Explore Workflows

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

Path: subworkflows/allele-process-reference.cwl

Branch/Commit ID: 3ceeb2e90f49579369b2e10485908516348381a9

RNA-seq 04 quantification

https://github.com/alexbarrera/GGR-cwl.git

Path: v1.0/RNA-seq_pipeline/04-quantification-se-revstranded.cwl

Branch/Commit ID: 1a0dd34d59ec983d1f7ad77bff35da2f016e3134

https://github.com/manabuishii/cwl-samples.git

Path: grep-and-count-for-figure-1.cwl

Branch/Commit ID: 752ef34cfa64ba020ef2d70924fb1e8fea889612

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: 954bb2f213d97dfef1cddaf9e830169a92ad0c6b

Workflow runs homer-make-tag-directory.cwl tool using scatter for the following inputs - bam_file - fragment_size - total_reads `dotproduct` is used as a `scatterMethod`, so one element will be taken from each array to construct each job: 1) bam_file[0] fragment_size[0] total_reads[0] 2) bam_file[1] fragment_size[1] total_reads[1] ... N) bam_file[N] fragment_size[N] total_reads[N] `bam_file`, `fragment_size` and `total_reads` arrays should have the identical order.

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

Path: tools/heatmap-prepare.cwl

Branch/Commit ID: 433c10a6ee9f9b07f1af4141e3df6a584dfe86a1

https://github.com/yyoshiaki/VIRTUS.git

Path: workflow/download_gtf.cwl

Branch/Commit ID: 49687daefb8ae715386f722702755ffaa49283e6

Workflow runs [Bowtie](http://bowtie-bio.sourceforge.net/tutorial.shtml) v1.2.0 (12/30/2016) to build indices for reference genome provided in a single FASTA file as fasta_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/bowtie-index.cwl

Branch/Commit ID: 104059e07a2964673e21d371763e33c0afeb2d03

STARR-seq pipeline - reads: PE

https://github.com/alexbarrera/GGR-cwl.git

Path: v1.0/STARR-seq_pipeline/pipeline-pe-umis.cwl

Branch/Commit ID: 1a0dd34d59ec983d1f7ad77bff35da2f016e3134

https://github.com/uc-cdis/genomel_pipelines.git

Path: genomel/genomel_cohort_freebayes.cwl

Branch/Commit ID: c661469505c606e1353f23c21a6654724a9d8d63

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

Path: definitions/subworkflows/molecular_qc.cwl

Branch/Commit ID: 7638b3075863ae8172f4adaec82fb2eb8e80d3d5