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Graph	Name	Retrieved From	View
	format_rrnas_from_seq_entry	https://github.com/ncbi/pgap.git Path: task_types/tt_format_rrnas_from_seq_entry.cwl Branch/Commit ID: 733ab7198a66a0153d0f03c3022ab53c17325ff8
	Deprecated. Single-cell Differential Expression Deprecated. Single-cell Differential Expression =============================================== Runs differential expression analysis for a subset of cells between two selected conditions.	https://github.com/datirium/workflows.git Path: workflows/sc_diff_expr.cwl Branch/Commit ID: 22880e0f41d0420a17d643e8a6e8ee18165bbfbf
	metrics.cwl	https://github.com/NCI-GDC/gdc-dnaseq-cwl.git Path: workflows/dnaseq/metrics.cwl Branch/Commit ID: 8edf6a5e4e7790434ad0742e50d0c97a5d0bb846
	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: 7bfe73a708dbf31d037303bb5a8fed1a79984b0f Packed ID: main
	umi molecular alignment workflow	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/molecular_qc.cwl Branch/Commit ID: a23f42ef49c10a588fd35a3afaad5de03e253533
	wgs alignment and tumor-only variant detection	https://github.com/genome/analysis-workflows.git Path: definitions/pipelines/tumor_only_wgs.cwl Branch/Commit ID: 789267ce0e3fed674ea5212a562315218fcf1bfc
	sum-wf.cwl	https://github.com/common-workflow-language/common-workflow-language.git Path: v1.0/v1.0/sum-wf.cwl Branch/Commit ID: 4fd45edb9531a03223c18a586e32d0baf0d5acb2
	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: 3fc68366adb179927af5528c27b153abaf94494d
	GAT - Genomic Association Tester GAT: Genomic Association Tester ============================================== A common question in genomic analysis is whether two sets of genomic intervals overlap significantly. This question arises, for example, in the interpretation of ChIP-Seq or RNA-Seq data. The Genomic Association Tester (GAT) is a tool for computing the significance of overlap between multiple sets of genomic intervals. GAT estimates significance based on simulation. Gat implemements a sampling algorithm. Given a chromosome (workspace) and segments of interest, for example from a ChIP-Seq experiment, gat creates randomized version of the segments of interest falling into the workspace. These sampled segments are then compared to existing genomic annotations. The sampling method is conceptually simple. Randomized samples of the segments of interest are created in a two-step procedure. Firstly, a segment size is selected from to same size distribution as the original segments of interest. Secondly, a random position is assigned to the segment. The sampling stops when exactly the same number of nucleotides have been sampled. To improve the speed of sampling, segment overlap is not resolved until the very end of the sampling procedure. Conflicts are then resolved by randomly removing and re-sampling segments until a covering set has been achieved. Because the size of randomized segments is derived from the observed segment size distribution of the segments of interest, the actual segment sizes in the sampled segments are usually not exactly identical to the ones in the segments of interest. This is in contrast to a sampling method that permutes segment positions within the workspace.	https://github.com/datirium/workflows.git Path: workflows/gat-run.cwl Branch/Commit ID: 09267e79fd867aa68a219c69e6db7d8e2e877be2
	DESeq2 Multi-factor Analysis DESeq2 Multi-factor Analysis ============================ Runs DeSeq2 multi-factor analysis with manual control over major parameters	https://github.com/datirium/workflows.git Path: workflows/deseq-multi-factor.cwl Branch/Commit ID: ebbf23764ede324cabc064bd50647c1f643726fa