Explore Workflows

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

Path: cwltool/schemas/v1.0/v1.0/schemadef-wf.cwl

Branch/Commit ID: 047e69bb169e79fad6a7285ee798c4ecec3b218b

https://github.com/hubmapconsortium/sc-atac-seq-pipeline.git

Path: steps/bulk_process.cwl

Branch/Commit ID: 53415520f94ac0d9a1fae89af0f6e8250240723a

Workflow to build different indices for different tools from a genome and transcriptome. This workflow expects an (annotated) genome in GBOL ttl format. Steps: - SAPP: rdf2gtf (genome fasta) - SAPP: rdf2fasta (transcripts fasta) - STAR index (Optional for Eukaryotic origin) - bowtie2 index - kallisto index

https://git.wageningenur.nl/unlock/cwl.git

Path: cwl/workflows/workflow_indexbuilder.cwl

Branch/Commit ID: b9097b82e6ab6f2c9496013ce4dd6877092956a0

https://github.com/NAL-i5K/Organism_Onboarding.git

Path: flow_dispatch/2blat/workflow.cwl

Branch/Commit ID: 5910b4d88aca172252d9102ddb610a7dc9e1347f

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 single-read** 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 single-read 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-se-dutp-mitochondrial.cwl

Branch/Commit ID: 954bb2f213d97dfef1cddaf9e830169a92ad0c6b

This is a workflow to go from standard bams to collapsed bams and QC results.

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

Path: workflows/subworkflows/standard_bam_to_collapsed_qc.cwl

Branch/Commit ID: b0f226a9ac5152f3afe0d38c8cd54aa25b8b01cf

https://github.com/NAL-i5K/Organism_Onboarding.git

Path: flow_dispatch/2other_species/workflow.cwl

Branch/Commit ID: 5910b4d88aca172252d9102ddb610a7dc9e1347f

Workflow for running the facets suite workflow on a single tumor normal pair Includes handling of errors in case execution fails for the sample pair

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

Path: cwl/facets-suite-workflow.cwl

Branch/Commit ID: 59b69eed7ffefcffd81313ec8ffb84c0d716b933

Workflow to run GetBaseCountsMultiSample fillout on a number of bam files with a single maf file

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

Path: cwl/fillout_workflow.cwl

Branch/Commit ID: 59b69eed7ffefcffd81313ec8ffb84c0d716b933

What is MAnorm? -------------- MAnorm is a robust model for quantitative comparison of ChIP-Seq data sets of TFs (transcription factors) or epigenetic modifications and you can use it for: * Normalization of two ChIP-seq samples * Quantitative comparison (differential analysis) of two ChIP-seq samples * Evaluating the overlap enrichment of the protein binding sites(peaks) * Elucidating underlying mechanisms of cell-type specific gene regulation How MAnorm works? ---------------- MAnorm uses common peaks of two samples as a reference to build the rescaling model for normalization, which is based on the empirical assumption that if a chromatin-associated protein has a substantial number of peaks shared in two conditions, the binding at these common regions will tend to be determined by similar mechanisms, and thus should exhibit similar global binding intensities across samples. The observed differences on common peaks are presumed to reflect the scaling relationship of ChIP-Seq signals between two samples, which can be applied to all peaks. What do the inputs mean? ---------------- ### General **Experiment short name/Alias** * short name for you experiment to identify among the others **ChIP-Seq PE sample 1** * previously analyzed ChIP-Seq paired-end experiment to be used as Sample 1 **ChIP-Seq PE sample 2** * previously analyzed ChIP-Seq paired-end experiment to be used as Sample 2 **Genome** * Reference genome to be used for gene assigning ### Advanced **Reads shift size for sample 1** * This value is used to shift reads towards 3' direction to determine the precise binding site. Set as half of the fragment length. Default 100 **Reads shift size for sample 2** * This value is used to shift reads towards 5' direction to determine the precise binding site. Set as half of the fragment length. Default 100 **M-value (log2-ratio) cutoff** * Absolute M-value (log2-ratio) cutoff to define biased (differential binding) peaks. Default: 1.0 **P-value cutoff** * P-value cutoff to define biased peaks. Default: 0.01 **Window size** * Window size to count reads and calculate read densities. 2000 is recommended for sharp histone marks like H3K4me3 and H3K27ac, and 1000 for TFs or DNase-seq. Default: 2000

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

Path: workflows/manorm-pe.cwl

Branch/Commit ID: 954bb2f213d97dfef1cddaf9e830169a92ad0c6b