Explore Workflows
View already parsed workflows here or click here to add your own
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FASTQ Vector Removal
This workflow convert fastq to multiple fasta files |
https://github.com/ncbi/cwl-ngs-workflows-cbb.git
Path: workflows/File-formats/fastq-to-splitted-fasta.cwl Branch/Commit ID: e541470bc9d0b064bc4ed7dd2b45d8ec67760613 |
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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: tools/group-isoforms-batch.cwl Branch/Commit ID: ad948b2691ef7f0f34de38f0102c3cd6f5182b29 |
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Generate genome indices for STAR & bowtie
Creates indices for: * [STAR](https://github.com/alexdobin/STAR) v2.5.3a (03/17/2017) PMID: [23104886](https://www.ncbi.nlm.nih.gov/pubmed/23104886) * [bowtie](http://bowtie-bio.sourceforge.net/tutorial.shtml) v1.2.0 (12/30/2016) It performs the following steps: 1. `STAR --runMode genomeGenerate` to generate indices, based on [FASTA](http://zhanglab.ccmb.med.umich.edu/FASTA/) and [GTF](http://mblab.wustl.edu/GTF2.html) input files, returns results as an array of files 2. Outputs indices as [Direcotry](http://www.commonwl.org/v1.0/CommandLineTool.html#Directory) data type 3. Separates *chrNameLength.txt* file from Directory output 4. `bowtie-build` to generate indices requires genome [FASTA](http://zhanglab.ccmb.med.umich.edu/FASTA/) file as input, returns results as a group of main and secondary files |
https://github.com/datirium/workflows.git
Path: workflows/genome-indices.cwl Branch/Commit ID: 564156a9e1cc7c3679a926c479ba3ae133b1bfd4 |
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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: 1a46cb0e8f973481fe5ae3ae6188a41622c8532e |
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BAM to BEDPE
Comvert BAM to BEDPE and compress the output |
https://github.com/ncbi/cwl-ngs-workflows-cbb.git
Path: workflows/File-formats/bamtobedpe-gzip.cwl Branch/Commit ID: 6eb7fec3bd018addf02bb3285cb56d9453319d5d |
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RNA-Seq pipeline single-read
The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for a **single-read** 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-read RNA-Seq data. It performs the following steps: 1. Use STAR to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. Use fastx_quality_stats to analyze input FASTQ file and generate quality statistics file 3. 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 file 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/rnaseq-se.cwl Branch/Commit ID: 564156a9e1cc7c3679a926c479ba3ae133b1bfd4 |
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scatter-valuefrom-wf2.cwl
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https://github.com/common-workflow-language/cwltool.git
Path: cwltool/schemas/v1.0/v1.0/scatter-valuefrom-wf2.cwl Branch/Commit ID: fec7a10466a26e376b14181a88734983cfb1b8cb |
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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: 564156a9e1cc7c3679a926c479ba3ae133b1bfd4 |
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rnaseq-pe.cwl
Runs RNA-Seq BioWardrobe basic analysis with pair-end data file. |
https://github.com/Barski-lab/workflows.git
Path: workflows/rnaseq-pe.cwl Branch/Commit ID: 12edfc2207507e53c6b5bb21e50decb5535a12f7 |
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heatmap-prepare.cwl
Workflow runs homer-make-tag-directory.cwl tool using scatter for the following inputs - bam_file - fragment_size - total_reads `dotproduct` is used as a `scatterMethod`, so one element will be taken from each array to construct each job: 1) bam_file[0] fragment_size[0] total_reads[0] 2) bam_file[1] fragment_size[1] total_reads[1] ... N) bam_file[N] fragment_size[N] total_reads[N] `bam_file`, `fragment_size` and `total_reads` arrays should have the identical order. |
https://github.com/datirium/workflows.git
Path: subworkflows/heatmap-prepare.cwl Branch/Commit ID: 58d8b329a6531237205cc36d70604ab0be064402 |
