Explore Workflows

View already parsed workflows here or click here to add your own

Graph	Name	Retrieved From	View
	schemadef-wf.cwl	https://github.com/common-workflow-language/cwltool.git Path: cwltool/schemas/v1.0/v1.0/schemadef-wf.cwl Branch/Commit ID: 20d664eff23e59aa57908345bfdb1ceeab3438f2
	mut.cwl	https://github.com/common-workflow-language/cwltool.git Path: tests/wf/mut.cwl Branch/Commit ID: 9a8e654a91ea5d26e8452dd1cecf3faf22b7a12e
	scatter-wf4.cwl#main	https://github.com/common-workflow-language/cwltool.git Path: tests/wf/scatter-wf4.cwl Branch/Commit ID: a70a83fe14a100cd16e2402ec17b2904f5eeb17d Packed ID: main
	assm_assm_blastn_wnode	https://github.com/ncbi/pgap.git Path: task_types/tt_assm_assm_blastn_wnode.cwl Branch/Commit ID: 17bae57a1f00f5c6db8f3a82d86262f12b8153cf
	umi molecular alignment workflow	https://github.com/genome/analysis-workflows.git Path: definitions/subworkflows/molecular_qc.cwl Branch/Commit ID: f77a920bcc73f6cfdb091eed75a149d02cd8a263
	alignment for nonhuman with qc	https://github.com/genome/analysis-workflows.git Path: definitions/pipelines/alignment_wgs_nonhuman.cwl Branch/Commit ID: 2decd55996b912feb48be5db1b052aa3274ee405
	RNA-Seq alignment and transcript/gene abundance workflow	https://github.com/genome/analysis-workflows.git Path: definitions/pipelines/rnaseq.cwl Branch/Commit ID: 1249b5d4e23d57ca5e3b8ad6d8e5f10ddb019f68
	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: 5f4f9c63a4183eabd10e11d9e86cf054ef7ced05
	step-valuefrom-wf.cwl	https://github.com/common-workflow-language/cwl-v1.2.git Path: tests/step-valuefrom-wf.cwl Branch/Commit ID: a5073143db4155e05df8d2e7eb59d9e62acd65a5
	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: 9d5cbdd3ea0bb417518115d8092584254598a440