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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: 799575ce58746813f066a665adeacdda252d8cab

https://github.com/NCI-GDC/gdc-mirnaseq-cwl.git

Path: workflows/mirnaseq_automation/mirna_profiling.cwl

Branch/Commit ID: 963f6d502da2c4f152c1654e94008ccf8f6d0db3

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

Path: definitions/subworkflows/align_sort_markdup.cwl

Branch/Commit ID: 74647cc0f1abac4ee22950cfa89c44cf2ca3cffd

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

Path: definitions/subworkflows/pindel_region.cwl

Branch/Commit ID: 54846feabbf008c1946db2a86d87252e0edd95b0

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

Path: definitions/subworkflows/phase_vcf.cwl

Branch/Commit ID: 74647cc0f1abac4ee22950cfa89c44cf2ca3cffd

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

Path: definitions/pipelines/gathered_cle_aml_trio.cwl

Branch/Commit ID: e56f1024306aeb427d8aae2fff715ed2e8b8f86f

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

Path: definitions/subworkflows/pindel_cat.cwl

Branch/Commit ID: 97572e3a088d79f6a4166385f79e79ea77b11470

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

Path: definitions/subworkflows/qc_exome.cwl

Branch/Commit ID: 8c4e7372247a7f4ed9ed478ef8ea1d239bc88af0

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

Path: definitions/subworkflows/sequence_to_bqsr.cwl

Branch/Commit ID: 8c4e7372247a7f4ed9ed478ef8ea1d239bc88af0

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

Path: definitions/subworkflows/bam_readcount.cwl

Branch/Commit ID: 509938802c5e42bb8084c6a5a26ab6425c60e69a