Explore Workflows

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

Graph	Name	Retrieved From	View
	gather AML trio outputs	https://github.com/genome/analysis-workflows.git Path: definitions/pipelines/aml_trio_cle_gathered.cwl Branch/Commit ID: 742dbafb5fb103d8578f48a0576c14dd8dae3b2a
	exome alignment and germline variant detection	https://github.com/genome/cancer-genomics-workflow.git Path: detect_variants/germline_detect_variants.cwl Branch/Commit ID: eb565eac07209017b12ed79057b40cbf44fb6a0d
	CLE gold vcf evaluation workflow	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/vcf_eval_cle_gold.cwl Branch/Commit ID: 35e6b3ef71b4a2a9caba1dbd5dc424a8809bcc0a
	kmer_cache_retrieve	https://github.com/ncbi/pgap.git Path: task_types/tt_kmer_cache_retrieve.cwl Branch/Commit ID: b12ec8c8e832151033b9e6c0a76a3c3df18d45da
	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: 1f03ff02ef829bdb9d582825bcd4ca239e84ca2e
	exome alignment and germline variant detection, with optitype for HLA typing	https://github.com/genome/analysis-workflows.git Path: definitions/pipelines/germline_exome_hla_typing.cwl Branch/Commit ID: 844c10a4466ab39c02e5bfa7a210c195b8efa77a
	umi duplex alignment workflow	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/duplex_alignment.cwl Branch/Commit ID: 389f6edccab082d947bee9c032f59dbdf9f7c325
	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: 17a4a68b20e0af656e09714c1f39fe761b518686
	scatter-wf3.cwl#main	https://github.com/common-workflow-language/common-workflow-language.git Path: v1.0/v1.0/scatter-wf3.cwl Branch/Commit ID: e0cc5bd1c2fc4625f2cb5a819d3c1939aa8460db Packed ID: main
	umi duplex alignment workflow	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/duplex_alignment.cwl Branch/Commit ID: 35e6b3ef71b4a2a9caba1dbd5dc424a8809bcc0a