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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: f3e44d3b0f198cf5245c49011124dc3b6c2b06fd

https://github.com/ncbi/pgap.git

Path: task_types/tt_kmer_ref_compare_wnode.cwl

Branch/Commit ID: 1c1f5aa0b0dfe5b07beb8ccb0c0e3c073a9fb592

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

Path: definitions/subworkflows/varscan.cwl

Branch/Commit ID: c235dc6d623879a6c4f5fb307f545c9806eb2d23

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

Path: definitions/subworkflows/molecular_alignment.cwl

Branch/Commit ID: 18600518ce6539a2e29c1707392a4c5da5687fa3

Proteins - predict, cluster, identify, annotate

https://github.com/MG-RAST/pipeline.git

Path: CWL/Workflows/protein-annotation.workflow.cwl

Branch/Commit ID: 721aaf285e1848c3c52da38a1fed95192aeff8f4

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

Path: definitions/pipelines/somatic_exome.cwl

Branch/Commit ID: e2a34d2b8c406db9aed8e49e8bdcf36f51444379

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

Path: definitions/subworkflows/cellranger_mkfastq_and_count.cwl

Branch/Commit ID: a93be3183c2218ee50f13ae2675dd1cde563fdbc

Derive array element coordinates and archive data generated in derivation process.

https://github.com/gammasim/workflows.git

Path: workflows/DeriveArrayElementCoordinates.cwl

Branch/Commit ID: 13a1a949db93afa18ffe8180ff9549e395184e4b

Runs RNA-Seq BioWardrobe basic analysis with strand specific pair-end data file.

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

Path: workflows/rnaseq-pe-dutp.cwl

Branch/Commit ID: e706ffe742cfdf713c4315ab2fb56d07f7e688cb

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

Path: cwltool/schemas/v1.0/v1.0/count-lines4-wf.cwl

Branch/Commit ID: e8b3565a008d95859fc44227987a54e6a53a8c29