Effects of rooting via out-groups on in-group topology in phylogeny.

Q4 Health Professions
Margareta Ackerman, Daniel G Brown, David Loker
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引用次数: 0

Abstract

Users of phylogenetic methods require rooted trees, because the direction of time depends on the placement of the root. While phylogenetic trees are typically rooted by using an out-group, this mechanism is inappropriate when the addition of an out-group changes the in-group topology. We perform a formal analysis of phylogenetic algorithms under the inclusion of distant out-groups. It turns out that linkage-based algorithms (including UPGMA) and a class of bisecting methods do not modify the topology of the in-group when an out-group is included. By contrast, the popular neighbour joining algorithm fails this property in a strong sense: every data set can have its structure destroyed by some arbitrarily distant outlier. Furthermore, including multiple outliers can lead to an arbitrary topology on the in-group. The standard rooting approach that uses out-groups may be fundamentally unsuited for neighbour joining.

系统发育中外群生根对内群拓扑结构的影响。
系统发育方法的使用者需要有根的树,因为时间的方向取决于根的位置。虽然系统发育树通常通过使用外组来扎根,但是当外组的添加改变了内组拓扑结构时,这种机制是不合适的。我们执行的系统发育算法的形式化分析下,包括遥远的外群体。结果表明,当包含外组时,基于链接的算法(包括UPGMA)和一类平分方法不会修改内组的拓扑结构。相比之下,流行的邻居连接算法在很大程度上不具备这一特性:每个数据集的结构都可能被一些任意距离的离群值破坏。此外,包含多个离群值可能导致组内的任意拓扑结构。使用外群体的标准生根方法可能根本不适合邻居加入。
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来源期刊
International Journal of Bioinformatics Research and Applications
International Journal of Bioinformatics Research and Applications Health Professions-Health Information Management
CiteScore
0.60
自引率
0.00%
发文量
26
期刊介绍: Bioinformatics is an interdisciplinary research field that combines biology, computer science, mathematics and statistics into a broad-based field that will have profound impacts on all fields of biology. The emphasis of IJBRA is on basic bioinformatics research methods, tool development, performance evaluation and their applications in biology. IJBRA addresses the most innovative developments, research issues and solutions in bioinformatics and computational biology and their applications. Topics covered include Databases, bio-grid, system biology Biomedical image processing, modelling and simulation Bio-ontology and data mining, DNA assembly, clustering, mapping Computational genomics/proteomics Silico technology: computational intelligence, high performance computing E-health, telemedicine Gene expression, microarrays, identification, annotation Genetic algorithms, fuzzy logic, neural networks, data visualisation Hidden Markov models, machine learning, support vector machines Molecular evolution, phylogeny, modelling, simulation, sequence analysis Parallel algorithms/architectures, computational structural biology Phylogeny reconstruction algorithms, physiome, protein structure prediction Sequence assembly, search, alignment Signalling/computational biomedical data engineering Simulated annealing, statistical analysis, stochastic grammars.
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