Explore Workflows

https://github.com/sentieon/sentieon-cwl.git

Path: stage/bqsr-distr.cwl

Branch/Commit ID: d20382adfe7285cb517a25d95d2bcb7586546e23

Workflow for genome annotation from EMBL format

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

Path: cwl/workflows/workflow_sapp_microbes.cwl

Branch/Commit ID: master

GATK4.1.2 Calculate tumor-normal contamination workflow

https://github.com/nci-gdc/gatk4_mutect2_cwl.git

Path: subworkflows/calculate_contamination_workflow.cwl

Branch/Commit ID: 138d484362084dfc97d9fb7d839855b4bc2c5599

Workflow to build different indices for different tools from a genome and transcriptome. This workflow expects an (annotated) genome in GBOL ttl format. Steps: - SAPP: rdf2gtf (genome fasta) - SAPP: rdf2fasta (transcripts fasta) - STAR index (Optional for Eukaryotic origin) - bowtie2 index - kallisto index

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

Path: cwl/workflows/workflow_indexbuilder.cwl

Branch/Commit ID: master

https://github.com/aplbrain/saber.git

Path: saber/i2g/examples/I2G_Seg_Workflow/workflowSegment.cwl

Branch/Commit ID: 051b9506fd7356113be013ac3c435a101fd95123

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

Path: task_types/tt_extract_gencoll_ids.cwl

Branch/Commit ID: 69c0f25d08cfa02d8bfaa85ce5d70dd14cc52e3f

Workflow for Spliced RNAseq data Steps: - workflow_illumina_quality: - FastQC (Read Quality Control) - fastp (Read Trimming) - STAR (Read mapping) - featurecounts (transcript read counts) - kallisto (transcript [pseudo]counts)

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

Path: cwl/workflows/workflow_RNAseq_Spliced.cwl

Branch/Commit ID: master

https://github.com/mskcc/ACCESS-Pipeline.git

Path: workflows/ACCESS_pipeline.cwl

Branch/Commit ID: b0f226a9ac5152f3afe0d38c8cd54aa25b8b01cf

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. Documents ============================================== - GSEA Home Page: https://www.gsea-msigdb.org/gsea/index.jsp - Results Interpretation: https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideTEXT.htm#_Interpreting_GSEA_Results - GSEA User Guide: https://gseapy.readthedocs.io/en/latest/faq.html - GSEAPY Docs: https://gseapy.readthedocs.io/en/latest/introduction.html References ============================================== - Subramanian, Tamayo, et al. (2005, PNAS), https://www.pnas.org/content/102/43/15545 - Mootha, Lindgren, et al. (2003, Nature Genetics), http://www.nature.com/ng/journal/v34/n3/abs/ng1180.html

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

Path: workflows/gseapy.cwl

Branch/Commit ID: 23f48abfae31592d202cbc31394f6d5167d22014

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

Path: task_types/tt_univec_wnode.cwl

Branch/Commit ID: c7c674b873b9925b28ffbd602974eec4bfe78cf9