Explore Workflows

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Graph	Name	Retrieved From	View
	Filter single sample sv vcf from paired read callers(Manta/Smoove)	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/sv_paired_read_caller_filter.cwl Branch/Commit ID: 1750cd5cc653f058f521b6195e3bec1e7df1a086
	ROSE: rank ordering of super-enhancers Super-enhancers, consist of clusters of enhancers that are densely occupied by the master regulators and Mediator. Super-enhancers differ from typical enhancers in size, transcription factor density and content, ability to activate transcription, and sensitivity to perturbation. Use to create stitched enhancers, and to separate super-enhancers from typical enhancers using sequencing data (.bam) given a file of previously identified constituent enhancers (.gff)	https://github.com/datirium/workflows.git Path: workflows/super-enhancer.cwl Branch/Commit ID: 7fb8a1ebf8145791440bc2fed9c5f2d78a19d04c
	taxonomy_check_16S	https://github.com/ncbi/pgap.git Path: task_types/tt_taxonomy_check_16S.cwl Branch/Commit ID: b38b0070edf910984f29a4a495b5dfa525b8b305
	Detect DoCM variants	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/docm_germline.cwl Branch/Commit ID: a08de598edc04f340fdbff76c9a92336a7702022
	umi duplex alignment fastq workflow	https://github.com/genome/analysis-workflows.git Path: definitions/pipelines/alignment_umi_duplex.cwl Branch/Commit ID: 40097e1ed094c5b42b68f3db2ff2cbe78c182479
	Unaligned BAM to BQSR and VCF	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/bam_to_bqsr_no_dup_marking.cwl Branch/Commit ID: e56f1024306aeb427d8aae2fff715ed2e8b8f86f
	Chipseq alignment with qc and creating homer tag directory	https://github.com/genome/analysis-workflows.git Path: definitions/pipelines/chipseq.cwl Branch/Commit ID: 3b6d0475c80f5e452793a46a38ee188742b86595
	gather AML trio outputs	https://github.com/genome/analysis-workflows.git Path: definitions/pipelines/aml_trio_cle_gathered.cwl Branch/Commit ID: 3b6d0475c80f5e452793a46a38ee188742b86595
	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: 29bf638904709cfbf10908adcd51ba4886ace94a
	Exome QC workflow	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/qc_exome_no_verify_bam.cwl Branch/Commit ID: 9a657bc8c462542dc7f57fba9e04dc1669f966ba