基于离散形态特征的系统发育最大似然扩展模型。

IF 0.9 4区 数学 Q3 Mathematics Statistical Applications in Genetics and Molecular Biology Pub Date : 2020-02-20 DOI:10.1515/sagmb-2019-0029
David A Spade
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引用次数: 1

摘要

最大似然是一种基于一组遗传数据估计系统发育树的常用方法。然而,某些类型的遗传数据的进化模型在其规范中存在严重缺陷,这种错误的规范可能对系统发育推断产生不利影响。我们的注意力集中在扩展现有的一类模型,用于从离散形态特征估计系统发育树。这项工作的主要进展是一个模型,该模型允许使用似然方法从离散形态特征数据估计系统发育树的不相等平衡频率。还将讨论拟议模型的可能扩展。
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An extended model for phylogenetic maximum likelihood based on discrete morphological characters.

Maximum likelihood is a common method of estimating a phylogenetic tree based on a set of genetic data. However, models of evolution for certain types of genetic data are highly flawed in their specification, and this misspecification can have an adverse impact on phylogenetic inference. Our attention here is focused on extending an existing class of models for estimating phylogenetic trees from discrete morphological characters. The main advance of this work is a model that allows unequal equilibrium frequencies in the estimation of phylogenetic trees from discrete morphological character data using likelihood methods. Possible extensions of the proposed model will also be discussed.

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来源期刊
CiteScore
1.20
自引率
11.10%
发文量
8
审稿时长
6-12 weeks
期刊介绍: Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.
期刊最新文献
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