Explore Workflows

https://github.com/ncbi/pgap.git

Path: task_types/tt_taxonomy_check_16S.cwl

Branch/Commit ID: 23f0ee7a36649ab37cabdd9277b7c82d098be79c

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: e99e80a2c19682d59947bde04a892d7b6d90091c

Workflow runs homer-make-tag-directory.cwl tool using scatter for the following inputs - bam_file - fragment_size - total_reads `dotproduct` is used as a `scatterMethod`, so one element will be taken from each array to construct each job: 1) bam_file[0] fragment_size[0] total_reads[0] 2) bam_file[1] fragment_size[1] total_reads[1] ... N) bam_file[N] fragment_size[N] total_reads[N] `bam_file`, `fragment_size` and `total_reads` arrays should have the identical order.

https://github.com/datirium/workflows.git

Path: subworkflows/heatmap-prepare.cwl

Branch/Commit ID: 7290eb220c90a087dffde04efae3678286aa92d0

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/env-wf1.cwl

Branch/Commit ID: a0f2d38e37ff51721fdeaf993bb2ab474b17246b

https://github.com/genome/analysis-workflows.git

Path: definitions/pipelines/panel_of_normals.cwl

Branch/Commit ID: ec5355f335852e51c6938809c16ea1d230a3f983

https://github.com/common-workflow-language/cwl-v1.1.git

Path: tests/pass-unconnected.cwl

Branch/Commit ID: b1d4a69df86350059bd49aa127c02be0c349f7de

Perform taxonomic identification tasks on an input genome

https://github.com/ncbi/pgap.git

Path: taxcheck.cwl

Branch/Commit ID: af78bfbc7625a817a2875e87c8ee267cf46b8c57

https://github.com/genome/analysis-workflows.git

Path: definitions/pipelines/tumor_only_detect_variants.cwl

Branch/Commit ID: 0805e8e0d358136468e0a9f49e06005e41965adc

Motif Finding with HOMER with target and background regions from peaks --------------------------------------------------- HOMER contains a novel motif discovery algorithm that was designed for regulatory element analysis in genomics applications (DNA only, no protein). It is a differential motif discovery algorithm, which means that it takes two sets of sequences and tries to identify the regulatory elements that are specifically enriched in on set relative to the other. It uses ZOOPS scoring (zero or one occurrence per sequence) coupled with the hypergeometric enrichment calculations (or binomial) to determine motif enrichment. HOMER also tries its best to account for sequenced bias in the dataset. It was designed with ChIP-Seq and promoter analysis in mind, but can be applied to pretty much any nucleic acids motif finding problem. For more information please refer to: ------------------------------------- [Official documentation](http://homer.ucsd.edu/homer/motif/)

https://github.com/datirium/workflows.git

Path: workflows/homer-motif-analysis-peak.cwl

Branch/Commit ID: e99e80a2c19682d59947bde04a892d7b6d90091c

https://github.com/ncbi/pgap.git

Path: task_types/tt_taxonomy_check_16S.cwl

Branch/Commit ID: 2c7879b47890b9300ab9b5ebd35e17372e077757