Phylogenetic network-assisted rooting of unrooted gene trees

IF 0.9 4区 数学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Jerzy Tiuryn, Natalia Rutecka, Paweł Górecki
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引用次数: 0

Abstract

Gene trees inferred from molecular sequence alignments are typically unrooted, and determining the most credible rooting edge is a classical problem in computational biology. One approach to solve this problem is unrooted reconciliation, where the rooting edge is postulated based on the split of the root from a given species tree. In this paper, we propose a novel variant of the gene tree rooting problem, where the gene tree root is inferred using a phylogenetic network of the species present in the gene tree. To obtain the best rooting, unrooted reconciliation can be applied, where the unrooted gene tree is jointly reconciled with a set of splits inferred from the network. However, the exponential size of the set induced by display trees of the network makes this approach computationally prohibitive. To address this, we propose a broader and easier-to-control set of splits based on the structural properties of the network. We then derive exact mathematical formulas for the rooting problem and propose two general rooting algorithms to handle cases where the input network does not meet the initial requirements. Our experimental study based on simulated gene trees and networks demonstrates that our algorithms infer gene tree rootings correctly or with a small error in most cases.

Abstract Image

系统发生网络辅助无根基因树生根
从分子序列比对中推断出的基因树通常是无根的,而确定最可信的根缘是计算生物学中的一个经典问题。解决这一问题的一种方法是无根调和,即根据给定物种树的根的分裂来假设根缘。在本文中,我们提出了基因树根问题的一种新变体,即利用基因树中存在的物种的系统发生网络推断基因树根。为了获得最佳的扎根效果,可以采用无根调和法,即将无根基因树与从网络中推断出的一组分裂进行联合调和。然而,网络显示树所诱导的集合的指数级大小使得这种方法的计算量过大。为了解决这个问题,我们根据网络的结构特性,提出了一个范围更广、更易于控制的拆分集。然后,我们得出了扎根问题的精确数学公式,并提出了两种通用扎根算法,以处理输入网络不符合初始要求的情况。我们基于模拟基因树和网络进行的实验研究表明,我们的算法在大多数情况下都能正确推断出基因树的根系或误差很小。
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来源期刊
Journal of Combinatorial Optimization
Journal of Combinatorial Optimization 数学-计算机:跨学科应用
CiteScore
2.00
自引率
10.00%
发文量
83
审稿时长
6 months
期刊介绍: The objective of Journal of Combinatorial Optimization is to advance and promote the theory and applications of combinatorial optimization, which is an area of research at the intersection of applied mathematics, computer science, and operations research and which overlaps with many other areas such as computation complexity, computational biology, VLSI design, communication networks, and management science. It includes complexity analysis and algorithm design for combinatorial optimization problems, numerical experiments and problem discovery with applications in science and engineering. The Journal of Combinatorial Optimization publishes refereed papers dealing with all theoretical, computational and applied aspects of combinatorial optimization. It also publishes reviews of appropriate books and special issues of journals.
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