Explore Workflows

Runs ChIP-Seq BioWardrobe basic analysis with single-end data file.

https://github.com/Barski-lab/workflows.git

Path: workflows/chipseq-se.cwl

Branch/Commit ID: b8e28a017f7b1a2900ec0fd3b3549f123f0c91b4

https://github.com/common-workflow-language/cwltool.git

Path: tests/wf/1st-workflow.cwl

Branch/Commit ID: d6000d32f6c8fbd26421a2d30d79b28901d58fb0

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/count-lines15-wf.cwl

Branch/Commit ID: a0f2d38e37ff51721fdeaf993bb2ab474b17246b

https://github.com/genome/analysis-workflows.git

Path: definitions/pipelines/alignment_exome.cwl

Branch/Commit ID: 27dcb1ae121be6a23057b74332b8c752ea425735

https://github.com/common-workflow-language/cwltool.git

Path: cwltool/schemas/v1.0/v1.0/step-valuefrom2-wf.cwl

Branch/Commit ID: ae401a813472ca453a99ad067a5e6fc3bd71112b

https://github.com/genome/analysis-workflows.git

Path: definitions/pipelines/panel_of_normals.cwl

Branch/Commit ID: f9600f9959acdc30259ba7e64de61104c9b01f0b

https://github.com/genome/analysis-workflows.git

Path: definitions/pipelines/detect_variants_mouse.cwl

Branch/Commit ID: 27dcb1ae121be6a23057b74332b8c752ea425735

https://github.com/genome/analysis-workflows.git

Path: definitions/pipelines/alignment_exome_mouse.cwl

Branch/Commit ID: 27dcb1ae121be6a23057b74332b8c752ea425735

https://github.com/genome/analysis-workflows.git

Path: definitions/subworkflows/strelka_process_vcf.cwl

Branch/Commit ID: 6949082038c1ad36d6e9848b97a2537aef2d3805

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: 4dcc405133f22c63478b6091fb5f591b6be8950f