首页> 美国卫生研究院文献>BMC Genomics >A comparative study of SVDquartets and other coalescent-based species tree estimation methods
【2h】

A comparative study of SVDquartets and other coalescent-based species tree estimation methods

机译:SVD四重奏与其他基于联盟的物种树估计方法的比较研究

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。

摘要

BackgroundSpecies tree estimation is challenging in the presence of incomplete lineage sorting (ILS), which can make gene trees different from the species tree. Because ILS is expected to occur and the standard concatenation approach can return incorrect trees with high support in the presence of ILS, "coalescent-based" summary methods (which first estimate gene trees and then combine gene trees into a species tree) have been developed that have theoretical guarantees of robustness to arbitrarily high amounts of ILS. Some studies have suggested that summary methods should only be used on "c-genes" (i.e., recombination-free loci) that can be extremely short (sometimes fewer than 100 sites). However, gene trees estimated on short alignments can have high estimation error, and summary methods tend to have high error on short c-genes. To address this problem, Chifman and Kubatko introduced SVDquartets, a new coalescent-based method. SVDquartets takes multi-locus unlinked single-site data, infers the quartet trees for all subsets of four species, and then combines the set of quartet trees into a species tree using a quartet amalgamation heuristic. Yet, the relative accuracy of SVDquartets to leading coalescent-based methods has not been assessed.
机译:背景技术在存在不完整谱系分选(ILS)的情况下,物种树估计具有挑战性,这可能会使基因树与物种树有所不同。因为预期会发生ILS,并且标准连接方法可以在ILS存在的情况下在高支持下返回不正确的树,所以已经开发了“基于聚结”的汇总方法(先估算基因树,然后将基因树组合为树)。具有对任意数量ILS的鲁棒性的理论保证。一些研究表明,汇总方法只能用于可能非常短(有时少于100个位点)的“ c基因”(即无重组基因座)。然而,在短序列比对中估计的基因树可能具有较高的估计误差,摘要方法在短c基因上往往具有较高的误差。为了解决这个问题,Chifman和Kubatko引入了一种基于合并的新方法SVDquartets。 SVDquartets获取多位点未链接的单站点数据,为四个物种的所有子集推断出四方树,然后使用四方合并启发法将四方树的集合合并为树。但是,尚未评估SVDquartets与领先的基于合并方法的相对准确性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号