Explore Workflows

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Graph Name Retrieved From View
workflow graph wgs alignment and germline variant detection

https://github.com/genome/analysis-workflows.git

Path: definitions/pipelines/germline_wgs.cwl

Branch/Commit ID: 8da2b1cd6fa379b2c22baf9dad762d39630e6f46
workflow graph maf2vcf_gz_workflow.cwl

Workflow to convert a maf file into a vcf.gz with .tbi index

https://github.com/mskcc/pluto-cwl.git

Path: cwl/maf2vcf_gz_workflow.cwl

Branch/Commit ID: 59b69eed7ffefcffd81313ec8ffb84c0d716b933
workflow graph example.cwl

Example CWL workflow that uses some advanced features

https://github.com/mskcc/pluto-cwl.git

Path: cwl/example.cwl

Branch/Commit ID: 59b69eed7ffefcffd81313ec8ffb84c0d716b933
workflow graph Trim Galore RNA-Seq pipeline paired-end

The 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.cwl

Branch/Commit ID: 5e7385b8cfa4ddae822fff37b6bd22eb0370b389
workflow graph Cellranger reanalyze - reruns secondary analysis performed on the feature-barcode matrix

Devel version of Single-Cell Cell Ranger Reanalyze ================================================== Workflow calls \"cellranger aggr\" command to rerun secondary analysis performed on the feature-barcode matrix (dimensionality reduction, clustering and visualization) using different parameter settings. As an input we use filtered feature-barcode matrices in HDF5 format from cellranger count or aggr experiments. Note, we don't pass aggregation_metadata from the upstream cellranger aggr step. Need to address this issue when needed.

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

Path: workflows/cellranger-reanalyze.cwl

Branch/Commit ID: 954bb2f213d97dfef1cddaf9e830169a92ad0c6b
workflow graph count-lines11-wf-noET.cwl

https://github.com/common-workflow-language/cwl-v1.1.git

Path: tests/count-lines11-wf-noET.cwl

Branch/Commit ID: 0e37d46e793e72b7c16b5ec03e22cb3ce1f55ba3
workflow graph chipseq-gen-bigwig.cwl

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

Path: subworkflows/chipseq-gen-bigwig.cwl

Branch/Commit ID: 01c6af7a598eb44f6bcaa9b5eecf13229f28546e
workflow graph scRNA-seq pipeline using Salmon and Alevin

https://github.com/hubmapconsortium/salmon-rnaseq.git

Path: pipeline.cwl

Branch/Commit ID: d71be688b76ed09a64cf5d8cf36182fe35cce6b3
workflow graph DESeq - differential gene expression analysis

Differential gene expression analysis ===================================== Differential gene expression analysis based on the negative binomial distribution Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution. DESeq1 ------ High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. Simon Anders and Wolfgang Huber propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, [DESeq](http://bioconductor.org/packages/release/bioc/html/DESeq.html), as an R/Bioconductor package DESeq2 ------ In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. [DESeq2](http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html), a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression.

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

Path: workflows/deseq.cwl

Branch/Commit ID: 954bb2f213d97dfef1cddaf9e830169a92ad0c6b
workflow graph scatter GATK HaplotypeCaller over intervals

https://github.com/genome/analysis-workflows.git

Path: definitions/subworkflows/gatk_haplotypecaller_iterator.cwl

Branch/Commit ID: 0a9a4ce83b49ed4e7eee5bcc09d83725136a36b0