Explore Workflows

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Graph	Name	Retrieved From	View
	exome alignment and germline variant detection	https://github.com/genome/analysis-workflows.git Path: definitions/pipelines/germline_exome.cwl Branch/Commit ID: 35e6b3ef71b4a2a9caba1dbd5dc424a8809bcc0a
	vecscreen.cwl	https://github.com/ncbi/pgap.git Path: vecscreen/vecscreen.cwl Branch/Commit ID: 7cee09fb3e33c851e4e1dfc965c558b82290a785
	Detect Docm variants	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/docm_cle.cwl Branch/Commit ID: 2decd55996b912feb48be5db1b052aa3274ee405
	cache_asnb_entries	https://github.com/ncbi/pgap.git Path: task_types/tt_cache_asnb_entries.cwl Branch/Commit ID: be465ad19b07378f3f863f2c4e0019b420c859f2
	kmer_cache_retrieve	https://github.com/ncbi/pgap.git Path: task_types/tt_kmer_cache_retrieve.cwl Branch/Commit ID: bb2f26dfe630179737ec2ff08a8614f1f47abcaf
	bam to trimmed fastqs	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/bam_to_trimmed_fastq.cwl Branch/Commit ID: 844c10a4466ab39c02e5bfa7a210c195b8efa77a
	etl_http.cwl	https://github.com/NCI-GDC/gdc-dnaseq-cwl.git Path: workflows/dnaseq/etl_http.cwl Branch/Commit ID: d5757ab1f3aad3c542950e1dbe8f9d2eec74bede
	structural-variants-pair.cwl	https://github.com/mskcc/argos-cwl.git Path: modules/pair/structural-variants-pair.cwl Branch/Commit ID: 46eddf1e191352cad5e95dd3c24eeae3738da485
	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: ad948b2691ef7f0f34de38f0102c3cd6f5182b29
	strelka workflow	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/strelka_and_post_processing.cwl Branch/Commit ID: 2decd55996b912feb48be5db1b052aa3274ee405