Explore Workflows
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simple_two_step.cwl
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![]() Path: blast-pipelines/simple_two_step.cwl Branch/Commit ID: 496b3e2bff8c20d5a7d5c3cbe9b64697767b1c13 |
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Genomic regions intersection and visualization
Genomic regions intersection and visualization ============================================== 1. Merges intervals within each of the filtered peaks files from ChIP/ATAC experiments 2. Overlaps merged intervals and assigns the nearest genes to them |
![]() Path: workflows/intervene.cwl Branch/Commit ID: 4a5c59829ff8b9f3c843e66e3c675dcd9c689ed5 |
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gather AML trio outputs
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![]() Path: definitions/pipelines/aml_trio_cle_gathered.cwl Branch/Commit ID: 742dbafb5fb103d8578f48a0576c14dd8dae3b2a |
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exome alignment and germline variant detection
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![]() Path: detect_variants/germline_detect_variants.cwl Branch/Commit ID: eb565eac07209017b12ed79057b40cbf44fb6a0d |
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CLE gold vcf evaluation workflow
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![]() Path: definitions/subworkflows/vcf_eval_cle_gold.cwl Branch/Commit ID: 35e6b3ef71b4a2a9caba1dbd5dc424a8809bcc0a |
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kmer_cache_retrieve
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![]() Path: task_types/tt_kmer_cache_retrieve.cwl Branch/Commit ID: b12ec8c8e832151033b9e6c0a76a3c3df18d45da |
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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: 1f03ff02ef829bdb9d582825bcd4ca239e84ca2e |
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exome alignment and germline variant detection, with optitype for HLA typing
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![]() Path: definitions/pipelines/germline_exome_hla_typing.cwl Branch/Commit ID: 844c10a4466ab39c02e5bfa7a210c195b8efa77a |
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umi duplex alignment workflow
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![]() Path: definitions/subworkflows/duplex_alignment.cwl Branch/Commit ID: 389f6edccab082d947bee9c032f59dbdf9f7c325 |
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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: 17a4a68b20e0af656e09714c1f39fe761b518686 |