Detecting gene clusters under evolutionary constraint in a large number of genomes
Detecting gene clusters under evolutionary constraint in a large number of genomes
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DOI：
10.1093/bioinformatics/btp027
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发表时间：
2009-03-01
期刊：
影响因子：
5.8
通讯作者：
Xin, Dong
中科院分区：
文献类型：
--
作者：
Ling, Xu;He, Xin;Xin, Dong
动机：在多个基因组中保守的基因空间集群为基因功能和基因组组织进化提供了重要线索。现有的识别这些聚类的方法经常做出限制性的假设，比如基因顺序的精确守恒，并依赖于启发式算法。结果：我们开发了一种基于“基因团队”模型的非常有效的算法，该算法允许集群中的基因以不同的顺序出现。这使我们能够在大量基因组中检测灵活进化约束下的保守基因簇。我们的统计评估纳入了基因组之间的进化关系，这是大多数先前研究中缺失的一个关键方面。我们对133种细菌基因组进行了大规模分析。我们的结果证实了我们的方法是发现功能相关基因的有效方法。与已知操纵子的比较以及对我们预测的簇的结构特性的分析表明，操纵子是一个重要的约束来源，但也有其他力量决定基因的进化顺序和排列。利用我们的方法，我们预测了细菌中许多特征不明显的基因的功能。我们在这里提出的结合算法和统计方法为系统地研究基因组背景的进化约束提供了一个严格的框架。可用性：本文的软件、数据和全部结果可在http://www.ews.uiuc.edu/similar到xuling/mcmusec上在线获得。
Motivation: Spatial clusters of genes conserved across multiple genomes provide important clues to gene functions and evolution of genome organization. Existing methods of identifying these clusters often made restrictive assumptions, such as exact conservation of gene order, and relied on heuristic algorithms.Results: We developed a very efficient algorithm based on a 'gene teams' model that allows genes in the clusters to appear in different orders. This allows us to detect conserved gene clusters under flexible evolutionary constraints in a large number of genomes. Our statistical evaluation incorporates the evolutionary relationship among genomes, a key aspect that has been missing in most previous studies. We conducted a large-scale analysis of 133 bacterial genomes. Our results confirm that our approach is an effective way of uncovering functionally related genes. The comparison with known operons and the analysis of the structural properties of our predicted clusters suggest that operons are an important source of constraint, but there are also other forces that determine evolution of gene order and arrangement. Using our method, we predicted functions of many poorly characterized genes in bacterial. The combined algorithmic and statistical methods we present here provide a rigorous framework for systematically studying evolutionary constraints of genomic contexts.Availability: The software, data and the full results of this article are available online at http://www.ews.uiuc.edu/similar to xuling/mcmusec.