Gene finding with a hidden Markov model of genome structure and evolution
Gene finding with a hidden Markov model of genome structure and evolution
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DOI：
10.1093/bioinformatics/19.2.219
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发表时间：
2003-01-22
期刊：
影响因子：
5.8
通讯作者：
Hein, J
中科院分区：
文献类型：
--
作者：
Pedersen, JS;Hein, J
动机：越来越多的基因组被测序。因此，功能区域之间的进化模式差异可以在一整套生物体的全基因组范围内观察到。在基因组注释的过程中，可以利用不同功能区域的不同进化模式。现有的比较基因finders进化的建模留下了改进的空间。结果：基因组结构和进化的概率模型的设计。这种类型的模型被称为进化隐马尔可夫模型（EHMM），是由一个HMM和一组特定区域的进化模型的基础上的系统发育树。所有的参数都可以通过最大似然估计，包括系统发育树。它可以处理任何数量的对齐的基因组，使用它们的系统发育树来模拟进化相关性。用于处理模型的所有算法的时间复杂度在比对长度和基因组数目上是线性的。将该模型应用于基因发现问题。模拟序列进化的好处在一系列模拟和一组正交人类/小鼠基因pairs. Available：免费提供在互联网上的www服务器：http://www.birc.dk/Software/evogeneContact：jsp@daimi.au.dk。
Motivation: A growing number of genomes are sequenced. The differences in evolutionary pattern between functional regions can thus be observed genome-wide in a whole set of organisms. The diverse evolutionary pattern of different functional regions can be exploited in the process of genomic annotation. The modelling of evolution by the existing comparative gene finders leaves room for improvement.Results: A probabilistic model of both genome structure and evolution is designed. This type of model is called an Evolutionary Hidden Markov Model (EHMM), being composed of an HMM and a set of region-specific evolutionary models based on a phylogenetic tree. All parameters can be estimated by maximum likelihood, including the phylogenetic tree. It can handle any number of aligned genomes, using their phylogenetic tree to model the evolutionary correlations. The time complexity of all algorithms used for handling the model are linear in alignment length and genome number. The model is applied to the problem of gene finding. The benefit of modelling sequence evolution is demonstrated both in a range of simulations and on a set of orthologous human/mouse gene pairs.Availability: Free availability over the Internet on www server: http://www.birc.dk/Software/evogeneContact: jsp@daimi.au.dk.