Explore Workflows

View already parsed workflows here or click here to add your own

Graph	Name	Retrieved From	View
	Subworkflow that runs cnvkit in single sample mode and returns a vcf file	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/cnvkit_single_sample.cwl Branch/Commit ID: ecac0fda44df3a8f25ddfbb3e7a023fcbe4cbd0f
	adapter for sequence_align_and_tag Some workflow engines won't stage files in our nested structure, so parse it out here	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/sequence_align_and_tag_adapter.cwl Branch/Commit ID: 87faba2fff8007ecc95160729b1c7cd0376e46f2
	Detect Variants workflow	https://github.com/genome/analysis-workflows.git Path: definitions/pipelines/detect_variants_nonhuman.cwl Branch/Commit ID: 389f6edccab082d947bee9c032f59dbdf9f7c325
	tt_univec_wnode.cwl	https://github.com/ncbi/pgap.git Path: task_types/tt_univec_wnode.cwl Branch/Commit ID: cd97086739ae5988bab09b05e9259675c4b6bce6
	DiffBind - Differential Binding Analysis of ChIP-Seq Peak Data Differential Binding Analysis of ChIP-Seq Peak Data --------------------------------------------------- DiffBind processes ChIP-Seq data enriched for genomic loci where specific protein/DNA binding occurs, including peak sets identified by ChIP-Seq peak callers and aligned sequence read datasets. It is designed to work with multiple peak sets simultaneously, representing different ChIP experiments (antibodies, transcription factor and/or histone marks, experimental conditions, replicates) as well as managing the results of multiple peak callers. For more information please refer to: ------------------------------------- Ross-Innes CS, Stark R, Teschendorff AE, Holmes KA, Ali HR, Dunning MJ, Brown GD, Gojis O, Ellis IO, Green AR, Ali S, Chin S, Palmieri C, Caldas C, Carroll JS (2012). “Differential oestrogen receptor binding is associated with clinical outcome in breast cancer.” Nature, 481, -4.	https://github.com/datirium/workflows.git Path: workflows/diffbind.cwl Branch/Commit ID: 9850a859de1f42d3d252c50e15701928856fe774
	LSU-from-tablehits.cwl	https://github.com/proteinswebteam/ebi-metagenomics-cwl.git Path: tools/LSU-from-tablehits.cwl Branch/Commit ID: b6d3aaf3fa6695061208c6cdca3d7881cc45400d
	strelka workflow	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/strelka_and_post_processing.cwl Branch/Commit ID: 2e0562a5c3cd7aac24af4c622a5ae68a7fb23a71
	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: e0a30aa1ad516dd2ec0e9ce006428964b840daf4
	scatter GATK HaplotypeCaller over intervals	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/gatk_haplotypecaller_iterator.cwl Branch/Commit ID: 2e0562a5c3cd7aac24af4c622a5ae68a7fb23a71
	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: 9850a859de1f42d3d252c50e15701928856fe774