Explore Workflows
View already parsed workflows here or click here to add your own
Graph | Name | Retrieved From | View |
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Subworkflow to allow calling cnvkit with cram instead of bam files
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https://github.com/tmooney/cancer-genomics-workflow.git
Path: definitions/subworkflows/cram_to_cnvkit.cwl Branch/Commit ID: 233f026ffce240071edda526391be0c03186fce8 |
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kmer_cache_store
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https://github.com/ncbi/pgap.git
Path: task_types/tt_kmer_cache_store.cwl Branch/Commit ID: f18c1dce463509170ee3bf2844d5a3637ff706f5 |
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Chunked version of phmmer-v3.2.cwl
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https://github.com/mscheremetjew/workflow-is-cwl.git
Path: workflows/phmmer-v3.2-chunked-wf.cwl Branch/Commit ID: 72f702591368397f56d455128f60916902104dd2 |
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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 |
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phase VCF
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https://github.com/genome/analysis-workflows.git
Path: definitions/subworkflows/phase_vcf.cwl Branch/Commit ID: 4aba7c6591c2f1ebd827a36d325a58738c429bea |
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scRNA-seq pipeline using Salmon and Alevin
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https://github.com/hubmapconsortium/salmon-rnaseq.git
Path: pipeline.cwl Branch/Commit ID: d6557dc07b83f9ba51d1ff81ed9fc5c8655a9e8b |
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revcomp_with_rename.cwl
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https://github.com/alexbarrera/GGR-cwl.git
Path: workflows/workflows/sanbi_cwltutorial/revcomp/revcomp_with_rename.cwl Branch/Commit ID: 6e008c1170ef818b6c4c63f0eec7baa4f7be7b3c |
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kmer_top_n_extract
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https://github.com/ncbi/pgap.git
Path: task_types/tt_kmer_top_n_extract.cwl Branch/Commit ID: 7cee09fb3e33c851e4e1dfc965c558b82290a785 |
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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 |
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predixcan.cwl
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https://github.com/cwl-apps/predixcan_tools.git
Path: predixcan/predixcan.cwl Branch/Commit ID: 5b49ef07b994963d190f4f508bc08e4bec8b8a0b |