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Graph	Name	Retrieved From	View
	retrieve metadata from Zenodo community For a given Zenodo community, retrieve its repository records as Zenodo JSON and (eventually) schema.org JSON-LD and DataCite v4 XML.	https://github.com/stain/ro-index-paper.git Path: code/data-gathering/workflows/zenodo-records.cwl Branch/Commit ID: 7166c89aad278bdf438d593040948f3b95e6c410
	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: 29bf638904709cfbf10908adcd51ba4886ace94a
	Filter ChIP/ATAC peaks for Tag Density Profile or Motif Enrichment analyses Filters ChIP/ATAC peaks with the neatest genes assigned for Tag Density Profile or Motif Enrichment analyses ============================================================================================================ Tool filters output from any ChIP/ATAC pipeline to create a file with regions of interest for Tag Density Profile or Motif Enrichment analyses. Peaks with duplicated coordinates are discarded.	https://github.com/datirium/workflows.git Path: workflows/filter-peaks-for-heatmap.cwl Branch/Commit ID: 00ea05e22788029370898fd4c17798b11edf0e57
	scatter-valuefrom-wf3.cwl#main	https://github.com/common-workflow-language/cwltool.git Path: cwltool/schemas/v1.0/v1.0/scatter-valuefrom-wf3.cwl Branch/Commit ID: bbe20f54deea92d9c9cd38cb1f23c4423133d3de Packed ID: main
	Run genomic CMsearch (Rfam rRNA)	https://github.com/ncbi/pgap.git Path: bacterial_ncrna/wf_gcmsearch.cwl Branch/Commit ID: be9d12a3f8e1924183a1dc6a0bda6ada4195ca71
	Detect Docm variants	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/docm_cle.cwl Branch/Commit ID: 44ada20f3eeb59005d5bd999d2435102e9bae991
	Bacterial Annotation, pass 1, genemark training, by HMMs (first pass)	https://github.com/ncbi/pgap.git Path: bacterial_annot/wf_bacterial_annot_pass1.cwl Branch/Commit ID: be9d12a3f8e1924183a1dc6a0bda6ada4195ca71
	Trim Galore ChIP-Seq pipeline single-read 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. _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. 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-chipseq-se.cwl Branch/Commit ID: bfa3843bcf36125ff258d6314f64b41336f06e6b
	extract_gencoll_ids	https://github.com/ncbi/pgap.git Path: task_types/tt_extract_gencoll_ids.cwl Branch/Commit ID: e668f9c4047f1971ae53040a5af3eccc4bfc3c53
	blastp_wnode_naming	https://github.com/ncbi/pgap.git Path: task_types/tt_blastp_wnode_naming.cwl Branch/Commit ID: be9d12a3f8e1924183a1dc6a0bda6ada4195ca71