Explore Workflows

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

Path: tests/search.cwl

Branch/Commit ID: b1d4a69df86350059bd49aa127c02be0c349f7de

Packed ID: main

The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for a **pair-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 files 2. Use STAR to align reads from input FASTQ files according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 3. Use fastx_quality_stats to analyze input FASTQ files and generate quality statistics files 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 files 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-pe.cwl

Branch/Commit ID: 4f48ee6f8665a34cdf96e89c012ee807f80c7a3d

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

Path: tests/inp_update_wf.cwl

Branch/Commit ID: b1d4a69df86350059bd49aa127c02be0c349f7de

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

Path: definitions/subworkflows/bisulfite_qc.cwl

Branch/Commit ID: a23f42ef49c10a588fd35a3afaad5de03e253533

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

Path: definitions/pipelines/bisulfite.cwl

Branch/Commit ID: a23f42ef49c10a588fd35a3afaad5de03e253533

https://github.com/EBI-Metagenomics/workflow-is-cwl.git

Path: workflows/TranscriptsAnnotation-i5only-wf.cwl

Branch/Commit ID: 72f702591368397f56d455128f60916902104dd2

https://github.com/NAL-i5K/Organism_Onboarding.git

Path: gaps_or_not.cwl

Branch/Commit ID: 5910b4d88aca172252d9102ddb610a7dc9e1347f

https://github.com/hubmapconsortium/salmon-rnaseq.git

Path: bulk-pipeline.cwl

Branch/Commit ID: a5779cc2804edf3052e597ea32f1f1c49aa33afb

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

Path: task_types/tt_kmer_compare_wnode.cwl

Branch/Commit ID: f5c11df465aaadf712c38ba4933679fe1cbe03ca

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: e99e80a2c19682d59947bde04a892d7b6d90091c