Explore Workflows
View already parsed workflows here or click here to add your own
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Cell Ranger Build Reference Indices
Cell Ranger Build Reference Indices =================================== |
https://github.com/datirium/workflows.git
Path: workflows/cellranger-mkref.cwl Branch/Commit ID: 60854b5d299df91e135e05d02f4be61f6a310fbc |
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running cellranger mkfastq and count
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https://github.com/genome/analysis-workflows.git
Path: definitions/subworkflows/cellranger_mkfastq_and_count.cwl Branch/Commit ID: 6f9f8a2057c6a9f221a44559f671e87a75c70075 |
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Filter single sample sv vcf from paired read callers(Manta/Smoove)
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https://github.com/genome/analysis-workflows.git
Path: definitions/subworkflows/sv_paired_read_caller_filter.cwl Branch/Commit ID: da335d9963418f7bedd84cb2791a0df1b3165ffe |
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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: cbefc215d8286447620664fb47076ba5d81aa47f |
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cnv_exomedepth
CNV ExomeDepth calling |
https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git
Path: structuralvariants/cwl/abstract_operations/subworkflows/cnv_exome_depth.cwl Branch/Commit ID: de9cb009f8fe0c8d5a94db5c882cf21ddf372452 |
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merge-bam-parallel
This workflow merge BAM files per condition in parallel |
https://github.com/ncbi/cwl-ngs-workflows-cbb.git
Path: workflows/File-formats/merge-bam-parallel.cwl Branch/Commit ID: 527251ebb77750d02dcc9a370d978a153fc9328f |
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process VCF workflow
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https://github.com/genome/analysis-workflows.git
Path: definitions/subworkflows/strelka_process_vcf.cwl Branch/Commit ID: a59a803e1809a8fbfabca6b8962a8ad66dd01f1d |
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Unaligned BAM to BQSR and VCF
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https://github.com/genome/analysis-workflows.git
Path: definitions/subworkflows/bam_to_bqsr_no_dup_marking.cwl Branch/Commit ID: a59a803e1809a8fbfabca6b8962a8ad66dd01f1d |
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bam to trimmed fastqs and HISAT alignments
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https://github.com/genome/analysis-workflows.git
Path: definitions/subworkflows/bam_to_trimmed_fastq_and_hisat_alignments.cwl Branch/Commit ID: a59a803e1809a8fbfabca6b8962a8ad66dd01f1d |
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bam to trimmed fastqs
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https://github.com/genome/analysis-workflows.git
Path: definitions/subworkflows/bam_to_trimmed_fastq.cwl Branch/Commit ID: a59a803e1809a8fbfabca6b8962a8ad66dd01f1d |
