CNV-guided multi-read allocation for ChIP-seq

Qi Zhang, Sündüz Keleş

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

MOTIVATION: In chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) and other short-read sequencing experiments, a considerable fraction of the short reads align to multiple locations on the reference genome (multi-reads). Inferring the origin of multi-reads is critical for accurately mapping reads to repetitive regions. Current state-of-the-art multi-read allocation algorithms rely on the read counts in the local neighborhood of the alignment locations and ignore the variation in the copy numbers of these regions. Copy-number variation (CNV) can directly affect the read densities and, therefore, bias allocation of multi-reads.

RESULTS: We propose cnvCSEM (CNV-guided ChIP-Seq by expectation-maximization algorithm), a flexible framework that incorporates CNV in multi-read allocation. cnvCSEM eliminates the CNV bias in multi-read allocation by initializing the read allocation algorithm with CNV-aware initial values. Our data-driven simulations illustrate that cnvCSEM leads to higher read coverage with satisfactory accuracy and lower loss in read-depth recovery (estimation). We evaluate the biological relevance of the cnvCSEM-allocated reads and the resultant peaks with the analysis of several ENCODE ChIP-seq datasets.

AVAILABILITY AND IMPLEMENTATION: Available at http://www.stat.wisc.edu/∼qizhang/

CONTACT: : qizhang@stat.wisc.edu or keles@stat.wisc.edu

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Original languageEnglish (US)
Pages (from-to)2860-2867
Number of pages8
JournalBioinformatics (Oxford, England)
Volume30
Issue number20
DOIs
Publication statusPublished - Oct 15 2014

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ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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