Computational methods for Gene Orthology inference
Computational methods for Gene Orthology inference
复制标题
DOI：
10.1093/bib/bbr030
复制
发表时间：
2011-09-01
影响因子：
9.5
通讯作者：
Koonin, Eugene V.
中科院分区：
文献类型：
--
作者：
Kristensen, David M.;Wolf, Yuri I.;Koonin, Eugene V.
准确推断直向同源基因是大多数比较基因组学研究的前提，也是新基因组功能注释的重要内容。直向同源基因组的鉴定通常涉及系统发育树分析、基于序列保守性的启发式算法、同线性分析或这些方法的某种组合。最直接的基于树的方法通常依赖于个体基因树与物种树的比较。一旦这两棵树被准确地构建，直系同源物就被直系同源学的定义直接识别为那些在最近的起源点上通过物种形成而不是基因复制而相关的同源物。虽然原则上对于同源性鉴定是理想的，但对于大量基因和基因组构建系统发育树在计算上是昂贵的，并且它们经常包含错误，特别是在大的进化距离下。此外，在许多生物中，特别是原核生物和病毒，进化似乎并没有遵循简单的“树状”模式，这使得传统的树和解不适用。其他启发式方法将可能的直系同源物鉴定为一组生物体中最接近的同源对或基因组。这些方法比基于树的方法更快，更容易自动化，由图论算法提供的高效实现可以比较数千个基因组。这两种方法的比较表明，尽管概念上的差异，它们产生相似的直向同源物，特别是在短的进化距离。同线性还可以帮助鉴定直向同源物。通常，基于树、基于序列相似性和基于同线性的方法可以组合成灵活的混合方法。
Accurate inference of orthologous genes is a pre-requisite for most comparative genomics studies, and is also important for functional annotation of new genomes. Identification of orthologous gene sets typically involves phylogenetic tree analysis, heuristic algorithms based on sequence conservation, synteny analysis, or some combination of these approaches. The most direct tree-based methods typically rely on the comparison of an individual gene tree with a species tree. Once the two trees are accurately constructed, orthologs are straightforwardly identified by the definition of orthology as those homologs that are related by speciation, rather than gene duplication, at their most recent point of origin. Although ideal for the purpose of orthology identification in principle, phylogenetic trees are computationally expensive to construct for large numbers of genes and genomes, and they often contain errors, especially at large evolutionary distances. Moreover, in many organisms, in particular prokaryotes and viruses, evolution does not appear to have followed a simple 'tree-like' mode, which makes conventional tree reconciliation inapplicable. Other, heuristic methods identify probable orthologs as the closest homologous pairs or groups of genes in a set of organisms. These approaches are faster and easier to automate than tree-based methods, with efficient implementations provided by graph-theoretical algorithms enabling comparisons of thousands of genomes. Comparisons of these two approaches show that, despite conceptual differences, they produce similar sets of orthologs, especially at short evolutionary distances. Synteny also can aid in identification of orthologs. Often, tree-based, sequence similarity- and synteny-based approaches can be combined into flexible hybrid methods.