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Graph Name Retrieved From View
workflow graph somatic_exome: exome alignment and somatic variant detection

somatic_exome is designed to perform processing of mutant/wildtype H.sapiens exome sequencing data. It features BQSR corrected alignments, 4 caller variant detection, and vep style annotations. Structural variants are detected via manta and cnvkit. In addition QC metrics are run, including somalier concordance metrics. example input file = analysis_workflows/example_data/somatic_exome.yaml

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

Path: definitions/pipelines/somatic_exome.cwl

Branch/Commit ID: 788bdc99c1d5b6ee7c431c3c011eb30d385c1370
workflow graph taxcheck.cwl

Perform taxonomic identification tasks on an input genome

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

Path: taxcheck.cwl

Branch/Commit ID: 505b91e41741ccbcd5ebd2b6a09a3be604f9ece3
workflow graph tt_kmer_compare_wnode

Pairwise comparison

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

Path: task_types/tt_kmer_compare_wnode.cwl

Branch/Commit ID: 8fb4ac7f5a66897206c7469101a471108b06eada
workflow graph kmer_ref_compare_wnode

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

Path: task_types/tt_kmer_ref_compare_wnode.cwl

Branch/Commit ID: 8fb4ac7f5a66897206c7469101a471108b06eada
workflow graph gcaccess_from_list

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

Path: task_types/tt_gcaccess_from_list.cwl

Branch/Commit ID: 8fb4ac7f5a66897206c7469101a471108b06eada
workflow graph downsample unaligned BAM and align

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

Path: definitions/subworkflows/downsampled_alignment.cwl

Branch/Commit ID: 479c9b3e3fa32ec9c7cd4073cfbccc675fd254d9
workflow graph CNV_pipeline

https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git

Path: structuralvariants/cwl/workflow.cwl

Branch/Commit ID: 9ac2d150a57d1996210ed6a44dd0c0404dab383c
workflow graph bacterial_screening.cwl

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

Path: vecscreen/bacterial_screening.cwl

Branch/Commit ID: 505b91e41741ccbcd5ebd2b6a09a3be604f9ece3
workflow graph Detect Docm variants

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

Path: definitions/subworkflows/docm_cle.cwl

Branch/Commit ID: a59a803e1809a8fbfabca6b8962a8ad66dd01f1d
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: 4a5c59829ff8b9f3c843e66e3c675dcd9c689ed5