Explore Workflows

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

Graph	Name	Retrieved From	View
	scatter-wf1_v1_2.cwl	https://github.com/common-workflow-language/cwl-utils.git Path: testdata/scatter-wf1_v1_2.cwl Branch/Commit ID: 0ab1d42d10f7311bb4032956c4a6f3d2730d9507
	Exome QC workflow	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/qc_exome.cwl Branch/Commit ID: e59c77629936fad069007ba642cad49fef7ad29f
	analysis for assembled sequences rna / protein - qc, annotation, index, abundance	https://github.com/MG-RAST/pipeline.git Path: CWL/Workflows/assembled.workflow.cwl Branch/Commit ID: 1844de830f6935901849ccd9966685fbf13e8042
	chipseq-gen-bigwig.cwl	https://github.com/datirium/workflows.git Path: subworkflows/chipseq-gen-bigwig.cwl Branch/Commit ID: 942f453603bc1df04cee28d6ac6b3b8b649fda55
	exome alignment with qc, no bqsr, no verify_bam_id	https://github.com/genome/analysis-workflows.git Path: definitions/pipelines/alignment_exome_mouse.cwl Branch/Commit ID: 8438316338e66823e1c9aca9f675b2bf33f2aa59
	umi duplex alignment workflow	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/duplex_alignment.cwl Branch/Commit ID: 9c0b1497c467393e1a54735575043dced73e95c4
	revsort.cwl Reverse the lines in a document, then sort those lines.	https://github.com/common-workflow-language/cwltool.git Path: tests/wf/revsort.cwl Branch/Commit ID: d6000d32f6c8fbd26421a2d30d79b28901d58fb0
	io-union-input-default-wf.cwl	https://github.com/common-workflow-language/cwl-v1.2.git Path: tests/io-union-input-default-wf.cwl Branch/Commit ID: ea9f8634e41824ac3f81c3dde698d5f0eef54f1b
	mut3.cwl	https://github.com/common-workflow-language/cwltool.git Path: tests/wf/mut3.cwl Branch/Commit ID: 8ef515037de411abd2f84b569ad4d4a4f7a2c7a0
	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: a68821bf3a9ceadc3b2ffbb535d601d9a645b377