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Statistical Analysis in ChIP-seq-Related Applications

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Statistical Genomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2629))

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Abstract

Chromatin immunoprecipitation sequencing (ChIP-seq) has been widely performed to identify protein binding information along the genome. The sequencing protocol is quite flexible and mature to measure different types of protein binding as long as sequencing parameters are properly tailored to accommodate protein features. Two distinct types of protein binding are point-source-like binding by transcription factors and diffused-distribution binding by histone modifications. Consequently, statistical approaches have been proposed to address ChIP-seq-related questions according to different protein features. In this chapter, we briefly summarize statistical principles, approaches, and tools that are widely implemented in modeling ChIP-seq data, from raw data quality control to final result reporting. We discuss the key solutions in addressing eight routine questions in ChIP-seq applications. We also include discussion on approaches fitting unique data features in different ChIP-seq types. We hope this chapter will serve as a brief guide, especially for ChIP-seq beginners, to provide them with a high-level overview to understand and design processing plans for their ChIP-seq experiments.

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Authors and Affiliations

  1. Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA

    Mingxiang Teng

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  1. Mingxiang Teng
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Correspondence to Mingxiang Teng .

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Editors and Affiliations

  1. Moffitt Cancer Center, Tampa, FL, USA

    Brooke Fridley

  2. Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA

    Xuefeng Wang

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Teng, M. (2023). Statistical Analysis in ChIP-seq-Related Applications. In: Fridley, B., Wang, X. (eds) Statistical Genomics. Methods in Molecular Biology, vol 2629. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2986-4_9

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  • DOI: https://doi.org/10.1007/978-1-0716-2986-4_9

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