BACKGROUND As the number of sequenced genomes grows, researchers have access to an increasingly rich source for discovering detailed evolutionary information. However, the computational technologies for inferring biologically important evolutionary events are not sufficiently developed. RESULTS We present algorithms to estimate the evolutionary time ( t MRS ) to the most recent substitution event from a multiple alignment column by using a probabilistic model of sequence evolution. As the confidence in estimated t MRS values varies depending on gap fractions and nucleotide patterns of alignment columns, we also compute the standard deviation σ of t MRS by using a dynamic programming algorithm. We identified a number of human genomic sites at which the last substitutions occurred between two speciation events in the human lineage with confidence. A large fraction of such sites have substitutions that occurred between the concestor nodes of Hominoidea and Euarchontoglires. We investigated the correlation between tissue-specific transcribed enhancers and the distribution of the sites with specific substitution time intervals, and found that brain-specific transcribed enhancers are threefold enriched in the density of substitutions in the human lineage relative to expectations. CONCLUSIONS We have presented algorithms to estimate the evolutionary time ( t MRS ) to the most recent substitution event from a multiple alignment column by using a probabilistic model of sequence evolution. Our algorithms will be useful for Evo-Devo studies, as they facilitate screening potential genomic sites that have played an important role in the acquisition of unique biological features by target species.

中文翻译：

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TMRS：一种用于计算从多对齐列到最近替换事件的时间的算法。

背景随着测序基因组数量的增长，研究人员可以获得越来越丰富的资源来发现详细的进化信息。然而，用于推断生物学上重要的进化事件的计算技术还没有得到充分发展。结果 我们提出了算法，通过使用序列进化的概率模型来估计从多重比对列到最近替换事件的进化时间 (t MRS )。由于估计的 t MRS 值的置信度取决于比对列的间隙分数和核苷酸模式，我们还通过使用动态规划算法计算 t MRS 的标准偏差 σ。我们确定了许多人类基因组位点，在这些位点上，人类谱系中的两个物种形成事件之间发生了最后一次替换。大部分此类站点在 Hominoidea 和 Euarchontoglires 的 constor 节点之间发生了替换。我们研究了组织特异性转录增强子与具有特定替代时间间隔的位点分布之间的相关性，发现大脑特异性转录增强子在人类谱系中的替代密度相对于预期值增加了三倍。结论 我们已经提出了算法，通过使用序列进化的概率模型来估计从多重比对列到最近替换事件的进化时间 (t MRS )。我们的算法将有助于 Evo-Devo 研究，因为它们有助于筛选潜在的基因组位点，这些位点在目标物种获得独特生物学特征方面发挥了重要作用。