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A New Approach for the Joint Analysis of Multiple Chip-Seq Libraries with Application to Histone Modification

Author

Listed:
  • Ferguson John P.

    (Yale University)

  • Cho Judy H.

    (Yale University)

  • Zhao Hongyu

    (Yale University)

Abstract

Most approaches for analyzing ChIP-Seq data are focused on inferring exact protein binding sites from a single library. However, frequently multiple ChIP-Seq libraries derived from differing cell lines or tissue types from the same individual may be available. In such a situation, a separate analysis for each tissue or cell line may be inefficient. Here, we describe a novel method to analyze such data that intelligently uses the joint information from multiple related ChIP-Seq libraries. We present our method as a two-stage procedure. First, separate single cell line analysis is performed for each cell line. Here, we use a novel mixture regression approach to infer the subset of genes that are most likely to be involved in protein binding in each cell line. In the second step, we combine the separate single cell line analyses using an Empirical Bayes algorithm that implicitly incorporates inter-cell line correlation. We demonstrate the usefulness of our method using both simulated data, as well as real H3K4me3 and H3K27me3 histone methylation libraries.

Suggested Citation

  • Ferguson John P. & Cho Judy H. & Zhao Hongyu, 2012. "A New Approach for the Joint Analysis of Multiple Chip-Seq Libraries with Application to Histone Modification," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(3), pages 1-21, February.
    • Handle: RePEc:bpj:sagmbi:v:11:y:2012:i:3:n:1
    DOI: 10.1515/1544-6115.1660
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    Cited by:

    1. Hillary Koch & Cheryl A. Keller & Guanjue Xiang & Belinda Giardine & Feipeng Zhang & Yicheng Wang & Ross C. Hardison & Qunhua Li, 2022. "CLIMB: High-dimensional association detection in large scale genomic data," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    2. Shaun Mahony & Matthew D Edwards & Esteban O Mazzoni & Richard I Sherwood & Akshay Kakumanu & Carolyn A Morrison & Hynek Wichterle & David K Gifford, 2014. "An Integrated Model of Multiple-Condition ChIP-Seq Data Reveals Predeterminants of Cdx2 Binding," PLOS Computational Biology, Public Library of Science, vol. 10(3), pages 1-14, March.

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