Publication | Open Access
normR: Regime enrichment calling for ChIP-seq data
15
Citations
67
References
2016
Year
Unknown Venue
EngineeringAdvanced ComputingComputer ArchitectureMolecular BiologyOverall Read StatisticsGene Expression ProfilingNucleic Acid BiomarkersHardware ArchitectureData ScienceBiostatisticsParallel ComputingData ManagementSensitive NormalizationPeak EnrichmentBioinformaticsChromatinRegime EnrichmentCancer GenomicsParallel ProgrammingSystems BiologyMedicineData Modeling
Abstract ChIP-seq probes genome-wide localization of DNA-associated proteins. To mitigate technical biases ChIP-seq read densities are normalized to read densities obtained by a control. Our statistical framework “normR” achieves a sensitive normalization by accounting for the effect of putative protein-bound regions on the overall read statistics. Here, we demonstrate normR’s suitability in three studies: (i) calling enrichment for high (H3K4me3) and low (H3K36me3) signal-to-ratio data; (ii) identifying two previously undescribed H3K27me3 and H3K9me3 heterochromatic regimes of broad and peak enrichment; and (iii) calling differential H3K4me3 or H3K27me3-enrichment between HepG2 hepatocarcinoma cells and primary human Hepatocytes. normR is readily available on http://bioconductor.org/packages/normr
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