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综合计算基因组学和蛋白质组学来揭示适应性进化。

Gathering computational genomics and proteomics to unravel adaptive evolution.

机构信息

REQUIMTE, Departamento de Química, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, Porto, Portugal.

出版信息

Evol Bioinform Online. 2007 Sep 6;3:207-9.

Abstract

A recent editorial in PLoS Biology by MacCallum and Hill (2006) pointed out the inappropriateness of studies evaluating signatures of positive selection based solely in single-site analyses. Therefore the rising number of articles claiming positive selection that have been recently published urges the question of how to improve the bioinformatics standards for reliably unravel positive selection? Deeper integrative efforts using state-of-the-art methodologies at the gene-level and protein-level are improving positive selection studies. Here we provide some computational guidelines to thoroughly document molecular adaptation.

摘要

最近在 PLoS Biology 杂志上发表的一篇社论中,MacCallum 和 Hill(2006)指出了仅基于单点分析评估正选择特征的研究的不适当性。因此,最近发表的大量声称正选择的文章引发了一个问题,即如何提高生物信息学标准以可靠地揭示正选择?在基因和蛋白质水平上使用最先进的方法进行更深入的综合研究正在改进正选择研究。在这里,我们提供了一些计算准则,以彻底记录分子适应。

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