Explore Workflows

https://github.com/ncbi/pgap.git

Path: vecscreen/vecscreen.cwl

Branch/Commit ID: 8ea3637b0f11eac1ea5599c41d74e00d85fb778d

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/scatter-wf1.cwl

Branch/Commit ID: 57baec040c99d7edef8242ef51b5470b1c82d733

Motif Finding with HOMER with target and background regions from peaks --------------------------------------------------- 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-peak.cwl

Branch/Commit ID: 7eef0294395d83ff0765fce61726a59d71126422

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

Path: cwltool/schemas/v1.0/v1.0/count-lines9-wf.cwl

Branch/Commit ID: b82ce7ae901a54c7a062fd5eefd8d5ceb5a4d684

https://github.com/ncbi/pgap.git

Path: bacterial_annot/wf_ab_initio_antifam.cwl

Branch/Commit ID: 8ea3637b0f11eac1ea5599c41d74e00d85fb778d

https://github.com/ncbi/pgap.git

Path: bacterial_annot/wf_orf_hmms.cwl

Branch/Commit ID: 8ea3637b0f11eac1ea5599c41d74e00d85fb778d

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

Path: cwltool/schemas/v1.0/v1.0/search.cwl

Branch/Commit ID: 75271e2a0887d47cca4077b60dd51ac763c09b63

Packed ID: main

https://github.com/kids-first/kf-rnaseq-workflow.git

Path: workflow/kfdrc_flagstat_qc.cwl

Branch/Commit ID: 3fd55a819cda2a1fc48957b5daa2bebdaee843fb

https://github.com/genome/analysis-workflows.git

Path: definitions/pipelines/tumor_only_exome.cwl

Branch/Commit ID: 3f3b186da9bf82a5e2ae74ba27aef35a46174ebe

The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **ChIP-Seq** basic analysis workflow for a **single-read** experiment with Trim Galore. The pipeline was adapted for ATAC-Seq single-read data analysis by updating genome coverage step. _Trim Galore_ is a wrapper around [Cutadapt](https://github.com/marcelm/cutadapt) and [FastQC](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) to consistently apply adapter and quality trimming to FastQ files, with extra functionality for RRBS data. In outputs it returns coordinate sorted BAM file alongside with index BAI file, quality statistics of the input FASTQ file, reads coverage in a form of BigWig file, peaks calling data in a form of narrowPeak or broadPeak files, islands with the assigned nearest genes and region type, data for average tag density plot (on the base of BAM file). Workflow starts with step *fastx\_quality\_stats* from FASTX-Toolkit to calculate quality statistics for input FASTQ file. At the same time `bowtie` is used to align reads from input FASTQ file to reference genome *bowtie\_aligner*. The output of this step is unsorted SAM file which is being sorted and indexed by `samtools sort` and `samtools index` *samtools\_sort\_index*. Based on workflow’s input parameters indexed and sorted BAM file can be processed by `samtools rmdup` *samtools\_rmdup* to get rid of duplicated reads. If removing duplicates is not required the original input BAM and BAI files return. Otherwise step *samtools\_sort\_index\_after\_rmdup* repeat `samtools sort` and `samtools index` with BAM and BAI files. Right after that `macs2 callpeak` performs peak calling *macs2\_callpeak*. On the base of returned outputs the next step *macs2\_island\_count* calculates the number of islands and estimated fragment size. If the last one is less that 80bp (hardcoded in the workflow) `macs2 callpeak` is rerun again with forced fixed fragment size value (*macs2\_callpeak\_forced*). If at the very beginning it was set in workflow input parameters to force run peak calling with fixed fragment size, this step is skipped and the original peak calling results are saved. In the next step workflow again calculates the number of islands and estimates fragment size (*macs2\_island\_count\_forced*) for the data obtained from *macs2\_callpeak\_forced* step. If the last one was skipped the results from *macs2\_island\_count\_forced* step are equal to the ones obtained from *macs2\_island\_count* step. Next step (*macs2\_stat*) is used to define which of the islands and estimated fragment size should be used in workflow output: either from *macs2\_island\_count* step or from *macs2\_island\_count\_forced* step. If input trigger of this step is set to True it means that *macs2\_callpeak\_forced* step was run and it returned different from *macs2\_callpeak* step results, so *macs2\_stat* step should return [fragments\_new, fragments\_old, islands\_new], if trigger is False the step returns [fragments\_old, fragments\_old, islands\_old], where sufix \"old\" defines results obtained from *macs2\_island\_count* step and sufix \"new\" - from *macs2\_island\_count\_forced* step. The following two steps (*bamtools\_stats* and *bam\_to\_bigwig*) are used to calculate coverage on the base of input BAM file and save it in BigWig format. For that purpose bamtools stats returns the number of mapped reads number which is then used as scaling factor by bedtools genomecov when it performs coverage calculation and saves it in BED format. The last one is then being sorted and converted to BigWig format by bedGraphToBigWig tool from UCSC utilities. To adapt the pipeline for ATAC-Seq data analysis we calculate genome coverage using only the first 9 bp from every read. Step *get\_stat* is used to return a text file with statistics in a form of [TOTAL, ALIGNED, SUPRESSED, USED] reads count. Step *island\_intersect* assigns genes and regions to the islands obtained from *macs2\_callpeak\_forced*. Step *average\_tag\_density* is used to calculate data for average tag density plot on the base of BAM file.

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

Path: workflows/trim-atacseq-se.cwl

Branch/Commit ID: 4a80f5b8f86c83af39494ecc309b789aeda77964