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Graph Name Retrieved From View
workflow graph Subworkflow to allow calling cnvkit with cram instead of bam files

https://github.com/tmooney/cancer-genomics-workflow.git

Path: definitions/subworkflows/cram_to_cnvkit.cwl

Branch/Commit ID: 233f026ffce240071edda526391be0c03186fce8

workflow graph kmer_cache_store

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

Path: task_types/tt_kmer_cache_store.cwl

Branch/Commit ID: f18c1dce463509170ee3bf2844d5a3637ff706f5

workflow graph Chunked version of phmmer-v3.2.cwl

https://github.com/mscheremetjew/workflow-is-cwl.git

Path: workflows/phmmer-v3.2-chunked-wf.cwl

Branch/Commit ID: 72f702591368397f56d455128f60916902104dd2

workflow graph PrediXcan

Predict.py has been wrapped in cwl, getting the information from: https://github.com/hakyimlab/MetaXcan/wiki/Individual-level-PrediXcan:-introduction,-tutorials-and-manual Here is a snippet from: https://github.com/hakyimlab/MetaXcan/wiki/Individual-level-PrediXcan:-introduction,-tutorials-and-manual In the following, we focus on the individual-level implementation of PrediXcan. The method was originally implemented in this repository. PrediXcan consists of two steps: Predict gene expression (or whatever biology the models predict) in a cohort with available genotypes Run associations to a trait measured in the cohort The first step is implemented in Predict.py. The prediction models are trained and pre-compiled on specific data sets with their own human genome releases and variant definitions. We implemented a few rules to support variant matching from genotypes based on different variant definitions. In the following, mapping refers to the process of assigning a model variant to a genotype variant. Originally, PrediXcan was applied to genes so we say \"gene expression\" a lot as it was the mechanism we initially studied. But conceptually, everything said here applies to any intermediate/molecular mechanism such as splicing or brain morphology. Whenever we say \"gene\", it generally could mean a splicing intron event, etc.

https://github.com/cwl-apps/predixcan_tools.git

Path: predixcan/predixcan_unpack.cwl

Branch/Commit ID: 5b49ef07b994963d190f4f508bc08e4bec8b8a0b

workflow graph phase VCF

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

Path: definitions/subworkflows/phase_vcf.cwl

Branch/Commit ID: 4aba7c6591c2f1ebd827a36d325a58738c429bea

workflow graph scRNA-seq pipeline using Salmon and Alevin

https://github.com/hubmapconsortium/salmon-rnaseq.git

Path: pipeline.cwl

Branch/Commit ID: d6557dc07b83f9ba51d1ff81ed9fc5c8655a9e8b

workflow graph revcomp_with_rename.cwl

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

Path: workflows/workflows/sanbi_cwltutorial/revcomp/revcomp_with_rename.cwl

Branch/Commit ID: 6e008c1170ef818b6c4c63f0eec7baa4f7be7b3c

workflow graph kmer_top_n_extract

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

Path: task_types/tt_kmer_top_n_extract.cwl

Branch/Commit ID: 7cee09fb3e33c851e4e1dfc965c558b82290a785

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: 104059e07a2964673e21d371763e33c0afeb2d03

workflow graph predixcan.cwl

https://github.com/cwl-apps/predixcan_tools.git

Path: predixcan/predixcan.cwl

Branch/Commit ID: 5b49ef07b994963d190f4f508bc08e4bec8b8a0b