Detecting differential peaks in ChIP-seq signals with ODIN
Detecting differential peaks in ChIP-seq signals with ODIN
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DOI：
10.1093/bioinformatics/btu722
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发表时间：
2014-12-15
期刊：
影响因子：
5.8
通讯作者：
Costa, Ivan G.
中科院分区：
文献类型：
--
作者：
Allhoff, Manuel;Sere, Kristin;Costa, Ivan G.
动机：从ChIP-seq数据中检测脱氧核糖核酸（DNA）蛋白相互作用的变化是揭示生物过程背后的调控网络的关键一步。这个问题最简单的变体是差分峰值调用（DPC）问题。在这里，人们必须找到在蛋白质与DNA相互作用的两种细胞条件下ChIP-seq信号变化的基因组区域。绝大多数峰值调用方法一次只能分析一个ChIP-seq信号，无法进行DPC。近年来，人们提出了几种基于这些峰值呼叫者与统计检验相结合的方法来检测数字差分表达式。然而，这些方法无法检测到蛋白质- dna相互作用的详细变化。结果：我们提出了一个一级差分峰值调用者（ODIN）；一种基于隐马尔可夫模型的ChIP-seq数据对差分峰（DPs）检测和分析方法。ODIN在一个集成的框架中执行基因组信号处理、峰值调用和p值计算。我们还提出了一种评价方法来比较ODIN与竞争方法。评价方法是基于相同细胞条件下DPs与表达变化的关联。我们基于转录因子、组蛋白修饰和模拟数据的几项ChIP-seq实验的实证研究表明，在大多数情况下，ODIN优于考虑的竞争方法。
Motivation: Detection of changes in deoxyribonucleic acid (DNA)protein interactions from ChIP-seq data is a crucial step in unraveling the regulatory networks behind biological processes. The simplest variation of this problem is the differential peak calling (DPC) problem. Here, one has to find genomic regions with ChIP-seq signal changes between two cellular conditions in the interaction of a protein with DNA. The great majority of peak calling methods can only analyze one ChIP-seq signal at a time and are unable to perform DPC. Recently, a few approaches based on the combination of these peak callers with statistical tests for detecting differential digital expression have been proposed. However, these methods fail to detect detailed changes of protein-DNA interactions.Results: We propose an One-stage DIffereNtial peak caller (ODIN); an Hidden Markov Model-based approach to detect and analyze differential peaks (DPs) in pairs of ChIP-seq data. ODIN performs genomic signal processing, peak calling and p-value calculation in an integrated framework. We also propose an evaluation methodology to compare ODIN with competing methods. The evaluation method is based on the association of DPs with expression changes in the same cellular conditions. Our empirical study based on several ChIP-seq experiments from transcription factors, histone modifications and simulated data shows that ODIN outperforms considered competing methods in most scenarios.