Explore Workflows

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Graph Name Retrieved From View
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/Barski-lab/workflows.git

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

Branch/Commit ID: b25b17651171f32005e9d879a9a049382f044baf
workflow graph merge and annotate svs with population allele freq and vep

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

Path: definitions/subworkflows/merge_svs.cwl

Branch/Commit ID: ad65dc1dfff9afa5077f498b85e699716c47f6cb
workflow graph Unaligned bam to sorted, markduped bam

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

Path: definitions/subworkflows/align_sort_markdup.cwl

Branch/Commit ID: d57c2af01a3cb6016e5a264f60641eafd2e5aa05
workflow graph Build STAR indices

Workflow runs [STAR](https://github.com/alexdobin/STAR) v2.5.3a (03/17/2017) PMID: [23104886](https://www.ncbi.nlm.nih.gov/pubmed/23104886) to build indices for reference genome provided in a single FASTA file as fasta_file input and GTF annotation file from annotation_gtf_file input. Generated indices are saved in a folder with the name that corresponds to the input genome.

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

Path: workflows/star-index.cwl

Branch/Commit ID: 42dc4f70b117e78785b82865ec4c4b941ac1c259
workflow graph tmb_workflow.cwl

Workflow to run the TMB analysis on a batch of samples and merge the results back into a single data clinical file

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

Path: cwl/tmb_workflow.cwl

Branch/Commit ID: 5cad957fec135aa55ca8d588372db0557ca1cad5
workflow graph cluster_blastp_wnode and gpx_qdump combined

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

Path: task_types/tt_cluster_and_qdump.cwl

Branch/Commit ID: ae781871782805632f8947c1b11f65507c80cd43
workflow graph 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 **paired-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 paired-end RNA-Seq data. It performs the following steps: 1. Use STAR to align reads from input FASTQ files according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. Use fastx_quality_stats to analyze input FASTQ files and generate quality statistics files 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) 4. Generate BigWig file on the base of sorted BAM file 5. Map input FASTQ files to predefined rRNA reference indices using Bowtie to define the level of rRNA contamination; export resulted statistics to file 6. 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.cwl

Branch/Commit ID: 42dc4f70b117e78785b82865ec4c4b941ac1c259
workflow graph count-lines9-wf.cwl

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/count-lines9-wf.cwl

Branch/Commit ID: 57baec040c99d7edef8242ef51b5470b1c82d733
workflow graph Pairwise genomic regions intersection

Pairwise genomic regions intersection ============================================= Overlaps peaks from two ChIP/ATAC experiments

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

Path: workflows/peak-intersect.cwl

Branch/Commit ID: 261c0232a7a40880f2480b811ed2d7e89c463869
workflow graph DEPRECATED - Motif Finding with HOMER with target and background regions from peaks

Motif Finding with HOMER with target and background regions from peaks --------------------------------------------------- HOMER contains a novel motif discovery algorithm that was designed for regulatory element analysis in genomics applications (DNA only, no protein). It is a differential motif discovery algorithm, which means that it takes two sets of sequences and tries to identify the regulatory elements that are specifically enriched in on set relative to the other. It uses ZOOPS scoring (zero or one occurrence per sequence) coupled with the hypergeometric enrichment calculations (or binomial) to determine motif enrichment. HOMER also tries its best to account for sequenced bias in the dataset. It was designed with ChIP-Seq and promoter analysis in mind, but can be applied to pretty much any nucleic acids motif finding problem. For more information please refer to: ------------------------------------- [Official documentation](http://homer.ucsd.edu/homer/motif/)

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

Path: workflows/homer-motif-analysis-peak.cwl

Branch/Commit ID: 261c0232a7a40880f2480b811ed2d7e89c463869