Explore Workflows

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

Path: task_types/tt_taxonomy_check_16S.cwl

Branch/Commit ID: 4def84df33963fc9ac9d5c5f804b911d01a0d9ad

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: 1131f82a53315cca217a6c84b3bd272aa62e4bca

Super-enhancers, consist of clusters of enhancers that are densely occupied by the master regulators and Mediator. Super-enhancers differ from typical enhancers in size, transcription factor density and content, ability to activate transcription, and sensitivity to perturbation. Use to create stitched enhancers, and to separate super-enhancers from typical enhancers using sequencing data (.bam) given a file of previously identified constituent enhancers (.gff)

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

Path: workflows/super-enhancer.cwl

Branch/Commit ID: a1f6ca50fcb0881781b3ba0306dd61ebf555eaba

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

Path: cwltool/schemas/v1.0/v1.0/schemadef-wf.cwl

Branch/Commit ID: 1e3f5404b7d5af02e3dec0faea31352111ad7cd8

Single-cell Multiome ATAC and RNA-Seq Aggregate Aggregates data from multiple Single-cell Multiome ATAC and RNA-Seq Alignment experiments

https://github.com/barski-lab/sc-seq-analysis.git

Path: workflows/sc-multiome-aggregate-wf.cwl

Branch/Commit ID: 8614e5d20f5e81dce537216bd340cdbc1067bbc7

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

Path: tests/count-lines7-wf.cwl

Branch/Commit ID: a5073143db4155e05df8d2e7eb59d9e62acd65a5

https://github.com/NAL-i5K/Organism_Onboarding.git

Path: flow_dispatch/workflow.cwl

Branch/Commit ID: add45db6f08de518e224bdc3c04094fd69cad2d2

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

Path: tests/step-valuefrom2-wf.cwl

Branch/Commit ID: a5073143db4155e05df8d2e7eb59d9e62acd65a5

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

Path: cwltool/schemas/v1.0/v1.0/env-wf1.cwl

Branch/Commit ID: 20d664eff23e59aa57908345bfdb1ceeab3438f2

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

Path: definitions/subworkflows/qc_wgs_mouse.cwl

Branch/Commit ID: f77a920bcc73f6cfdb091eed75a149d02cd8a263