Explore Workflows

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

Path: definitions/pipelines/immuno.cwl

Branch/Commit ID: 5fda2d9eb52a363bd51011b3851c2afb86318c0c

https://git.astron.nl/RD/LINC.git

Path: workflows/HBA_calibrator.cwl

Branch/Commit ID: 7b6185e2e6f9d36b1987274e82842c82ba6f8342

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. Documents ============================================== - GSEA Home Page: https://www.gsea-msigdb.org/gsea/index.jsp - Results Interpretation: https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideTEXT.htm#_Interpreting_GSEA_Results - GSEA User Guide: https://gseapy.readthedocs.io/en/latest/faq.html - GSEAPY Docs: https://gseapy.readthedocs.io/en/latest/introduction.html References ============================================== - Subramanian, Tamayo, et al. (2005, PNAS), https://www.pnas.org/content/102/43/15545 - Mootha, Lindgren, et al. (2003, Nature Genetics), http://www.nature.com/ng/journal/v34/n3/abs/ng1180.html

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

Path: workflows/gseapy.cwl

Branch/Commit ID: d76110e0bfc40c874f82e37cef6451d74df4f908

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

Path: task_types/tt_cluster_and_qdump.cwl

Branch/Commit ID: d1fdfc7b5cc8d7ceab92c283ff0f86e90ae21390

Validate camera charge response.

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

Path: workflows/ValidateCameraChargeResponse.cwl

Branch/Commit ID: 789752af87eb190387ff2acb4c95c7a5cdb961e7

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

Path: definitions/subworkflows/pindel_region.cwl

Branch/Commit ID: 5fda2d9eb52a363bd51011b3851c2afb86318c0c

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

Path: definitions/pipelines/alignment_umi_molecular.cwl

Branch/Commit ID: 5fda2d9eb52a363bd51011b3851c2afb86318c0c

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

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

Path: tools/group-isoforms-batch.cwl

Branch/Commit ID: d76110e0bfc40c874f82e37cef6451d74df4f908

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

Path: definitions/subworkflows/vcf_eval_cle_gold.cwl

Branch/Commit ID: 5fda2d9eb52a363bd51011b3851c2afb86318c0c

Validate linearity of camera response including the transition from high-to-low gain channels for cameras with dual-gain readout.

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

Path: workflows/ValidateCameraLinearity.cwl

Branch/Commit ID: 789752af87eb190387ff2acb4c95c7a5cdb961e7