Explore Workflows
View already parsed workflows here or click here to add your own
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alignment workflow
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Path: assets/CWL/workflow.cwl Branch/Commit ID: ac15c607dbc7c9da0bd3bf70eaa44ba357bf6a30 |
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linc_target.cwl
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Path: workflows/linc_target.cwl Branch/Commit ID: 58b342e498d9462d57c04df3820d51a14040d550 |
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directory.cwl
Inspect provided directory and return filenames. Generate a new directory and return it (including content). |
Path: tests/wf/directory.cwl Branch/Commit ID: f1d192dd2b28902fd0098e133c2ef241557d27a8 |
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Single-Cell Manual Cell Type Assignment
Single-Cell Manual Cell Type Assignment Assigns identities to cells clustered with any of the “Single-Cell Cluster Analysis” pipelines. For “Single-Cell RNA-Seq Cluster Analysis” the results of this workflow are used in the “Single-Cell RNA-Seq Differential Expression Analysis”, “Single-Cell RNA-Seq Trajectory Analysis”, and — when combined with outputs from the “Cell Ranger Count (RNA+VDJ)” or “Cell Ranger Aggregate (RNA, RNA+VDJ)” workflow — in the “Single-Cell Immune Profiling Analysis” pipeline. For “Single-Cell ATAC-Seq Cluster Analysis”, the results of this workflow are used in the “Single-Cell ATAC-Seq Differential Accessibility Analysis” and “Single-Cell ATAC-Seq Genome Coverage” pipelines. For “Single-Cell WNN Cluster Analysis”, the results of this workflow are used in all of the above, except the “Single-Cell Immune Profiling Analysis” pipeline. |
Path: workflows/sc-ctype-assign.cwl Branch/Commit ID: 57863b6131d8262c5ce864adaf8e4038401e71a2 |
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kf-cram2gvcf_calc_contam.cwl
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Path: workflows/kf-cram2gvcf_calc_contam.cwl Branch/Commit ID: 55315b6abb488f1f25fe725407814e8d4c23ba81 |
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count-lines10-wf.cwl
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Path: cwltool/schemas/v1.0/v1.0/count-lines10-wf.cwl Branch/Commit ID: 0e98de8f692bb7b9626ed44af835051750ac20cd |
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qc-assembled.workflow.cwl
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Path: CWL/Workflows/qc-assembled.workflow.cwl Branch/Commit ID: 6a8727124baf77416ca797982fd4e0689c2a593a |
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chipseq-se.cwl
Runs ChIP-Seq BioWardrobe basic analysis with single-end data file. |
Path: workflows/chipseq-se.cwl Branch/Commit ID: 749460273e6c8d9e8b7d2395f0ac157701d3495e |
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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: 57863b6131d8262c5ce864adaf8e4038401e71a2 |
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foldseek easy-search sub-workflow
retrieve sequence from blastdbcmd result makeblastdb: ../Tools/14_makeblastdb.cwl blastdbcmd: ../Tools/15_blastdbcmd.cwl seqretsplit: ../Tools/16_seqretsplit.cwl needle (Global alignment): ../Tools/17_needle.cwl water (Local alignment): ../Tools/17_water.cwl |
Path: Workflow/11_retrieve_sequence_wf.cwl Branch/Commit ID: 00841b0975d16883f8d01c98315c48a9d1dc1dbe |
