Explore Workflows

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

Graph	Name	Retrieved From	View
	search.cwl#main	https://github.com/common-workflow-language/cwltool.git Path: cwltool/schemas/v1.0/v1.0/search.cwl Branch/Commit ID: c6cced7a2e6389d2eb43342e702677ccb7c7497c Packed ID: main
	gathered exome alignment and somatic variant detection for cle purpose	https://github.com/genome/analysis-workflows.git Path: definitions/pipelines/somatic_exome_cle_gathered.cwl Branch/Commit ID: 8dc462a7d9ba1479f764682af99c69d8574cb3dc
	wffail.cwl	https://github.com/common-workflow-language/cwltool.git Path: tests/wf/wffail.cwl Branch/Commit ID: a3d565bf8e630101d25d31804cfbceb0a0ba28de
	alignment for nonhuman with qc	https://github.com/genome/analysis-workflows.git Path: definitions/pipelines/alignment_wgs_nonhuman.cwl Branch/Commit ID: 97572e3a088d79f6a4166385f79e79ea77b11470
	Hello World Outputs a message using echo	https://github.com/common-workflow-language/cwltool.git Path: tests/wf/hello-workflow.cwl Branch/Commit ID: d6000d32f6c8fbd26421a2d30d79b28901d58fb0
	ROSE: rank ordering of super-enhancers Super-enhancers, consist of clusters of enhancers that are densely occupied by the master regulators and Mediator. Super-enhancers differ from typical enhancers in size, transcription factor density and content, ability to activate transcription, and sensitivity to perturbation. Use to create stitched enhancers, and to separate super-enhancers from typical enhancers using sequencing data (.bam) given a file of previously identified constituent enhancers (.gff)	https://github.com/datirium/workflows.git Path: workflows/super-enhancer.cwl Branch/Commit ID: e99e80a2c19682d59947bde04a892d7b6d90091c
	directory.cwl Inspect provided directory and return filenames. Generate a new directory and return it (including content).	https://github.com/common-workflow-language/cwltool.git Path: tests/wf/directory.cwl Branch/Commit ID: a3d565bf8e630101d25d31804cfbceb0a0ba28de
	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: ce058d892d330125cd03d0a0d5fb3b321cda0be3
	scatter-valuefrom-wf2.cwl	https://github.com/common-workflow-language/cwltool.git Path: cwltool/schemas/v1.0/v1.0/scatter-valuefrom-wf2.cwl Branch/Commit ID: 7c7615c44b80f8e76e659433f8c7875603ae0b25
	env-wf1.cwl	https://github.com/common-workflow-language/cwl-v1.2.git Path: tests/env-wf1.cwl Branch/Commit ID: ea9f8634e41824ac3f81c3dde698d5f0eef54f1b