Explore Workflows

Single-cell RNA-Seq Alignment Runs Cell Ranger Count to quantify gene expression from a single-cell RNA-Seq library.

https://github.com/Barski-lab/sc-seq-analysis.git

Path: workflows/sc-rna-align-wf.cwl

Branch/Commit ID: e70b7fab45e4bd2abfb7dab2b8b1f79ce904ac69

Workflow runs [Bowtie](http://bowtie-bio.sourceforge.net/tutorial.shtml) v1.2.0 (12/30/2016) to build indices for reference genome provided in a single FASTA file as fasta_file input. Generated indices are saved in a folder with the name that corresponds to the input genome

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

Path: workflows/bowtie-index.cwl

Branch/Commit ID: 7fb8a1ebf8145791440bc2fed9c5f2d78a19d04c

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

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

Branch/Commit ID: e59538cd9899a88d7e31e0f259bc56734f604383

Packed ID: main

https://github.com/EBI-Metagenomics/ebi-metagenomics-cwl.git

Path: tools/tRNA_selection.cwl

Branch/Commit ID: 43d2fb8a5430dc56b55e84e3986d0079cad8d185

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: 7fb8a1ebf8145791440bc2fed9c5f2d78a19d04c

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

Path: task_types/tt_cache_asnb_entries.cwl

Branch/Commit ID: b0ee40d34d233f1611c2e2c66b6d22a3b7deec05

Workflow pilon assembly polishing Steps: - BBmap (Read mapping to assembly) - Pilon

https://gitlab.com/m-unlock/cwl.git

Path: cwl/workflows/workflow_pilon_mapping.cwl

Branch/Commit ID: 50aaa5a89d0cd01c80d55fb68dd72708d3796503

https://github.com/nci-gdc/gdc-dnaseq-cwl.git

Path: workflows/bamfastq_align/amplicon_metrics.cwl

Branch/Commit ID: 8edf6a5e4e7790434ad0742e50d0c97a5d0bb846

Workflow for quality assessment of paired reads and classification using NGTax 2.0 and functional annotation using picrust2. In addition files are exported to their respective subfolders for easier data management in a later stage. Steps: - FastQC (read quality control) - NGTax 2.0 - Picrust 2 - Export module for ngtax

https://git.wur.nl/unlock/cwl.git

Path: cwl/workflows/workflow_ngtax_picrust2.cwl

Branch/Commit ID: 60fafdfbec9b39c860945ef4634e0c28cb5e976c

Workflow runs group-isoforms.cwl tool using scatter for isoforms_file input. genes_filename and common_tss_filename inputs are ignored.

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

Path: subworkflows/group-isoforms-batch.cwl

Branch/Commit ID: 7518b100d8cbc80c8be32e9e939dfbb27d6b4361