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Graph	Name	Retrieved From	View
	Unaligned bam to sorted, markduped bam	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/align_sort_markdup.cwl Branch/Commit ID: 479c9b3e3fa32ec9c7cd4073cfbccc675fd254d9
	Cut-n-Run pipeline paired-end Experimental pipeline for Cut-n-Run analysis. Uses mapping results from the following experiment types: - `chipseq-pe.cwl` - `trim-chipseq-pe.cwl` - `trim-atacseq-pe.cwl` Note, the upstream analyses should not have duplicates removed	https://github.com/datirium/workflows.git Path: workflows/trim-chipseq-pe-cut-n-run.cwl Branch/Commit ID: 935a78f1aff757f977de4e3672aefead3b23606b
	Cellranger Reanalyze Cellranger Reanalyze ====================	https://github.com/datirium/workflows.git Path: workflows/cellranger-reanalyze.cwl Branch/Commit ID: 935a78f1aff757f977de4e3672aefead3b23606b
	super-enhancer.cwl Both `islands_file` and `islands_control_file` should be produced by the same cwl tool (iaintersect.cwl or macs2-callpeak-biowardrobe-only.cwl)	https://github.com/Barski-lab/workflows.git Path: workflows/super-enhancer.cwl Branch/Commit ID: 1d45ce42181dfce7aceec8bc99a3730eb5285948
	assemble.cwl Assemble a set of reads using SKESA	https://github.com/ncbi/pgap.git Path: assemble.cwl Branch/Commit ID: 505b91e41741ccbcd5ebd2b6a09a3be604f9ece3
	MAnorm PE - quantitative comparison of ChIP-Seq paired-end data What is MAnorm? -------------- MAnorm is a robust model for quantitative comparison of ChIP-Seq data sets of TFs (transcription factors) or epigenetic modifications and you can use it for: * Normalization of two ChIP-seq samples * Quantitative comparison (differential analysis) of two ChIP-seq samples * Evaluating the overlap enrichment of the protein binding sites(peaks) * Elucidating underlying mechanisms of cell-type specific gene regulation How MAnorm works? ---------------- MAnorm uses common peaks of two samples as a reference to build the rescaling model for normalization, which is based on the empirical assumption that if a chromatin-associated protein has a substantial number of peaks shared in two conditions, the binding at these common regions will tend to be determined by similar mechanisms, and thus should exhibit similar global binding intensities across samples. The observed differences on common peaks are presumed to reflect the scaling relationship of ChIP-Seq signals between two samples, which can be applied to all peaks. What do the inputs mean? ---------------- ### General Experiment short name/Alias * short name for you experiment to identify among the others ChIP-Seq PE sample 1 * previously analyzed ChIP-Seq paired-end experiment to be used as Sample 1 ChIP-Seq PE sample 2 * previously analyzed ChIP-Seq paired-end experiment to be used as Sample 2 Genome * Reference genome to be used for gene assigning ### Advanced Reads shift size for sample 1 * This value is used to shift reads towards 3' direction to determine the precise binding site. Set as half of the fragment length. Default 100 Reads shift size for sample 2 * This value is used to shift reads towards 5' direction to determine the precise binding site. Set as half of the fragment length. Default 100 M-value (log2-ratio) cutoff * Absolute M-value (log2-ratio) cutoff to define biased (differential binding) peaks. Default: 1.0 P-value cutoff * P-value cutoff to define biased peaks. Default: 0.01 Window size * Window size to count reads and calculate read densities. 2000 is recommended for sharp histone marks like H3K4me3 and H3K27ac, and 1000 for TFs or DNase-seq. Default: 2000	https://github.com/datirium/workflows.git Path: workflows/manorm-pe.cwl Branch/Commit ID: 935a78f1aff757f977de4e3672aefead3b23606b
	Motif Finding with HOMER with target and background regions from peaks Motif Finding with HOMER with target and background regions from peaks --------------------------------------------------- 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. 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-peak.cwl Branch/Commit ID: 935a78f1aff757f977de4e3672aefead3b23606b
	varscan somatic workflow	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/varscan.cwl Branch/Commit ID: 6b365b79675b2aabfb8d5829bb8df0a6e986b037
	Cut-n-Run pipeline paired-end Experimental pipeline for Cut-n-Run analysis. Uses mapping results from the following experiment types: - `chipseq-pe.cwl` - `trim-chipseq-pe.cwl` - `trim-atacseq-pe.cwl` Note, the upstream analyses should not have duplicates removed	https://github.com/datirium/workflows.git Path: workflows/trim-chipseq-pe-cut-n-run.cwl Branch/Commit ID: 4a5c59829ff8b9f3c843e66e3c675dcd9c689ed5
	workflow.cwl	https://github.com/NAL-i5K/Organism_Onboarding.git Path: flow_dispatch/workflow.cwl Branch/Commit ID: 8b8c6dd16e06b43fbb50f1c0821856a31f1bbbc5