JAMIE: A software tool for jointly analyzing multiple ChIP-chip experiments

Hao Wu, Hongkai Ji

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Chromatin immunoprecipitation followed by genome tiling array hybridization (ChIP-chip) is a powerful approach to map transcription factor binding sites (TFBSs). Similar to other high-throughput genomic technologies, ChIP-chip often produces noisy data. Distinguishing signals from noise in these data is challenging. ChIP-chip data in public databases are rapidly growing. It is becoming more and more common that scientists can find multiple data sets for the same transcription factor in different biological contexts or data for different transcription factors in the same biological context. When these related experiments are analyzed together, binding site detection can be improved by borrowing information across data sets. This chapter introduces a computational tool JAMIE for Jointly Analyzing Multiple ChIP-chip Experiments. JAMIE is based on a hierarchical mixture model, and it is implemented as an R package. Simulation and real data studies have shown that it can significantly increase sensitivity and specificity of TFBS detection compared to existing algorithms. The purpose of this chapter is to describe how the JAMIE package can be used to perform the integrative data analysis.

Original languageEnglish (US)
Title of host publicationNext Generation Microarray Bioinformatics
Subtitle of host publicationMethods and Protocols
EditorsJunbai Wang, Tianhai Tian, Aik Choon Tan
Pages363-375
Number of pages13
DOIs
StatePublished - 2012

Publication series

NameMethods in Molecular Biology
Volume802
ISSN (Print)1064-3745

Keywords

  • ChIP-chip
  • Data integration
  • Tiling array
  • Transcription factor binding site

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics

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