Explore Workflows

Inspect provided directory and return filenames. Generate a new directory and return it (including content).

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

Path: tests/wf/directory.cwl

Branch/Commit ID: 875b928ce50a3202f5954843b79ea86683c160fa

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

Path: task_types/tt_kmer_ref_compare_wnode.cwl

Branch/Commit ID: 50d161364e2859ed5c95ef07c9f7234f1431cf31

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

Path: definitions/subworkflows/qc_wgs_nonhuman.cwl

Branch/Commit ID: 2f65fc96207a71b1cda4e246f808bed056608cd0

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

Path: cwltool/schemas/v1.0/v1.0/count-lines12-wf.cwl

Branch/Commit ID: 09323506da219ba3ddb5313bd83022b52cac9adc

Workflow converts input BAM file into bigWig and bedGraph files. Input BAM file should be sorted by coordinates (required by `bam_to_bedgraph` step). If `split` input is not provided use true by default. Default logic is implemented in `valueFrom` field of `split` input inside `bam_to_bedgraph` step to avoid possible bug in cwltool with setting default values for workflow inputs. `scale` has higher priority over the `mapped_reads_number`. The last one is used to calculate `-scale` parameter for `bedtools genomecov` (step `bam_to_bedgraph`) only in a case when input `scale` is not provided. All logic is implemented inside `bedtools-genomecov.cwl`. `bigwig_filename` defines the output name only for generated bigWig file. `bedgraph_filename` defines the output name for generated bedGraph file and can influence on generated bigWig filename in case when `bigwig_filename` is not provided. All workflow inputs and outputs don't have `format` field to avoid format incompatibility errors when workflow is used as subworkflow.

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

Path: tools/bam-bedgraph-bigwig.cwl

Branch/Commit ID: c6bfa0de917efb536dd385624fc7702e6748e61d

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

Path: tests/wf/sec-wf-out.cwl

Branch/Commit ID: 875b928ce50a3202f5954843b79ea86683c160fa

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

Path: tests/io-int-default-wf.cwl

Branch/Commit ID: a5073143db4155e05df8d2e7eb59d9e62acd65a5

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

Path: task_types/tt_taxonomy_check_16S.cwl

Branch/Commit ID: 807fe40bca1fbd18ede6250851b9f71de98da69b

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

Path: protein_alignment/wf_compart_filter_prosplign.cwl

Branch/Commit ID: c17cac4c046f8ba2b8574a121c44a72d2e6b27e6

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: c6bfa0de917efb536dd385624fc7702e6748e61d