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Graph	Name	Retrieved From	View
	align_merge_sas	https://github.com/ncbi/pgap.git Path: task_types/tt_align_merge_sas.cwl Branch/Commit ID: b548dc1ec6cb90d14d57a813fb69430adaa1c425
	RNA-Seq pipeline paired-end strand specific The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) RNA-Seq basic analysis for a paired-end experiment. A corresponded input [FASTQ](http://maq.sourceforge.net/fastq.shtml) file has to be provided. Current workflow should be used only with the paired-end RNA-Seq data. It performs the following steps: 1. Use STAR to align reads from input FASTQ files according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. Use fastx_quality_stats to analyze input FASTQ files and generate quality statistics files 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) 4. Generate BigWig file on the base of sorted BAM file 5. Map input FASTQ files to predefined rRNA reference indices using Bowtie to define the level of rRNA contamination; export resulted statistics to file 6. 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-pe-dutp.cwl Branch/Commit ID: 9ee330737f4603e4e959ffe786fbb2046db70a00
	Pairwise genomic regions intersection Pairwise genomic regions intersection ============================================= Overlaps peaks from two ChIP/ATAC experiments	https://github.com/datirium/workflows.git Path: workflows/peak-intersect.cwl Branch/Commit ID: 7ae3b75bbe614e59cdeaba06047234a6c40c0fe9
	scatter-valuefrom-wf5.cwl	https://github.com/common-workflow-language/common-workflow-language.git Path: v1.0/v1.0/scatter-valuefrom-wf5.cwl Branch/Commit ID: 22490926651174c6cbe01c76c2ded3c9e8d0ee6f
	Unaligned BAM to BQSR and VCF	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/bam_to_bqsr_no_dup_marking.cwl Branch/Commit ID: c6bbd4cdd612b3b5cc6e9000df4800c21e192bf5
	kmer_seq_entry_extract_wnode	https://github.com/ncbi/pgap.git Path: task_types/tt_kmer_seq_entry_extract_wnode.cwl Branch/Commit ID: 4533a5e930305c674057bc4cf5dda4f39d39b5df
	Trim Galore RNA-Seq pipeline single-read The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) RNA-Seq basic analysis for a single-end 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-end RNA-Seq data. It performs the following steps: 1. Trim adapters from input FASTQ file 2. Use STAR to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 3. Use fastx_quality_stats to analyze input FASTQ file and generate quality statistics file 4. 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/trim-rnaseq-se.cwl Branch/Commit ID: 3fc68366adb179927af5528c27b153abaf94494d
	stdout-wf_v1_2.cwl	https://github.com/common-workflow-language/cwl-utils.git Path: testdata/stdout-wf_v1_2.cwl Branch/Commit ID: 77669d4dd1d1ebd2bdd9810f911608146d9b8e51
	scatter-valuefrom-wf6.cwl	https://github.com/common-workflow-language/cwltool.git Path: cwltool/schemas/v1.0/v1.0/scatter-valuefrom-wf6.cwl Branch/Commit ID: f207d168f4e7eb4dd2279840d4062ba75d9c79c3
	Bismark Methylation - pipeline for BS-Seq data analysis 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: 4dcc405133f22c63478b6091fb5f591b6be8950f