Efficiently sparse listing of classes of optimal cophylogeny reconciliations.

IF 1.7 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS Algorithms for Molecular Biology Pub Date : 2022-02-15 DOI:10.1186/s13015-022-00206-y
Yishu Wang, Arnaud Mary, Marie-France Sagot, Blerina Sinaimeri
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Abstract

Background: Cophylogeny reconciliation is a powerful method for analyzing host-parasite (or host-symbiont) co-evolution. It models co-evolution as an optimization problem where the set of all optimal solutions may represent different biological scenarios which thus need to be analyzed separately. Despite the significant research done in the area, few approaches have addressed the problem of helping the biologist deal with the often huge space of optimal solutions.

Results: In this paper, we propose a new approach to tackle this problem. We introduce three different criteria under which two solutions may be considered biologically equivalent, and then we propose polynomial-delay algorithms that enumerate only one representative per equivalence class (without listing all the solutions).

Conclusions: Our results are of both theoretical and practical importance. Indeed, as shown by the experiments, we are able to significantly reduce the space of optimal solutions while still maintaining important biological information about the whole space.

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背景:共生体和解是分析宿主-寄生虫(或宿主-共生体)共同进化的有力方法。它将共同进化建模为一个优化问题,其中所有最优解的集合可能代表不同的生物场景,因此需要单独分析。尽管在这一领域进行了大量的研究,但很少有方法能够帮助生物学家处理通常巨大的最优解空间。结果:本文提出了一种解决这一问题的新方法。我们引入了三种不同的准则,在这些准则下,两个解可以被认为是生物等效的,然后我们提出了多项式延迟算法,每个等价类只枚举一个代表(不列出所有解)。结论:本研究结果具有一定的理论和实践意义。的确,如实验所示,我们能够显著减少最优解的空间,同时仍然保持整个空间的重要生物信息。
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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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