Split-inducing indels in phylogenomic analysis.

IF 1.7 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS
Algorithms for Molecular Biology Pub Date : 2018-07-16 eCollection Date: 2018-01-01 DOI:10.1186/s13015-018-0130-7
Alexander Donath, Peter F Stadler
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引用次数: 11

Abstract

Background: Most phylogenetic studies using molecular data treat gaps in multiple sequence alignments as missing data or even completely exclude alignment columns that contain gaps.

Results: Here we show that gap patterns in large-scale, genome-wide alignments are themselves phylogenetically informative and can be used to infer reliable phylogenies provided the gap data are properly filtered to reduce noise introduced by the alignment method. We introduce here the notion of split-inducing indels (splids) that define an approximate bipartition of the taxon set. We show both in simulated data and in case studies on real-life data that splids can be efficiently extracted from phylogenomic data sets.

Conclusions: Suitably processed gap patterns extracted from genome-wide alignment provide a surprisingly clear phylogenetic signal and an allow the inference of accurate phylogenetic trees.

Abstract Image

Abstract Image

Abstract Image

系统基因组分析中的分裂诱导因子。
背景:大多数使用分子数据的系统发育研究将多个序列比对中的间隙视为缺失数据,甚至完全排除包含间隙的比对列。结果:本研究表明,大规模全基因组比对中的间隙模式本身具有系统发育信息,如果间隙数据经过适当过滤以减少比对方法引入的噪声,则可用于推断可靠的系统发育。我们在这里引入了分裂诱导索引(splids)的概念,它定义了分类单元集的近似双分区。我们在模拟数据和现实生活数据的案例研究中都表明,可以从系统基因组数据集中有效地提取分裂。结论:从全基因组比对中提取的适当处理的间隙模式提供了令人惊讶的清晰系统发育信号,并允许准确的系统发育树推断。
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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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