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Graph	Name	Retrieved From	View
	varscanW.cwl Author: AMBARISH KUMAR er.ambarish@gmail.com; ambari73_sit@jnu.ac.in This is a proposed standard operating procedure for genomic variant detection using VARSCAN. It is hoped to be effective and useful for getting SARS-CoV-2 genome variants. It uses Illumina RNASEQ reads and genome sequence.	https://github.com/ambarishK/bio-cwl-tools.git Path: varscanW.cwl Branch/Commit ID: dccb12c85526c878ce3a489f9502350d187955a5
	Find reads with predicted coding sequences above 60 AA in length	https://github.com/proteinswebteam/ebi-metagenomics-cwl.git Path: workflows/orf_prediction.cwl Branch/Commit ID: 25129f55226dee595ef941edc24d3c44414e0523
	Apply filters to VCF file	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/germline_filter_vcf.cwl Branch/Commit ID: 54846feabbf008c1946db2a86d87252e0edd95b0
	assm_assm_blastn_wnode	https://github.com/ncbi/pgap.git Path: task_types/tt_assm_assm_blastn_wnode.cwl Branch/Commit ID: f225cd99b0e0a5043dd102f8b33a6139fefe9ea4
	fp_filter workflow	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/fp_filter.cwl Branch/Commit ID: 3bb0693076adc1543079a6b136c74906ad546ac6
	exome alignment with qc	https://github.com/genome/analysis-workflows.git Path: definitions/pipelines/alignment_exome.cwl Branch/Commit ID: 54846feabbf008c1946db2a86d87252e0edd95b0
	count-lines1-wf.cwl	https://github.com/common-workflow-language/cwltool.git Path: cwltool/schemas/v1.0/v1.0/count-lines1-wf.cwl Branch/Commit ID: 814bd0405a7701efc7d63e8f0179df394c7766f7
	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: d1bef74924efcb8bfaa00987b3f148d5a192b7a9
	fp_filter workflow	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/fp_filter.cwl Branch/Commit ID: eb0092603bf57acb7bda08a06e4f2f1e2a8c9b6d
	ani_top_n	https://github.com/ncbi/pgap.git Path: task_types/tt_ani_top_n.cwl Branch/Commit ID: 0514ffe248dd11068a3f2268bc67b6ce5ab051d2