QR-STAR: A Polynomial-Time Statistically Consistent Method for Rooting Species Trees Under the Coalescent.

IF 1.4 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS
Journal of Computational Biology Pub Date : 2023-11-01 Epub Date: 2023-10-30 DOI:10.1089/cmb.2023.0185
Yasamin Tabatabaee, Sebastien Roch, Tandy Warnow
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引用次数: 0

Abstract

We address the problem of rooting an unrooted species tree given a set of unrooted gene trees, under the assumption that gene trees evolve within the model species tree under the multispecies coalescent (MSC) model. Quintet Rooting (QR) is a polynomial time algorithm that was recently proposed for this problem, which is based on the theory developed by Allman, Degnan, and Rhodes that proves the identifiability of rooted 5-taxon trees from unrooted gene trees under the MSC. However, although QR had good accuracy in simulations, its statistical consistency was left as an open problem. We present QR-STAR, a variant of QR with an additional step and a different cost function, and prove that it is statistically consistent under the MSC. Moreover, we derive sample complexity bounds for QR-STAR and show that a particular variant of it based on "short quintets" has polynomial sample complexity. Finally, our simulation study under a variety of model conditions shows that QR-STAR matches or improves on the accuracy of QR. QR-STAR is available in open-source form on github.

QR-STAR:一种聚结状态下种树生根的多项式时间统计一致方法。
我们在假设基因树在多物种联合(MSC)模型下的模型物种树内进化的情况下,在给定一组未标记基因树的情况下解决了未标记物种树的生根问题。五元寻根(QR)是最近针对这个问题提出的一种多项式时间算法,该算法基于Allman、Degnan和Rhodes开发的理论,该理论证明了在MSC下,有根的5分类单元树与未命名的基因树的可识别性。然而,尽管QR在模拟中具有良好的准确性,但其统计一致性仍然是一个悬而未决的问题。我们提出了QR-STAR,这是QR的一种变体,具有额外的步骤和不同的成本函数,并证明了它在MSC下的统计一致性。此外,我们推导了QR-STAR的样本复杂度边界,并证明了它的一个基于“短五元组”的特定变体具有多项式样本复杂度。最后,我们在各种模型条件下的仿真研究表明,QR-STAR匹配或提高了QR的准确性。QR-STAR在github上以开源形式提供。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Computational Biology
Journal of Computational Biology 生物-计算机:跨学科应用
CiteScore
3.60
自引率
5.90%
发文量
113
审稿时长
6-12 weeks
期刊介绍: Journal of Computational Biology is the leading peer-reviewed journal in computational biology and bioinformatics, publishing in-depth statistical, mathematical, and computational analysis of methods, as well as their practical impact. Available only online, this is an essential journal for scientists and students who want to keep abreast of developments in bioinformatics. Journal of Computational Biology coverage includes: -Genomics -Mathematical modeling and simulation -Distributed and parallel biological computing -Designing biological databases -Pattern matching and pattern detection -Linking disparate databases and data -New tools for computational biology -Relational and object-oriented database technology for bioinformatics -Biological expert system design and use -Reasoning by analogy, hypothesis formation, and testing by machine -Management of biological databases
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