Explore Workflows
View already parsed workflows here or click here to add your own
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trim-chipseq-se.cwl
Runs ChIP-Seq BioWardrobe basic analysis with single-end data file. |
https://github.com/Barski-lab/workflows.git
Path: workflows/trim-chipseq-se.cwl Branch/Commit ID: 801f7b363e0599b9a28ecda696dfdb1c0e40ce71 |
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step-valuefrom2-wf.cwl
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https://github.com/common-workflow-language/cwltool.git
Path: cwltool/schemas/v1.0/v1.0/step-valuefrom2-wf.cwl Branch/Commit ID: 6003cbb94f16103241b562f2133e7c4acac6c621 |
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ValidateParameter
Run a set of validation methods for the given simulation model parameter(s). |
https://github.com/gammasim/workflows.git
Path: workflows/ValidateParameter.cwl Branch/Commit ID: 13a1a949db93afa18ffe8180ff9549e395184e4b |
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Trim Galore RNA-Seq pipeline paired-end strand specific
Modified 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-dutp.cwl Branch/Commit ID: 44214a9d02e6d85b03eb708552ed812ae3d4a733 |
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trim-chipseq-se.cwl
Runs ChIP-Seq BioWardrobe basic analysis with single-end data file. |
https://github.com/Barski-lab/workflows.git
Path: workflows/trim-chipseq-se.cwl Branch/Commit ID: e89b2c17aa5efccef6ca424dec5a0a021bd8d20c |
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PCA - Principal Component Analysis
Principal Component Analysis --------------- Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. The calculation is done by a singular value decomposition of the (centered and possibly scaled) data matrix, not by using eigen on the covariance matrix. This is generally the preferred method for numerical accuracy. |
https://github.com/datirium/workflows.git
Path: workflows/pca.cwl Branch/Commit ID: 5561f7ee11dd74848680351411a19aa87b13d27b |
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gathered exome alignment and somatic variant detection for cle purpose
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https://github.com/genome/analysis-workflows.git
Path: definitions/pipelines/somatic_exome_cle_gathered.cwl Branch/Commit ID: a59a803e1809a8fbfabca6b8962a8ad66dd01f1d |
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exome alignment and somatic variant detection for cle purpose
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https://github.com/apaul7/cancer-genomics-workflow.git
Path: definitions/pipelines/somatic_exome_cle.cwl Branch/Commit ID: bfcb5ffbea3d00a38cc03595d41e53ea976d599d |
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final_chunking.cwl
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https://github.com/EBI-Metagenomics/pipeline-v5.git
Path: workflows/subworkflows/final_chunking.cwl Branch/Commit ID: 6ec8d032feb120eb0eebf9a0c01d48deabf42eea |
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format_rrnas_from_seq_entry
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https://github.com/ncbi/pgap.git
Path: task_types/tt_format_rrnas_from_seq_entry.cwl Branch/Commit ID: 6d8d29a2156b93a75f1d1c6952738bd63f6bd98e |
