Explore Workflows

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

Path: cwltool/schemas/v1.0/v1.0/scatter-valuefrom-wf2.cwl

Branch/Commit ID: f207d168f4e7eb4dd2279840d4062ba75d9c79c3

Seurat Cluster ============== Runs filtering, integration, and clustering analyses for Cell Ranger Count Gene Expression or Cell Ranger Aggregate experiments.

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

Path: workflows/seurat-cluster.cwl

Branch/Commit ID: 12e5256de1b680c551c87fd5db6f3bc65428af67

https://git.astron.nl/RD/LINC.git

Path: workflows/ldv_prefactor_calibrator.cwl

Branch/Commit ID: efd84bf3c56117c6a05852b3b47aaf4ad8de11bd

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

Path: definitions/pipelines/gathered_downsample_and_recall.cwl

Branch/Commit ID: c625e05eefb1754353c1bdfa46c01dc61e6233dd

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

Path: definitions/subworkflows/pindel_cat.cwl

Branch/Commit ID: 1249b5d4e23d57ca5e3b8ad6d8e5f10ddb019f68

https://github.com/phenoflow/Long-covid---ace80670-7a21-11ed-b9d2-e51f21933d80.git

Path: Long-covid.cwl

Branch/Commit ID: main

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: a1f6ca50fcb0881781b3ba0306dd61ebf555eaba

https://github.com/Duke-GCB/bespin-cwl.git

Path: subworkflows/exomeseq-gatk4-02-variantdiscovery.cwl

Branch/Commit ID: 9f96a6621a0925d4a1ef4b6a733316149520b008

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

Path: definitions/subworkflows/gatk_haplotypecaller_iterator.cwl

Branch/Commit ID: 049f4aeff4c4a1b8421cac9b1c1c1f0da5848315

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

Path: task_types/tt_kmer_build_tree.cwl

Branch/Commit ID: 485f0be56cea77bff62b797ae7eff422a990a92c