Explore Workflows

View already parsed workflows here or click here to add your own

Graph Name Retrieved From View
workflow graph mut.cwl

https://github.com/common-workflow-language/cwltool.git

Path: tests/wf/mut.cwl

Branch/Commit ID: 6b8f06a9f6f6a570142c7aedc767fea2efa2a0cc
workflow graph cond-single-source-wf-005.1.cwl

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

Path: testdata/cond-single-source-wf-005.1.cwl

Branch/Commit ID: b926e330eba795f3acc1f71fd0645e75f925a2da
workflow graph gp_makeblastdb

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

Path: progs/gp_makeblastdb.cwl

Branch/Commit ID: 369e2b6c7f4db75099d258729dec1326f55d2cc5
workflow graph Filter ChIP/ATAC peaks for Tag Density Profile or Motif Enrichment analyses

Filters ChIP/ATAC peaks with the neatest genes assigned for Tag Density Profile or Motif Enrichment analyses ============================================================================================================ Tool filters output from any ChIP/ATAC pipeline to create a file with regions of interest for Tag Density Profile or Motif Enrichment analyses. Peaks with duplicated coordinates are discarded.

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

Path: workflows/filter-peaks-for-heatmap.cwl

Branch/Commit ID: 36fd18f11e939d3908b1eca8d2939402f7a99b0f
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 must be used with paired-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 2 (after running STAR) 5. Generate BigWig file using 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: 57863b6131d8262c5ce864adaf8e4038401e71a2
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: f3e44d3b0f198cf5245c49011124dc3b6c2b06fd
workflow graph step_valuefrom5_wf_v1_2.cwl

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

Path: testdata/step_valuefrom5_wf_v1_2.cwl

Branch/Commit ID: 0ab1d42d10f7311bb4032956c4a6f3d2730d9507
workflow graph Feature expression merge - combines feature expression from several experiments

Feature expression merge - combines feature expression from several experiments ========================================================================= Workflows merges RPKM (by default) gene expression from several experiments based on the values from GeneId, Chrom, TxStart, TxEnd and Strand columns (by default). Reported unique columns are renamed based on the experiments names.

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

Path: workflows/feature-merge.cwl

Branch/Commit ID: 57863b6131d8262c5ce864adaf8e4038401e71a2
workflow graph wgs alignment with qc

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

Path: definitions/pipelines/alignment_wgs.cwl

Branch/Commit ID: adcae308fdccaa1190083616118dfadb4df65dca
workflow graph Seed Protein Alignments

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

Path: protein_alignment/wf_seed_seqids.cwl

Branch/Commit ID: cb15f907132fb90bc66b39bb0af3c211801feba1