Identifying novel constrained elements by exploiting biased substitution patterns.
Identifying novel constrained elements by exploiting biased substitution patterns.
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DOI：
10.1093/bioinformatics/btp190
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发表时间：
2009-06-15
期刊：
影响因子：
--
通讯作者：
Xie X
中科院分区：
文献类型：
--
作者：
Garber M;Guttman M;Clamp M;Zody MC;Friedman N;Xie X
动机：比较密切相关物种的基因组提供了一个强大的工具来识别参考基因组中的功能元件。已经开发了许多方法来识别跨物种的保守序列;然而，现有的方法仅将保守性建模为突变率的降低，而忽略了对突变模式的选择作用。结果如下：我们提出了一种新的方法，利用深度测序的分支，以确定进化选择的发现不仅签名率为基础的保护，但也进行自然选择的序列特征的替代模式。我们描述了一种新的统计方法建模有偏见的核苷酸取代，一个学习算法推断位点特异性的取代偏见直接从序列比对和隐马尔可夫模型检测的约束条件下的元素，其特征在于有偏见的取代。我们表明，新的方法可以识别显着更多的退化约束序列比基于速率的方法。将其应用于ENCODE区域，我们识别出这些区域中多达10.2%处于选择中。可用性：这些算法是在一个名为SiPhy的Java软件包中实现的，该软件包可在http://www.broadinstitute.org/science/software/上免费获得。联系方式：xhx@ics.uci.edu补充信息：补充数据可从生物信息学在线网站获得。
Motivation: Comparing the genomes from closely related species provides a powerful tool to identify functional elements in a reference genome. Many methods have been developed to identify conserved sequences across species; however, existing methods only model conservation as a decrease in the rate of mutation and have ignored selection acting on the pattern of mutations. Results: We present a new approach that takes advantage of deeply sequenced clades to identify evolutionary selection by uncovering not only signatures of rate-based conservation but also substitution patterns characteristic of sequence undergoing natural selection. We describe a new statistical method for modeling biased nucleotide substitutions, a learning algorithm for inferring site-specific substitution biases directly from sequence alignments and a hidden Markov model for detecting constrained elements characterized by biased substitutions. We show that the new approach can identify significantly more degenerate constrained sequences than rate-based methods. Applying it to the ENCODE regions, we identify as much as 10.2% of these regions are under selection. Availability: The algorithms are implemented in a Java software package, called SiPhy, freely available at http://www.broadinstitute.org/science/software/. Contact: xhx@ics.uci.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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