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Graph Name Retrieved From View
workflow graph bact_get_kmer_reference

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

Path: task_types/tt_bact_get_kmer_reference.cwl

Branch/Commit ID: 5463361069e263ad6455858e054c1337b1d9e752
workflow graph Running cellranger count and lineage inference

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

Path: definitions/subworkflows/single_cell_rnaseq.cwl

Branch/Commit ID: 742dbafb5fb103d8578f48a0576c14dd8dae3b2a
workflow graph GSEApy - Gene Set Enrichment Analysis in Python

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: 2f0db4b3c515f91c5cfda19c78cf90d339390986
workflow graph count-lines8-wf.cwl

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

Path: tests/count-lines8-wf.cwl

Branch/Commit ID: 368b562a1449e8cd39ae8b7f05926b2bfb9b22df
workflow graph import_schema-def.cwl

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

Path: v1.0/v1.0/import_schema-def.cwl

Branch/Commit ID: e67f19d8a713759d761ecad050966d1eb043b85c
workflow graph steplevel-resreq.cwl

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

Path: cwltool/schemas/v1.0/v1.0/steplevel-resreq.cwl

Branch/Commit ID: 9a8e654a91ea5d26e8452dd1cecf3faf22b7a12e
workflow graph Identifies non-coding RNAs using Rfams covariance models

https://github.com/stain/workflow-is-cwl.git

Path: workflows/cmsearch-multimodel-wf.cwl

Branch/Commit ID: b1e88a8c2f6f07d236193d3e89dc2d724700780a
workflow graph Apply filters to VCF file

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

Path: definitions/subworkflows/filter_vcf_mouse.cwl

Branch/Commit ID: 9e5f228bc1a3d0dfe950b5d41d7e4319e834a6d4
workflow graph Downsample and HaplotypeCaller

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

Path: definitions/pipelines/downsample_and_recall.cwl

Branch/Commit ID: ecac0fda44df3a8f25ddfbb3e7a023fcbe4cbd0f
workflow graph umi per-lane alignment subworkflow

https://github.com/fgomez02/analysis-workflows.git

Path: definitions/subworkflows/umi_alignment.cwl

Branch/Commit ID: 9c9e6a6a48eb321804ce772a2c2c12b4f2f32529