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基因组进化计算机模拟的进展:通过近似贝叶斯计算实现更现实的进化基因组学分析。

Advances in computer simulation of genome evolution: toward more realistic evolutionary genomics analysis by approximate bayesian computation.

作者信息

Arenas Miguel

机构信息

Centre for Molecular Biology "Severo Ochoa", Consejo Superior de Investigaciones Científicas (CSIC), Universidad Autónoma de Madrid (CSIC-UAM), C/Nicolás Cabrera, 1, Cantoblanco, 28049, Madrid, Spain,

出版信息

J Mol Evol. 2015 Apr;80(3-4):189-92. doi: 10.1007/s00239-015-9673-0. Epub 2015 Mar 26.

Abstract

NGS technologies present a fast and cheap generation of genomic data. Nevertheless, ancestral genome inference is not so straightforward due to complex evolutionary processes acting on this material such as inversions, translocations, and other genome rearrangements that, in addition to their implicit complexity, can co-occur and confound ancestral inferences. Recently, models of genome evolution that accommodate such complex genomic events are emerging. This letter explores these novel evolutionary models and proposes their incorporation into robust statistical approaches based on computer simulations, such as approximate Bayesian computation, that may produce a more realistic evolutionary analysis of genomic data. Advantages and pitfalls in using these analytical methods are discussed. Potential applications of these ancestral genomic inferences are also pointed out.

摘要

新一代测序(NGS)技术能够快速且低成本地生成基因组数据。然而,由于作用于这些物质的复杂进化过程,如倒位、易位和其他基因组重排,祖先基因组推断并非那么简单直接。这些重排除了本身具有内在复杂性外,还可能同时发生并混淆祖先推断。最近,能够适应此类复杂基因组事件的基因组进化模型正在兴起。本文探讨了这些新颖的进化模型,并建议将其纳入基于计算机模拟的稳健统计方法,如近似贝叶斯计算,这可能会对基因组数据进行更现实的进化分析。文中讨论了使用这些分析方法的优点和缺陷。还指出了这些祖先基因组推断的潜在应用。

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