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Graph Name Retrieved From View
workflow graph Cut-n-Run pipeline paired-end

Experimental pipeline for Cut-n-Run analysis. Uses mapping results from the following experiment types: - `chipseq-pe.cwl` - `trim-chipseq-pe.cwl` - `trim-atacseq-pe.cwl` Note, the upstream analyses should not have duplicates removed

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

Path: workflows/trim-chipseq-pe-cut-n-run.cwl

Branch/Commit ID: 282762f8bbaea57dd488115745ef798e128bade1
workflow graph PGAP Pipeline, simple user input, PGAPX-134

PGAP pipeline for external usage, powered via containers, simple user input: (FASTA + yaml only, no template)

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

Path: pgap.cwl

Branch/Commit ID: 551493f5c24b757a46cd22821a05e6ac6dcceb7f
workflow graph final_filtering

Final filtering

https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git

Path: structuralvariants/cwl/subworkflows/final_filtering.cwl

Branch/Commit ID: 6ccec9c5c5bc9fb4e75ca0b9cc22d13df9ffb815
workflow graph Generate genome index bowtie

Workflow makes indices for [bowtie](http://bowtie-bio.sourceforge.net/tutorial.shtml) v1.2.0 (12/30/2016). Executes `bowtie-index` to generate indices requires genome [FASTA](http://zhanglab.ccmb.med.umich.edu/FASTA/) file as input, returns results as a directory

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

Path: workflows/bowtie-index.cwl

Branch/Commit ID: 2b8146f76595f0c4d8bf692de78b21280162b1d0
workflow graph Variant calling germline paired-end

A workflow for the Broad Institute's best practices gatk4 germline variant calling pipeline. ## __Outputs__ #### Primary Output files: - bqsr2_indels.vcf, filtered and recalibrated indels (IGV browser) - bqsr2_snps.vcf, filtered and recalibrated snps (IGV browser) - bqsr2_snps.ann.vcf, filtered and recalibrated snps with effect annotations #### Secondary Output files: - sorted_dedup_reads.bam, sorted deduplicated alignments (IGV browser) - raw_indels.vcf, first pass indel calls - raw_snps.vcf, first pass snp calls #### Reports: - overview.md (input list, alignment metrics, variant counts) - insert_size_histogram.pdf - recalibration_plots.pdf - snpEff_summary.html ## __Inputs__ #### General Info - Sample short name/Alias: unique name for sample - Experimental condition: condition, variable, etc name (e.g. \"control\" or \"20C 60min\") - Cells: name of cells used for the sample - Catalog No.: vender catalog number if available - BWA index: BWA index sample that contains reference genome FASTA with associated indices. - SNPEFF database: Name of SNPEFF database to use for SNP effect annotation. - Read 1 file: First FASTQ file (generally contains \"R1\" in the filename) - Read 2 file: Paired FASTQ file (generally contains \"R2\" in the filename) #### Advanced - Ploidy: number of copies per chromosome (default should be 2) - SNP filters: see Step 6 Notes: https://gencore.bio.nyu.edu/variant-calling-pipeline-gatk4/ - Indel filters: see Step 7 Notes: https://gencore.bio.nyu.edu/variant-calling-pipeline-gatk4/ #### SNPEFF notes: Get snpeff databases using `docker run --rm -ti gatk4-dev /bin/bash` then running `java -jar $SNPEFF_JAR databases`. Then, use the first column as SNPEFF input (e.g. \"hg38\"). - hg38, Homo_sapiens (USCS), http://downloads.sourceforge.net/project/snpeff/databases/v4_3/snpEff_v4_3_hg38.zip - mm10, Mus_musculus, http://downloads.sourceforge.net/project/snpeff/databases/v4_3/snpEff_v4_3_mm10.zip - dm6.03, Drosophila_melanogaster, http://downloads.sourceforge.net/project/snpeff/databases/v4_3/snpEff_v4_3_dm6.03.zip - Rnor_6.0.86, Rattus_norvegicus, http://downloads.sourceforge.net/project/snpeff/databases/v4_3/snpEff_v4_3_Rnor_6.0.86.zip - R64-1-1.86, Saccharomyces_cerevisiae, http://downloads.sourceforge.net/project/snpeff/databases/v4_3/snpEff_v4_3_R64-1-1.86.zip ### __Data Analysis Steps__ 1. Trimming the adapters with TrimGalore. - This step is particularly important when the reads are long and the fragments are short - resulting in sequencing adapters at the ends of reads. If adapter is not removed the read will not map. TrimGalore can recognize standard adapters, such as Illumina or Nextera/Tn5 adapters. 2. Generate quality control statistics of trimmed, unmapped sequence data 3. Run germline variant calling pipeline, custom wrapper script implementing Steps 1 - 17 of the Broad Institute's best practices gatk4 germline variant calling pipeline (https://gencore.bio.nyu.edu/variant-calling-pipeline-gatk4/) ### __References__ 1. https://gencore.bio.nyu.edu/variant-calling-pipeline-gatk4/ 2. https://gatk.broadinstitute.org/hc/en-us/articles/360035535932-Germline-short-variant-discovery-SNPs-Indels- 3. https://software.broadinstitute.org/software/igv/VCF

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

Path: workflows/vc-germline-pe.cwl

Branch/Commit ID: 7030da528559c7106d156284e50ff0ecedab0c4e
workflow graph scatter-wf4.cwl#main

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

Path: cwltool/schemas/v1.0/v1.0/scatter-wf4.cwl

Branch/Commit ID: d7b1bf353dcc43c707c49a018f2870584821d389

Packed ID: main
workflow graph umi molecular alignment fastq workflow

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

Path: definitions/pipelines/umi_molecular_alignment.cwl

Branch/Commit ID: d2c2f2eb846ae2e9cdcab46e3bb88e42126cb3f5
workflow graph pindel parallel workflow

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

Path: definitions/subworkflows/pindel.cwl

Branch/Commit ID: f42c889734c8f709ad2fd9090493bcaac8326c98
workflow graph Immunotherapy Workflow

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

Path: definitions/pipelines/immuno.cwl

Branch/Commit ID: 5cb188131f786ed33156e2f0e3dd63ab9c04245d
workflow graph RNA-Seq pipeline paired-end stranded mitochondrial

Slightly changed original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for **strand specific pair-end** experiment. An additional steps were added to map data to mitochondrial chromosome only and then merge the output. Experiment files in [FASTQ](http://maq.sourceforge.net/fastq.shtml) format either compressed or not can be used. Current workflow should be used only with the pair-end strand specific RNA-Seq data. It performs the following steps: 1. `STAR` to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. `fastx_quality_stats` to analyze input FASTQ file and generate quality statistics file 3. `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-pe-dutp-mitochondrial.cwl

Branch/Commit ID: 664de58d95728edbf7d369d894f9037ebe2475fa