Locus-aware decomposition of gene trees with respect to polytomous species trees.

IF 1.7 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS
Algorithms for Molecular Biology Pub Date : 2018-06-04 eCollection Date: 2018-01-01 DOI:10.1186/s13015-018-0128-1
Michał Aleksander Ciach, Anna Muszewska, Paweł Górecki
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引用次数: 1

Abstract

Background: Horizontal gene transfer (HGT), a process of acquisition and fixation of foreign genetic material, is an important biological phenomenon. Several approaches to HGT inference have been proposed. However, most of them either rely on approximate, non-phylogenetic methods or on the tree reconciliation, which is computationally intensive and sensitive to parameter values.

Results: We investigate the locus tree inference problem as a possible alternative that combines the advantages of both approaches. We present several algorithms to solve the problem in the parsimony framework. We introduce a novel tree mapping, which allows us to obtain a heuristic solution to the problems of locus tree inference and duplication classification.

Conclusions: Our approach allows for faster comparisons of gene and species trees and improves known algorithms for duplication inference in the presence of polytomies in the species trees. We have implemented our algorithms in a software tool available at https://github.com/mciach/LocusTreeInference.

Abstract Image

Abstract Image

Abstract Image

多株种树基因树的位点感知分解。
背景:水平基因转移(HGT)是一种重要的生物学现象,是外来遗传物质的获取和固定过程。已经提出了几种HGT推理方法。然而,大多数方法要么依赖于近似的非系统发育方法,要么依赖于计算量大且对参数值敏感的树调和。结果:我们研究了轨迹树推理问题作为一种可能的替代方案,结合了两种方法的优点。我们提出了几种算法来解决简约框架下的问题。我们引入了一种新的树映射方法,使我们能够启发式地解决位点树推理和重复分类问题。结论:我们的方法可以更快地比较基因和物种树,并改进了物种树中存在多组性的重复推断的已知算法。我们已经在https://github.com/mciach/LocusTreeInference上的一个软件工具中实现了我们的算法。
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来源期刊
Algorithms for Molecular Biology
Algorithms for Molecular Biology 生物-生化研究方法
CiteScore
2.40
自引率
10.00%
发文量
16
审稿时长
>12 weeks
期刊介绍: Algorithms for Molecular Biology publishes articles on novel algorithms for biological sequence and structure analysis, phylogeny reconstruction, and combinatorial algorithms and machine learning. Areas of interest include but are not limited to: algorithms for RNA and protein structure analysis, gene prediction and genome analysis, comparative sequence analysis and alignment, phylogeny, gene expression, machine learning, and combinatorial algorithms. Where appropriate, manuscripts should describe applications to real-world data. However, pure algorithm papers are also welcome if future applications to biological data are to be expected, or if they address complexity or approximation issues of novel computational problems in molecular biology. Articles about novel software tools will be considered for publication if they contain some algorithmically interesting aspects.
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