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workflow graph workflow_with_facets.cwl

CWL workflow for generating Roslin / Argos post pipeline analysis files and cBioPortal data and metadata files This workflow includes Facets and Facets Suite usages Inputs ------ The following parameters are required: project_id project_pi request_pi project_short_name project_name project_description cancer_type cancer_study_identifier argos_version_string helix_filter_version is_impact extra_pi_groups pairs The following filenames are required: analysis_mutations_filename analysis_gene_cna_filename analysis_sv_filename analysis_segment_cna_filename cbio_segment_data_filename cbio_meta_cna_segments_filename The following filenames have default values and are optional: cbio_mutation_data_filename cbio_cna_data_filename cbio_fusion_data_filename cbio_clinical_patient_data_filename cbio_clinical_sample_data_filename cbio_clinical_sample_meta_filename cbio_clinical_patient_meta_filename cbio_meta_study_filename cbio_meta_cna_filename cbio_meta_fusions_filename cbio_meta_mutations_filename cbio_cases_all_filename cbio_cases_cnaseq_filename cbio_cases_cna_filename cbio_cases_sequenced_filename Output ------ Workflow output should look like this: output ├── analysis │   ├── <project_id>.gene.cna.txt │   ├── <project_id>.muts.maf │   ├── <project_id>.seg.cna.txt │   └── <project_id>.svs.maf ├── facets │ ├── <tumor_id>.<normal_id> (passed) │ │ └── <facets_files> │ └── <tumor_id>.<normal_id> (failed) │ └── <log_files> └── portal ├── case_list │   ├── cases_all.txt │   ├── cases_cnaseq.txt │   ├── cases_cna.txt │   └── cases_sequenced.txt ├── data_clinical_patient.txt ├── data_clinical_sample.txt ├── data_CNA.ascna.txt ├── data_CNA.scna.txt ├── data_CNA.txt ├── data_fusions.txt ├── data_mutations_extended.txt ├── meta_clinical_patient.txt ├── meta_clinical_sample.txt ├── meta_CNA.txt ├── meta_fusions.txt ├── meta_mutations_extended.txt ├── meta_study.txt ├── <project_id>_data_cna_hg19.seg └── <project_id>_meta_cna_hg19_seg.txt

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

Path: cwl/workflow_with_facets.cwl

Branch/Commit ID: 462f6015c9268a4205b6e81de018a470b8a4a153
workflow graph scatter-wf1_v1_0.cwl

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

Path: testdata/scatter-wf1_v1_0.cwl

Branch/Commit ID: e949503ac0dd7e22ba9b04ac51926d13780f9cee
workflow graph fail-wf.cwl

Run failtool which will fail

 https://github.com/Duke-GCB/calrissian.git

Path: input-data/fail-wf.cwl

Branch/Commit ID: ceb1c2731dd4c3c20229a5cad06a64a487103c21
workflow graph chipseq-pe.cwl

Runs ChIP-Seq BioWardrobe basic analysis with paired-end input data files.

https://github.com/Barski-lab/workflows.git

Path: workflows/chipseq-pe.cwl

Branch/Commit ID: 9a03dbe8829ca649814d9c8bd11fe3a673750a95
workflow graph Cell Ranger Aggregate (RNA, RNA+VDJ)

Cell Ranger Aggregate (RNA, RNA+VDJ) Combines outputs from multiple runs of either “Cell Ranger Count (RNA)” or “Cell Ranger Count (RNA+VDJ)” pipelines. The results of this workflow are primarily used in “Single-Cell RNA-Seq Filtering Analysis” and “Single-Cell Immune Profiling Analysis” pipelines.

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

Path: workflows/cellranger-aggr.cwl

Branch/Commit ID: b4d578c2ba4713a5a22163d9f8c7105acda1f22e
workflow graph Trim Galore RNA-Seq pipeline paired-end strand specific

Modified 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-dutp.cwl

Branch/Commit ID: b4d578c2ba4713a5a22163d9f8c7105acda1f22e
workflow graph Single-Cell RNA-Seq Differential Expression Analysis

Single-Cell RNA-Seq Differential Expression Analysis Identifies differentially expressed genes between any two groups of cells, optionally aggregating gene expression data from single-cell to pseudobulk form.

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

Path: workflows/sc-rna-de-pseudobulk.cwl

Branch/Commit ID: b4d578c2ba4713a5a22163d9f8c7105acda1f22e
workflow graph step-valuefrom3-wf.cwl

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

Path: cwltool/schemas/v1.0/v1.0/step-valuefrom3-wf.cwl

Branch/Commit ID: 2ae8117360a3cd4909d9d3f2b35c30bfffb25d0a
workflow graph mut3.cwl

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

Path: tests/wf/mut3.cwl

Branch/Commit ID: 63f539ba60e91f0cb3ce7cda2c5da5c65525c375
workflow graph Motif Finding with HOMER with random background regions

Motif Finding with HOMER with random background regions --------------------------------------------------- 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. Here is how we generate background for Motifs Analysis ------------------------------------- 1. Take input file with regions in a form of “chr\" “start\" “end\" 2. Sort and remove duplicates from this regions file 3. Extend each region in 20Kb into both directions 4. Merge all overlapped extended regions 5. Subtract not extended regions from the extended ones 6. Randomly distribute not extended regions within the regions that we got as a result of the previous step 7. Get fasta file from these randomly distributed regions (from the previous step). Use it as background 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.cwl

Branch/Commit ID: 12e5256de1b680c551c87fd5db6f3bc65428af67