Explore Workflows
View already parsed workflows here or click here to add your own
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sum-wf.cwl
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Path: cwltool/schemas/v1.0/v1.0/sum-wf.cwl Branch/Commit ID: e9c83739a93fa0b18f8dea2f98b632a9e32725c9 |
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count-lines5-wf.cwl
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Path: cwltool/schemas/v1.0/v1.0/count-lines5-wf.cwl Branch/Commit ID: 7c7615c44b80f8e76e659433f8c7875603ae0b25 |
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kmer_build_tree
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Path: task_types/tt_kmer_build_tree.cwl Branch/Commit ID: 6d04f5d65d1d4893706d9ae7e27341633333054f |
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tt_fscr_calls_pass1
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Path: task_types/tt_fscr_calls_pass1.cwl Branch/Commit ID: 55b6ee46b0c9fb1c9949cd0888b388c6f11b73b1 |
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vector_cleanup
This workflow detect and remove vectors from a DNA fasta file |
Path: workflows/Contamination/vector-cleanup.cwl Branch/Commit ID: 3247592a89deafaa0d9c5910a1cb1d000ef9b098 |
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cond-wf-012_nojs.cwl
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Path: tests/conditionals/cond-wf-012_nojs.cwl Branch/Commit ID: e62f99dd79d6cb9c157cceb458f74200da84f6e9 |
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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. |
Path: workflows/pca.cwl Branch/Commit ID: 675a3ff982091faf304931e9261aacdbabf51702 |
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dynresreq-workflow-tooldefault.cwl
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Path: tests/dynresreq-workflow-tooldefault.cwl Branch/Commit ID: 31ec48a8d81ef7c1b2c5e9c0a19e7623efe4a1e2 |
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steplevel-resreq.cwl
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Path: cwltool/schemas/v1.0/v1.0/steplevel-resreq.cwl Branch/Commit ID: b82ce7ae901a54c7a062fd5eefd8d5ceb5a4d684 |
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main.cwl
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Path: offline/streamflow/cwl/main.cwl Branch/Commit ID: e2c8ee3c187cb951066909296ead46b784cd2dee |
