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Recco:使用成本优化的重组分析

Recco: recombination analysis using cost optimization.

作者信息

Maydt Jochen, Lengauer Thomas

机构信息

Max-Planck Institut für Informatik Saarbrücken, Germany.

出版信息

Bioinformatics. 2006 May 1;22(9):1064-71. doi: 10.1093/bioinformatics/btl057. Epub 2006 Feb 17.

Abstract

MOTIVATION

Recombination plays an important role in the evolution of many pathogens, such as HIV or malaria. Despite substantial prior work, there is still a pressing need for efficient and effective methods of detecting recombination and analyzing recombinant sequences.

RESULTS

We introduce Recco, a novel fast method that, given a multiple sequence alignment, scores the cost of obtaining one of the sequences from the others by mutation and recombination. The algorithm comes with an illustrative visualization tool for locating recombination breakpoints. We analyze the sequence alignment with respect to all choices of the parameter alpha weighting recombination cost against mutation cost. The analysis of the resulting cost curve yields additional information as to which sequence might be recombinant. On random genealogies Recco is comparable in its power of detecting recombination with the algorithm Geneconv (Sawyer, 1989). For specific relevant recombination scenarios Recco significantly outperforms Geneconv.

摘要

动机

重组在许多病原体(如艾滋病毒或疟疾)的进化中起着重要作用。尽管之前有大量工作,但仍迫切需要高效且有效的方法来检测重组并分析重组序列。

结果

我们引入了Recco,这是一种新颖的快速方法,给定一个多序列比对,它会对通过突变和重组从其他序列中获得其中一个序列的成本进行评分。该算法配有一个用于定位重组断点的直观可视化工具。我们针对参数α权衡重组成本与突变成本的所有选择来分析序列比对。对所得成本曲线的分析产生了关于哪个序列可能是重组序列的额外信息。在随机谱系上,Recco在检测重组的能力方面与Geneconv算法(索耶,1989年)相当。对于特定的相关重组场景,Recco明显优于Geneconv。

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