BayesPeak: Bayesian analysis of ChIP-seq data.
BayesPeak: Bayesian analysis of ChIP-seq data.
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DOI：
10.1186/1471-2105-10-299
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发表时间：
2009-09-21
影响因子：
3
通讯作者：
Tavaré S
中科院分区：
文献类型：
--
作者：
Spyrou C;Stark R;Lynch AG;Tavaré S
高通量测序技术已广泛应用于蛋白质与DNA相互作用的研究。染色质免疫沉淀，然后对所得样品进行测序，产生大量数据，可用于绘制基因组特征，如转录因子结合位点和组蛋白修饰。我们提出的统计算法BayesPeak使用全贝叶斯隐马尔可夫模型来检测基因组中的富集位置。该结构适应Solexa/Illumina测序数据的自然特征，并允许不同区域的reads丰度过度分散。此外，可以在分析中加入一个控制样本来解释实验和序列偏差。采用马尔可夫链蒙特卡罗算法估计模型参数的后验分布，并使用后验概率检测感兴趣的位置。我们提出了一种灵活的方法来识别ChIP-seq读取峰，适用于转录因子结合和组蛋白修饰数据。我们的方法估计可用于下游分析的富集概率。该方法使用实验验证的数据进行评估，并显示出具有低假阳性率的高置信度呼叫。
High-throughput sequencing technology has become popular and widely used to study protein and DNA interactions. Chromatin immunoprecipitation, followed by sequencing of the resulting samples, produces large amounts of data that can be used to map genomic features such as transcription factor binding sites and histone modifications. Our proposed statistical algorithm, BayesPeak, uses a fully Bayesian hidden Markov model to detect enriched locations in the genome. The structure accommodates the natural features of the Solexa/Illumina sequencing data and allows for overdispersion in the abundance of reads in different regions. Moreover, a control sample can be incorporated in the analysis to account for experimental and sequence biases. Markov chain Monte Carlo algorithms are applied to estimate the posterior distributions of the model parameters, and posterior probabilities are used to detect the sites of interest. We have presented a flexible approach for identifying peaks from ChIP-seq reads, suitable for use on both transcription factor binding and histone modification data. Our method estimates probabilities of enrichment that can be used in downstream analysis. The method is assessed using experimentally verified data and is shown to provide high-confidence calls with low false positive rates.
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