Explore Workflows
View already parsed workflows here or click here to add your own
| Graph | Name | Retrieved From | View |
|---|---|---|---|
|
|
tRNA_selection.cwl
|
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
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 |
|
|
|
cache_asnb_entries
|
https://github.com/ncbi/pgap.git
Path: task_types/tt_cache_asnb_entries.cwl Branch/Commit ID: b0ee40d34d233f1611c2e2c66b6d22a3b7deec05 |
|
|
|
Metagenomics workflow
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 |
|
|
|
amplicon_metrics.cwl
|
https://github.com/nci-gdc/gdc-dnaseq-cwl.git
Path: workflows/bamfastq_align/amplicon_metrics.cwl Branch/Commit ID: 8edf6a5e4e7790434ad0742e50d0c97a5d0bb846 |
|
|
|
Quality assessment, amplicon classification and functional prediction
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 |
|
|
|
group-isoforms-batch.cwl
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 |
|
|
|
xenbase-fastq-bowtie-bigwig-se-pe.cwl
|
https://github.com/Barski-lab/workflows.git
Path: subworkflows/xenbase-fastq-bowtie-bigwig-se-pe.cwl Branch/Commit ID: 877546bb89b793cc8830f8d803858706937a654b |
|
|
|
Illumina read quality control, trimming and contamination filter.
**Workflow for Illumina paired read quality control, trimming and filtering.**<br /> Multiple paired datasets will be merged into single paired dataset.<br /> Summary: - FastQC on raw data files<br /> - fastp for read quality trimming<br /> - BBduk for phiX and (optional) rRNA filtering<br /> - Kraken2 for taxonomic classification of reads (optional)<br /> - BBmap for (contamination) filtering using given references (optional)<br /> - FastQC on filtered (merged) data<br /> Other UNLOCK workflows on WorkflowHub: https://workflowhub.eu/projects/16/workflows?view=default<br><br> **All tool CWL files and other workflows can be found here:**<br> Tools: https://gitlab.com/m-unlock/cwl<br> Workflows: https://gitlab.com/m-unlock/cwl/workflows<br> **How to setup and use an UNLOCK workflow:**<br> https://m-unlock.gitlab.io/docs/setup/setup.html<br> |
https://gitlab.com/m-unlock/cwl.git
Path: cwl/workflows/workflow_illumina_quality.cwl Branch/Commit ID: 50aaa5a89d0cd01c80d55fb68dd72708d3796503 |
|
|
|
Nanopore Quality Control and Filtering
**Workflow for nanopore read quality control and contamination filtering.** - FastQC before filtering (read quality control) - Kraken2 taxonomic read classification - Minimap2 read filtering based on given references - FastQC after filtering (read quality control)<br><br> Other UNLOCK workflows on WorkflowHub: https://workflowhub.eu/projects/16/workflows?view=default<br><br> **All tool CWL files and other workflows can be found here:**<br> Tools: https://gitlab.com/m-unlock/cwl<br> Workflows: https://gitlab.com/m-unlock/cwl/workflows<br> **How to setup and use an UNLOCK workflow:**<br> https://m-unlock.gitlab.io/docs/setup/setup.html<br> |
https://gitlab.com/m-unlock/cwl.git
Path: cwl/workflows/workflow_nanopore_quality.cwl Branch/Commit ID: 50aaa5a89d0cd01c80d55fb68dd72708d3796503 |
