进化场景中的相对时序信息和正交性。

IF 1.5 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS Algorithms for Molecular Biology Pub Date : 2023-11-08 DOI:10.1186/s13015-023-00240-4
David Schaller, Tom Hartmann, Manuel Lafond, Peter F Stadler, Nicolas Wieseke, Marc Hellmuth
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引用次数: 1

摘要

背景:描述物种集合中基因家族进化的进化场景包括基因树T的顶点到物种树S的顶点和边的映射。两个现存基因(T的叶子)的最后共同祖先和它们所在的两个物种(S的叶子)最后共同祖先的相对时间指示水平基因转移(HGT)和古代复制。另一方面,同源基因对要求它们最后的共同祖先与相应的物种形成事件重合。基因和物种分化的相对时间信息由三个彩色图捕获,这些图以现存基因为顶点,以发现基因的物种为顶点颜色:等分化时间(EDT)图、后分化时间(LDT)图和前分化时间(PDT)图,它们共同形成了完整图的边缘划分。结果:在这里,我们根据可以从三个图中读取的信息和禁止三元组给出了一个完整的刻画,并提供了一个多项式时间算法来构建解释图的进化场景,前提是存在这样的场景。虽然LDT和PDT图都是cograph,但对于EDT图来说,这通常不是真的。我们证明了每个EDT图都是完美的。虽然在一般情况下,关于LDT和PDT图的信息对于在多项式时间内识别EDT图是必要的,但在无HGT的情况下,可以删除这些额外信息。然而,在不知道假定的LDT和PDT图的情况下,对EDT图的识别对于一般情况是NP完全的。相比之下,PDT图可以在多项式时间内识别。最后,我们将EDT图与针对水平基因转移场景提出的矫正学的替代定义联系起来。除了一个例外,相应的图被显示为有色的cograph。
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Relative timing information and orthology in evolutionary scenarios.

Background: Evolutionary scenarios describing the evolution of a family of genes within a collection of species comprise the mapping of the vertices of a gene tree T to vertices and edges of a species tree S. The relative timing of the last common ancestors of two extant genes (leaves of T) and the last common ancestors of the two species (leaves of S) in which they reside is indicative of horizontal gene transfers (HGT) and ancient duplications. Orthologous gene pairs, on the other hand, require that their last common ancestors coincides with a corresponding speciation event. The relative timing information of gene and species divergences is captured by three colored graphs that have the extant genes as vertices and the species in which the genes are found as vertex colors: the equal-divergence-time (EDT) graph, the later-divergence-time (LDT) graph and the prior-divergence-time (PDT) graph, which together form an edge partition of the complete graph.

Results: Here we give a complete characterization in terms of informative and forbidden triples that can be read off the three graphs and provide a polynomial time algorithm for constructing an evolutionary scenario that explains the graphs, provided such a scenario exists. While both LDT and PDT graphs are cographs, this is not true for the EDT graph in general. We show that every EDT graph is perfect. While the information about LDT and PDT graphs is necessary to recognize EDT graphs in polynomial-time for general scenarios, this extra information can be dropped in the HGT-free case. However, recognition of EDT graphs without knowledge of putative LDT and PDT graphs is NP-complete for general scenarios. In contrast, PDT graphs can be recognized in polynomial-time. We finally connect the EDT graph to the alternative definitions of orthology that have been proposed for scenarios with horizontal gene transfer. With one exception, the corresponding graphs are shown to be colored cographs.

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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.
期刊最新文献
On the parameterized complexity of the median and closest problems under some permutation metrics. TINNiK: inference of the tree of blobs of a species network under the coalescent model. New generalized metric based on branch length distance to compare B cell lineage trees. Metric multidimensional scaling for large single-cell datasets using neural networks. Compression algorithm for colored de Bruijn graphs.
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