Explore Workflows

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

Path: tests/conflict-wf.cwl

Branch/Commit ID: 50251ef931d108c09bed2d330d3d4fe9c562b1c3

Packed ID: collision

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

Path: tests/count-lines12-wf.cwl

Branch/Commit ID: a0f2d38e37ff51721fdeaf993bb2ab474b17246b

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: 10ce6e113f749c7bd725e426445220c3bdc5ddf1

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

Path: task_types/tt_blastn_wnode.cwl

Branch/Commit ID: b0ee40d34d233f1611c2e2c66b6d22a3b7deec05

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

Path: progs/gp_makeblastdb.cwl

Branch/Commit ID: b38b0070edf910984f29a4a495b5dfa525b8b305

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

Path: cwltool/schemas/v1.0/v1.0/scatter-valuefrom-wf4.cwl

Branch/Commit ID: 26870e38cec81af880cd3e4789ae6cee8fc27020

Packed ID: main

Note: should be updated The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for a **single-end** experiment. A corresponded input [FASTQ](http://maq.sourceforge.net/fastq.shtml) file has to be provided. Current workflow should be used only with the single-end RNA-Seq data. It performs the following steps: 1. Trim adapters from input FASTQ file 2. Use STAR to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 3. Use fastx_quality_stats to analyze input FASTQ file and generate quality statistics file 4. Use samtools sort to generate coordinate sorted BAM(+BAI) file pair from the unsorted BAM file obtained on the step 1 (after running STAR) 5. Generate BigWig file on the base of sorted BAM file 6. Map input FASTQ file to predefined rRNA reference indices using Bowtie to define the level of rRNA contamination; export resulted statistics to file 7. Calculate isoform expression level for the sorted BAM file and GTF/TAB annotation file using GEEP reads-counting utility; export results to file

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

Path: workflows/trim-rnaseq-se-dutp.cwl

Branch/Commit ID: 8049a781ac4aae579fbd3036fa0bf654532f15be

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

Path: cwltool/schemas/v1.0/v1.0/scatter-valuefrom-wf3.cwl

Branch/Commit ID: 7bfd77118cdc80dd7150115dd7a1a7ee6046f6fe

Packed ID: main

https://github.com/yyoshiaki/VIRTUS2.git

Path: workflow/VIRTUS.PE.cwl

Branch/Commit ID: 96ccb37e04af37474771526cf0d85d3ded2005f7

https://github.com/yyoshiaki/VIRTUS2.git

Path: workflow/createindex.cwl

Branch/Commit ID: 96ccb37e04af37474771526cf0d85d3ded2005f7