Explore Workflows

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Graph Name Retrieved From View
workflow graph readme-genePrediction-workflow.cwl

https://github.com/nal-i5k/organism_onboarding.git

Path: flow_create_readme/readme-genePrediction-workflow.cwl

Branch/Commit ID: c6077d2756e628f469b446e1584ac8a86582d729

workflow graph workflow.cwl

https://github.com/reanahub/reana-demo-cms-h4l.git

Path: workflow/workflow.cwl

Branch/Commit ID: b1b1f24cff3eb56daf4439a568d2fc824885929c

workflow graph blastp_wnode_struct

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

Path: task_types/tt_blastp_wnode_struct.cwl

Branch/Commit ID: 609aead9804a8f31fa9b3dbc7e52105aec487f31

workflow graph workflow.cwl

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

Path: flow_apollo2_data_processing/processing/workflow.cwl

Branch/Commit ID: 89cff9f0d36a23bf57b3f4bdbd3ed57e3347c945

workflow graph bact_get_kmer_reference

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

Path: task_types/tt_bact_get_kmer_reference.cwl

Branch/Commit ID: d39017c63dd8e088f1ad3809d709529df602e05f

workflow graph wgs alignment and germline variant detection

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

Path: definitions/pipelines/germline_wgs.cwl

Branch/Commit ID: 97572e3a088d79f6a4166385f79e79ea77b11470

workflow graph workflow.cwl

https://github.com/nal-i5k/organism_onboarding.git

Path: flow_dispatch/workflow.cwl

Branch/Commit ID: 7562bd2c6900b30bce6c6f78951cd76d28218f47

workflow graph tt_kmer_compare_wnode

Pairwise comparison

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

Path: task_types/tt_kmer_compare_wnode.cwl

Branch/Commit ID: 90a321ecf2d049330bcf0657cc4d764d2c3f42dd

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: 4a5c59829ff8b9f3c843e66e3c675dcd9c689ed5

workflow graph readgroups_bam_to_readgroups_fastq_lists.cwl

https://github.com/nci-gdc/gdc-dnaseq-cwl.git

Path: workflows/bamfastq_align/readgroups_bam_to_readgroups_fastq_lists.cwl

Branch/Commit ID: 0495e3095182b2e1b4d6274833b3d2ce30347a4e