Explore Workflows

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Graph Name Retrieved From View
workflow graph allele-process-strain.cwl

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

Path: subworkflows/allele-process-strain.cwl

Branch/Commit ID: 3b2e0de49d9ee6fd9a8c9580b6a02d0f7e4c8f7c
workflow graph RNA-Seq alignment and transcript/gene abundance workflow

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

Path: definitions/pipelines/rnaseq.cwl

Branch/Commit ID: 3034168d652bfa930ba09af20e473a4564a8010d
workflow graph tt_univec_wnode.cwl

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

Path: task_types/tt_univec_wnode.cwl

Branch/Commit ID: 7b5130d2408bce82ee15c666b37d931ef6f452e3
workflow graph Merge, annotate, and generate a TSV for SVs

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

Path: definitions/subworkflows/merge_svs.cwl

Branch/Commit ID: d57c2af01a3cb6016e5a264f60641eafd2e5aa05
workflow graph Trim Galore 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-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 file 2. Use STAR to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 3. Use fastx_quality_stats to analyze input FASTQ file and generate quality statistics file 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 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/trim-rnaseq-se.cwl

Branch/Commit ID: e238d1756f1db35571e84d72e1699e5d1540f10c
workflow graph allele-alignreads-se-pe.cwl

Workflow maps FASTQ files from `fastq_files` input into reference genome `reference_star_indices_folder` and insilico generated `insilico_star_indices_folder` genome (concatenated genome for both `strain1` and `strain2` strains). For both genomes STAR is run with `outFilterMultimapNmax` parameter set to 1 to discard all of the multimapped reads. For insilico genome SAM file is generated. Then it's splitted into two SAM files based on strain names and then sorted by coordinates into the BAM format. For reference genome output BAM file from STAR slignment is also coordinate sorted.

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

Path: subworkflows/allele-alignreads-se-pe.cwl

Branch/Commit ID: 3b2e0de49d9ee6fd9a8c9580b6a02d0f7e4c8f7c
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: a839eb6390974089e1a558c49fc07b4c66c50767
workflow graph Detect Variants workflow for WGS pipeline

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

Path: definitions/pipelines/detect_variants_wgs.cwl

Branch/Commit ID: 3034168d652bfa930ba09af20e473a4564a8010d
workflow graph bacterial_orthology

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

Path: bacterial_orthology/wf_bacterial_orthology.cwl

Branch/Commit ID: 91181df8d9ef8eed9d8f40db707b9a4376fecaf5
workflow graph find_hotspots_in_normals.cwl

Workflow to find hotspot VAFs from duplex (for Tumor sample) and unfiltered (for Normal sample) pileups. These inputs are all required to be sorted in the same order: sample_ids patient_ids sample_classes unfiltered_pileups duplex_pileups

https://github.com/mskcc/ACCESS-Pipeline.git

Path: workflows/subworkflows/find_hotspots_in_normals.cwl

Branch/Commit ID: b0f226a9ac5152f3afe0d38c8cd54aa25b8b01cf