Explore Workflows

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

Path: tests/io-int-optional-wf.cwl

Branch/Commit ID: 707ebcd2173889604459c5f4ffb55173c508abb3

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

Path: task_types/tt_kmer_build_tree.cwl

Branch/Commit ID: 41d14ec5e2dfa0fac7eebeefda1f26ccea14c9a0

accept a simulation model parameter (or set of parameters) as validated and to be used in future MC productions.

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

Path: workflows/AccceptParameter.cwl

Branch/Commit ID: 789752af87eb190387ff2acb4c95c7a5cdb961e7

GSEAPY: Gene Set Enrichment Analysis in Python ============================================== Gene Set Enrichment Analysis is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states (e.g. phenotypes). GSEA requires as input an expression dataset, which contains expression profiles for multiple samples. While the software supports multiple input file formats for these datasets, the tab-delimited GCT format is the most common. The first column of the GCT file contains feature identifiers (gene ids or symbols in the case of data derived from RNA-Seq experiments). The second column contains a description of the feature; this column is ignored by GSEA and may be filled with “NA”s. Subsequent columns contain the expression values for each feature, with one sample's expression value per column. It is important to note that there are no hard and fast rules regarding how a GCT file's expression values are derived. The important point is that they are comparable to one another across features within a sample and comparable to one another across samples. Tools such as DESeq2 can be made to produce properly normalized data (normalized counts) which are compatible with GSEA.

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

Path: workflows/gseapy.cwl

Branch/Commit ID: a1f6ca50fcb0881781b3ba0306dd61ebf555eaba

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

Path: tests/count-lines16-wf.cwl

Branch/Commit ID: a5073143db4155e05df8d2e7eb59d9e62acd65a5

https://github.com/common-workflow-language/cwltool.git

Path: cwltool/schemas/v1.0/v1.0/count-lines11-wf.cwl

Branch/Commit ID: 6d2998467fada81e5024c1f8594ae167514cb290

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

Path: tests/record-in-secondaryFiles-missing-wf.cwl

Branch/Commit ID: 707ebcd2173889604459c5f4ffb55173c508abb3

Workflow for running the facets suite workflow on a single tumor normal pair Includes handling of errors in case execution fails for the sample pair

https://github.com/mskcc/pluto-cwl.git

Path: cwl/facets-suite-workflow.cwl

Branch/Commit ID: 342e6f1f4f7a3839e579fbe96ccc8d6f7a61ac77

Generates a FASTA file with the DNA sequences for all transcripts in a GFF file and builds kallisto index

https://github.com/Barski-lab/workflows.git

Path: tools/genome-kallisto-index.cwl

Branch/Commit ID: cf678db8304ffaa20c1d6c854364db5ed41803c2

Workflow runs group-isoforms.cwl tool using scatter for isoforms_file input. genes_filename and common_tss_filename inputs are ignored.

https://github.com/Barski-lab/workflows.git

Path: tools/group-isoforms-batch.cwl

Branch/Commit ID: dc4ee45ed2c5c30e9a1a173c9ea4445f27d3788a