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Graph Name Retrieved From View
workflow graph PerformanceSummaryGenome_v0_1_0.cwl

https://github.com/PMCC-BioinformaticsCore/janis-pipelines.git

Path: janis_pipelines/wgs_somatic/cwl/tools/PerformanceSummaryGenome_v0_1_0.cwl

Branch/Commit ID: d919f2dd335da64a4fa352df9ea1b27ba13edad8
workflow graph exome alignment and germline variant detection

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

Path: definitions/pipelines/germline_exome_gvcf.cwl

Branch/Commit ID: 8da2b1cd6fa379b2c22baf9dad762d39630e6f46
workflow graph Unaligned BAM to BQSR and VCF

https://github.com/genome/cancer-genomics-workflow.git

Path: unaligned_bam_to_bqsr/workflow_no_dup_marking.cwl

Branch/Commit ID: d1ee6a2a323cee7e4af00c7e0b926c2192038e1d
workflow graph PerformanceSummaryGenome_v0_1_0.cwl

https://github.com/PMCC-BioinformaticsCore/janis-pipelines.git

Path: janis_pipelines/wgs_somatic/cwl/tools/PerformanceSummaryGenome_v0_1_0.cwl

Branch/Commit ID: a3d1f3b870eb834fed14ebcc928ea6a2270441a8
workflow graph align_sort_sa

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

Path: task_types/tt_align_sort_sa.cwl

Branch/Commit ID: 4ea5956bb97ea2eb6de124bc9b6a6a81a14fd2e7
workflow graph format_rrnas_from_seq_entry

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

Path: task_types/tt_format_rrnas_from_seq_entry.cwl

Branch/Commit ID: 4ea5956bb97ea2eb6de124bc9b6a6a81a14fd2e7
workflow graph tt_kmer_compare_wnode

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

Path: task_types/tt_kmer_compare_wnode.cwl

Branch/Commit ID: 4ea5956bb97ea2eb6de124bc9b6a6a81a14fd2e7
workflow graph allele-process-strain.cwl

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

Path: subworkflows/allele-process-strain.cwl

Branch/Commit ID: e238d1756f1db35571e84d72e1699e5d1540f10c
workflow graph tt_blastn_wnode

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

Path: task_types/tt_blastn_wnode.cwl

Branch/Commit ID: 2801ce53744a085580a8de91cd007c45146b51e8
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. 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: 27bee2c853c98af5ce8ace0585b74658adc2e955