be8950f