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Graph	Name	Retrieved From	View
	indexing_bed	https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git Path: structuralvariants/subworkflows/indexing_bed.cwl Branch/Commit ID: 86f2f3fb64e916607637d93cf6715bab90b1f1d3
	kmer_top_n_extract	https://github.com/ncbi/pgap.git Path: task_types/tt_kmer_top_n_extract.cwl Branch/Commit ID: be9d12a3f8e1924183a1dc6a0bda6ada4195ca71
	kmer_cache_store	https://github.com/ncbi/pgap.git Path: task_types/tt_kmer_cache_store.cwl Branch/Commit ID: b4a6e46405c08e0b14ad92f0ab38bcc4a69caa5c
	PCA - Principal Component Analysis Principal Component Analysis --------------- Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. The calculation is done by a singular value decomposition of the (centered and possibly scaled) data matrix, not by using eigen on the covariance matrix. This is generally the preferred method for numerical accuracy.	https://github.com/datirium/workflows.git Path: workflows/pca.cwl Branch/Commit ID: 36fd18f11e939d3908b1eca8d2939402f7a99b0f
	kmer_gc_extract_wnode	https://github.com/ncbi/pgap.git Path: task_types/tt_kmer_gc_extract_wnode.cwl Branch/Commit ID: 6d8d29a2156b93a75f1d1c6952738bd63f6bd98e
	tt_blastn_wnode	https://github.com/ncbi/pgap.git Path: task_types/tt_blastn_wnode.cwl Branch/Commit ID: f403d9e26d60d3e3591a03077bc9dfa188b1c2bb
	PGAP Pipeline, simple user input, PGAPX-134 PGAP pipeline for external usage, powered via containers, simple user input: (FASTA + yaml only, no template)	https://github.com/ncbi/pgap.git Path: pgap.cwl Branch/Commit ID: be9d12a3f8e1924183a1dc6a0bda6ada4195ca71
	Unaligned bam to sorted, markduped bam	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/align_sort_markdup.cwl Branch/Commit ID: ae57b60e9b01e3f0f02f4e828042748409dff5a3
	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: 36fd18f11e939d3908b1eca8d2939402f7a99b0f
	SV filtering workflow	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/filter_sv_vcf.cwl Branch/Commit ID: 60edaf6f57eaaf02cda1a3d8cb9a825aa64a43e2