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Efficient detection and classification of epigenomic changes under multiple conditions

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  • Pedro L. Baldoni
  • Naim U. Rashid
  • Joseph G. Ibrahim

Abstract

Epigenomics, the study of the human genome and its interactions with proteins and other cellular elements, has become of significant interest in recent years. Such interactions have been shown to regulate essential cellular functions and are associated with multiple complex diseases. Therefore, understanding how these interactions may change across conditions is central in biomedical research. Chromatin immunoprecipitation followed by massively parallel sequencing (ChIP‐seq) is one of several techniques to detect local changes in epigenomic activity (peaks). However, existing methods for differential peak calling are not optimized for the diversity in ChIP‐seq signal profiles, are limited to the analysis of two conditions, or cannot classify specific patterns of differential change when multiple patterns exist. To address these limitations, we present a flexible and efficient method for the detection of differential epigenomic activity across multiple conditions. We utilize data from the ENCODE Consortium and show that the presented method, epigraHMM, exhibits superior performance to current tools and it is among the fastest algorithms available, while allowing the classification of combinatorial patterns of differential epigenomic activity and the characterization of chromatin regulatory states.

Suggested Citation

  • Pedro L. Baldoni & Naim U. Rashid & Joseph G. Ibrahim, 2022. "Efficient detection and classification of epigenomic changes under multiple conditions," Biometrics, The International Biometric Society, vol. 78(3), pages 1141-1154, September.
    • Handle: RePEc:bla:biomet:v:78:y:2022:i:3:p:1141-1154
    DOI: 10.1111/biom.13477
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    1. Raphaël Margueron & Danny Reinberg, 2011. "The Polycomb complex PRC2 and its mark in life," Nature, Nature, vol. 469(7330), pages 343-349, January.
    2. Efron B. & Tibshirani R. & Storey J.D. & Tusher V., 2001. "Empirical Bayes Analysis of a Microarray Experiment," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1151-1160, December.
    3. Sanjeev Shukla & Ersen Kavak & Melissa Gregory & Masahiko Imashimizu & Bojan Shutinoski & Mikhail Kashlev & Philipp Oberdoerffer & Rickard Sandberg & Shalini Oberdoerffer, 2011. "CTCF-promoted RNA polymerase II pausing links DNA methylation to splicing," Nature, Nature, vol. 479(7371), pages 74-79, November.
    4. Xiaoyu Liu & Chenfei Wang & Wenqiang Liu & Jingyi Li & Chong Li & Xiaochen Kou & Jiayu Chen & Yanhong Zhao & Haibo Gao & Hong Wang & Yong Zhang & Yawei Gao & Shaorong Gao, 2016. "Distinct features of H3K4me3 and H3K27me3 chromatin domains in pre-implantation embryos," Nature, Nature, vol. 537(7621), pages 558-562, September.
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