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Graph	Name	Retrieved From	View
	cnv_manta CNV Manta calling	https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git Path: structuralvariants/cwl/abstract_operations/subworkflows/cnv_manta.cwl Branch/Commit ID: 9ac2d150a57d1996210ed6a44dd0c0404dab383c
	trim-rnaseq-pe.cwl Runs RNA-Seq BioWardrobe basic analysis with pair-end data file.	https://github.com/Barski-lab/workflows.git Path: workflows/trim-rnaseq-pe.cwl Branch/Commit ID: 12edfc2207507e53c6b5bb21e50decb5535a12f7
	ROSE: rank ordering of super-enhancers Super-enhancers, consist of clusters of enhancers that are densely occupied by the master regulators and Mediator. Super-enhancers differ from typical enhancers in size, transcription factor density and content, ability to activate transcription, and sensitivity to perturbation. Use to create stitched enhancers, and to separate super-enhancers from typical enhancers using sequencing data (.bam) given a file of previously identified constituent enhancers (.gff)	https://github.com/datirium/workflows.git Path: workflows/super-enhancer.cwl Branch/Commit ID: 935a78f1aff757f977de4e3672aefead3b23606b
	FASTQ Vector Removal This workflow clean up vectros from fastq files	https://github.com/ncbi/cwl-ngs-workflows-cbb.git Path: workflows/File-formats/remove-fastq-reads-from-blast.cwl Branch/Commit ID: e541470bc9d0b064bc4ed7dd2b45d8ec67760613
	workflow_demultiplexing.cwl	https://git.wur.nl/unlock/cwl.git Path: cwl/workflows/workflow_demultiplexing.cwl Branch/Commit ID: cd0c19d51068c5407cd70b718a561d4662819d87
	WGS and MT analysis for fastq files rna / protein - qc, preprocess, filter, annotation, index, abundance	https://github.com/MG-RAST/pipeline.git Path: CWL/Workflows/wgs-fasta.workflow.cwl Branch/Commit ID: 091374dc59a23966338638a668ae397d4ee20b2f
	cache_asnb_entries	https://github.com/ncbi/pgap.git Path: task_types/tt_cache_asnb_entries.cwl Branch/Commit ID: c18a7e5164cb6b19f06b3d1e869407c118a87f7e
	ChIP-Seq pipeline paired-end 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 paired-end experiment. A [FASTQ](http://maq.sourceforge.net/fastq.shtml) input file has to be provided. The pipeline produces a sorted BAM file alongside with index BAI file, quality statistics of the input FASTQ file, coverage by estimated fragments as a 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. 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 an unsorted SAM file which is being sorted and indexed by `samtools sort` and `samtools index` samtools\_sort\_index. Depending 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 BAM and BAI files are returned. Otherwise step samtools\_sort\_index\_after\_rmdup repeat `samtools sort` and `samtools index` with BAM and BAI files without duplicates. Next `macs2 callpeak` performs peak calling macs2\_callpeak and the next step reports macs2\_island\_count the number of islands and estimated fragment size. If the latter is less that 80bp (hardcoded in the workflow) `macs2 callpeak` is rerun again with forced fixed fragment size value (macs2\_callpeak\_forced). It is also possible to force MACS2 to use pre set fragment size in the first place. 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 from BAM file and save it in BigWig format. For that purpose bamtools stats returns the number of mapped reads which is then used as scaling factor by bedtools genomecov when it performs coverage calculation and saves it as a BEDgraph file whichis then sorted and converted to BigWig format by bedGraphToBigWig tool from UCSC utilities. 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 nearest 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 from the BAM file.	https://github.com/datirium/workflows.git Path: workflows/chipseq-pe.cwl Branch/Commit ID: 564156a9e1cc7c3679a926c479ba3ae133b1bfd4
	RNA-Seq pipeline single-read strand specific Note: should be updated The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) RNA-Seq basic analysis for strand specific single-read experiment. A corresponded input [FASTQ](http://maq.sourceforge.net/fastq.shtml) file has to be provided. Current workflow should be used only with the single-read RNA-Seq data. It performs the following steps: 1. Use STAR to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. Use fastx_quality_stats to analyze input FASTQ file and generate quality statistics file 3. Use samtools sort to generate coordinate sorted BAM(+BAI) file pair from the unsorted BAM file obtained on the step 1 (after running STAR) 5. Generate BigWig file on the base of sorted BAM file 6. Map input FASTQ file to predefined rRNA reference indices using Bowtie to define the level of rRNA contamination; export resulted statistics to file 7. Calculate isoform expression level for the sorted BAM file and GTF/TAB annotation file using GEEP reads-counting utility; export results to file	https://github.com/datirium/workflows.git Path: workflows/rnaseq-se-dutp.cwl Branch/Commit ID: 8049a781ac4aae579fbd3036fa0bf654532f15be
	kmer_cache_retrieve	https://github.com/ncbi/pgap.git Path: task_types/tt_kmer_cache_retrieve.cwl Branch/Commit ID: 664e99a23a3ed4ba36c08323ac597c4fbcd88df1