Choi Hyungwon, Nesvizhskii Alexey I, Ghosh Debashis, Qin Zhaohui S
Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA.
Bioinformatics. 2009 Jul 15;25(14):1715-21. doi: 10.1093/bioinformatics/btp312. Epub 2009 May 14.
Chromatin immunoprecipitation (ChIP) experiments followed by array hybridization, or ChIP-chip, is a powerful approach for identifying transcription factor binding sites (TFBS) and has been widely used. Recently, massively parallel sequencing coupled with ChIP experiments (ChIP-seq) has been increasingly used as an alternative to ChIP-chip, offering cost-effective genome-wide coverage and resolution up to a single base pair. For many well-studied TFs, both ChIP-seq and ChIP-chip experiments have been applied and their data are publicly available. Previous analyses have revealed substantial technology-specific binding signals despite strong correlation between the two sets of results. Therefore, it is of interest to see whether the two data sources can be combined to enhance the detection of TFBS.
In this work, hierarchical hidden Markov model (HHMM) is proposed for combining data from ChIP-seq and ChIP-chip. In HHMM, inference results from individual HMMs in ChIP-seq and ChIP-chip experiments are summarized by a higher level HMM. Simulation studies show the advantage of HHMM when data from both technologies co-exist. Analysis of two well-studied TFs, NRSF and CCCTC-binding factor (CTCF), also suggests that HHMM yields improved TFBS identification in comparison to analyses using individual data sources or a simple merger of the two.
Source code for the software ChIPmeta is freely available for download at http://www.umich.edu/~hwchoi/HHMMsoftware.zip, implemented in C and supported on linux.
染色质免疫沉淀（ChIP）实验结合芯片杂交，即ChIP-chip，是一种识别转录因子结合位点（TFBS）的强大方法，已被广泛应用。最近，大规模平行测序与ChIP实验相结合（ChIP-seq）越来越多地被用作ChIP-chip的替代方法，它能以具有成本效益的方式实现全基因组覆盖，分辨率可达单碱基对。对于许多已深入研究的转录因子，ChIP-seq和ChIP-chip实验都已开展，且其数据可公开获取。先前的分析表明，尽管两组结果之间有很强的相关性，但仍存在大量技术特异性的结合信号。因此，研究这两种数据源能否结合以增强TFBS的检测很有意义。
在这项工作中，提出了层次隐马尔可夫模型（HHMM）来结合ChIP-seq和ChIP-chip的数据。在HHMM中，ChIP-seq和ChIP-chip实验中各个隐马尔可夫模型的推断结果由一个更高层次的隐马尔可夫模型进行汇总。模拟研究表明，当两种技术的数据共存时，HHMM具有优势。对两个已深入研究的转录因子，即神经元限制性沉默因子（NRSF）和CCCTC结合因子（CTCF）的分析也表明，与使用单个数据源或简单合并两者的分析相比，HHMM在TFBS识别方面有改进。
软件ChIPmeta的源代码可从http://www.umich.edu/~hwchoi/HHMMsoftware.zip免费下载，用C语言实现，支持Linux系统。
