Explore Workflows

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Graph Name Retrieved From View
workflow graph ani_top_n

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

Path: task_types/tt_ani_top_n.cwl

Branch/Commit ID: 5ec226c941562124032ca6861bc8d1aeabf9d91a
workflow graph Varscan Workflow

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

Path: definitions/subworkflows/varscan_pre_and_post_processing.cwl

Branch/Commit ID: b9e7392e72506cadd898a6ac4db330baf6535ab6
workflow graph assm_assm_blastn_wnode

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

Path: task_types/tt_assm_assm_blastn_wnode.cwl

Branch/Commit ID: 5ec226c941562124032ca6861bc8d1aeabf9d91a
workflow graph kmer_build_tree

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

Path: task_types/tt_kmer_build_tree.cwl

Branch/Commit ID: 5ec226c941562124032ca6861bc8d1aeabf9d91a
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: 5ec226c941562124032ca6861bc8d1aeabf9d91a
workflow graph kmer_ref_compare_wnode

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

Path: task_types/tt_kmer_ref_compare_wnode.cwl

Branch/Commit ID: 5ec226c941562124032ca6861bc8d1aeabf9d91a
workflow graph kmer_seq_entry_extract_wnode

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

Path: task_types/tt_kmer_seq_entry_extract_wnode.cwl

Branch/Commit ID: 5ec226c941562124032ca6861bc8d1aeabf9d91a
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: 4360fb2e778ecee42e5f78f83b78c65ab3a2b1df
workflow graph tt_kmer_top_n.cwl

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

Path: task_types/tt_kmer_top_n.cwl

Branch/Commit ID: 5ec226c941562124032ca6861bc8d1aeabf9d91a
workflow graph Run pindel on provided region

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

Path: definitions/subworkflows/pindel_region.cwl

Branch/Commit ID: 480c438a6a7e78c624712aec01bc4214d2bc179c