Recco: recombination analysis using cost optimization
Recco: recombination analysis using cost optimization
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DOI：
10.1093/bioinformatics/btl057
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发表时间：
2006-05-01
期刊：
影响因子：
5.8
通讯作者：
Lengauer, T
中科院分区：
文献类型：
--
作者：
Maydt, J;Lengauer, T
动机：进化在许多病原体的进化中起着重要作用，如艾滋病毒或疟疾。尽管大量的先前工作，仍然是一个迫切需要的高效和有效的方法检测重组和分析重组sequence.Results：我们介绍Recco，一种新的快速方法，给出了多序列比对，得分的成本获得的序列之一，从其他的突变和重组。该算法带有一个说明性的可视化工具，用于定位重组断点。我们分析了序列比对相对于所有选择的参数α加权重组成本对突变成本。对所得成本曲线的分析产生了关于哪个序列可能是重组的额外信息。在随机系谱中，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的表现明显优于Geneconv。
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.