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Efficient error correction algorithms for gene tree reconciliation based on duplication, duplication and loss, and deep coalescence

机译:基于重复,重复和丢失以及深度合并的基因树协调的高效纠错算法

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BackgroundGene tree - species tree reconciliation problems infer the patterns and processes of gene evolution within a species tree. Gene tree parsimony approaches seek the evolutionary scenario that implies the fewest gene duplications, duplications and losses, or deep coalescence (incomplete lineage sorting) events needed to reconcile a gene tree and a species tree. While a gene tree parsimony approach can be informative about genome evolution and phylogenetics, error in gene trees can profoundly bias the results.ResultsWe introduce efficient algorithms that rapidly search local Subtree Prune and Regraft (SPR) or Tree Bisection and Reconnection (TBR) neighborhoods of a given gene tree to identify a topology that implies the fewest duplications, duplication and losses, or deep coalescence events. These algorithms improve on the current solutions by a factor of n for searching SPR neighborhoods and n 2 for searching TBR neighborhoods, where n is the number of taxa in the given gene tree. They provide a fast error correction protocol for ameliorating the effects of gene tree error by allowing small rearrangements in the topology to improve the reconciliation cost. We also demonstrate a simple protocol to use the gene rearrangement algorithm to improve gene tree parsimony phylogenetic analyses.ConclusionsThe new gene tree rearrangement algorithms provide a fast method to address gene tree error. They do not make assumptions about the underlying processes of genome evolution, and they are amenable to analyses of large-scale genomic data sets. These algorithms are also easily incorporated into gene tree parsimony phylogenetic analyses, potentially producing more credible estimates of reconciliation cost.
机译:背景基因树-物种树协调问题推断物种树内基因进化的模式和过程。基因树简约方法寻求一种进化场景,该场景暗示了基因树和物种树所需的最少的基因重复,重复和丢失或深度合并(不完整的谱系排序)事件。虽然基因树简约方法可以提供有关基因组进化和系统发育信息的信息,但基因树中的错误可以对结果产生重大影响。结果我们引入了有效的算法,可以快速搜索当地的子树修剪和移植(SPR)或树对分与重新连接(TBR)邻域。一个给定的基因树,以识别一种意味着最少重复,重复和丢失或深度合并事件的拓扑。这些算法在当前解决方案上改进了n倍,用于搜索SPR邻域和n 2 来搜索TBR邻域,其中n是给定基因树中的分类单元数。它们提供了一种快速错误校正协议,可通过允许拓扑中的较小重排来改善对账成本,从而改善基因树错误的影响。我们还演示了使用基因重排算法改进基因树简约性系统发育分析的简单协议。结论新的基因树重排算法提供了一种解决基因树错误的快速方法。他们没有对基因组进化的潜在过程做任何假设,并且适合于大规模基因组数据集的分析。这些算法还可以轻松地整合到基因树简约系统发育分析中,从而可能产生更可靠的对账成本估算。

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