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Graph	Name	Retrieved From	View
	umi molecular alignment workflow	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/molecular_qc.cwl Branch/Commit ID: 31602b94b34ff55876147c7299e1bec47e8d1a31
	rnaseq-se-dutp.cwl Runs RNA-Seq dUTP BioWardrobe basic analysis with strand specific single-end data file.	https://github.com/Barski-lab/workflows.git Path: workflows/rnaseq-se-dutp.cwl Branch/Commit ID: 812b0ff40dda18ab7a9a872ff13a577be8531ba6
	Motif Finding with HOMER with custom background regions Motif Finding with HOMER with custom 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. 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-bg.cwl Branch/Commit ID: 36fd18f11e939d3908b1eca8d2939402f7a99b0f
	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: f3e44d3b0f198cf5245c49011124dc3b6c2b06fd
	bam_readcount workflow	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/bam_readcount.cwl Branch/Commit ID: 44ada20f3eeb59005d5bd999d2435102e9bae991
	tt_kmer_compare_wnode Pairwise comparison	https://github.com/ncbi/pgap.git Path: task_types/tt_kmer_compare_wnode.cwl Branch/Commit ID: 1c1f5aa0b0dfe5b07beb8ccb0c0e3c073a9fb592
	exome alignment and tumor-only variant detection	https://github.com/genome/analysis-workflows.git Path: definitions/pipelines/tumor_only_exome.cwl Branch/Commit ID: 788bdc99c1d5b6ee7c431c3c011eb30d385c1370
	three_step_color.cwl	https://github.com/common-workflow-language/cwltool.git Path: tests/wf/three_step_color.cwl Branch/Commit ID: 6c86caa0571fd186d90a6600e0bb405596d4a5e0
	downsample unaligned BAM and align	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/downsampled_alignment.cwl Branch/Commit ID: 31602b94b34ff55876147c7299e1bec47e8d1a31
	varscan somatic workflow	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/varscan.cwl Branch/Commit ID: 60edaf6f57eaaf02cda1a3d8cb9a825aa64a43e2