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Graph	Name	Retrieved From	View
	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: 60854b5d299df91e135e05d02f4be61f6a310fbc
	Replace legacy AML Trio Assay	https://github.com/genome/analysis-workflows.git Path: definitions/pipelines/aml_trio_cle.cwl Branch/Commit ID: 174f3b239018328cec1d821947438b457552724c
	Subworkflow that runs cnvkit in single sample mode and returns a vcf file	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/cnvkit_single_sample.cwl Branch/Commit ID: da335d9963418f7bedd84cb2791a0df1b3165ffe
	gcaccess_from_list	https://github.com/ncbi/pgap.git Path: task_types/tt_gcaccess_from_list.cwl Branch/Commit ID: 4b73bfeb967ee9f57a0410276f7c39e784f0846f
	workflow-slurmcern.cwl	https://github.com/reanahub/reana-demo-root6-roofit.git Path: workflow/cwl/workflow-slurmcern.cwl Branch/Commit ID: 2b79f1c4aea6981845647b1ba880832288eaeb88
	kmer_build_tree	https://github.com/ncbi/pgap.git Path: task_types/tt_kmer_build_tree.cwl Branch/Commit ID: 92118627c800e4addb7e29b9dabcca073a5bae71
	umi duplex alignment workflow	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/duplex_alignment.cwl Branch/Commit ID: a59a803e1809a8fbfabca6b8962a8ad66dd01f1d
	MAnorm SE - quantitative comparison of ChIP-Seq single-read 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 SE sample 1 * previously analyzed ChIP-Seq single-read experiment to be used as Sample 1 ChIP-Seq SE sample 2 * previously analyzed ChIP-Seq single-read 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-se.cwl Branch/Commit ID: c9e7f3de7f6ba38ee663bd3f9649e8d7dbac0c86
	fastq_contamination_cleanup This workflow detect and remove contamination from a DNA fasta file	https://github.com/ncbi/cwl-ngs-workflows-cbb.git Path: workflows/Contamination/fastq-contamination-cleanup.cwl Branch/Commit ID: 11f70a71cb68b3960c2d410ba1fdcd3b8a7e1419
	Subworkflow that runs cnvkit in single sample mode and returns a vcf file	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/cnvkit_single_sample.cwl Branch/Commit ID: a59a803e1809a8fbfabca6b8962a8ad66dd01f1d