贪婪的基因组支架方法。

IF 1.7 4区生物学 Q4 BIOCHEMICAL RESEARCH METHODS

Algorithms for Molecular Biology Pub Date : 2022-10-29 DOI:10.1186/s13015-022-00223-x

Tom Davot, Annie Chateau, Rohan Fossé, Rodolphe Giroudeau, Mathias Weller

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引用次数: 0

摘要

背景:脚手架是一个生物信息学问题，旨在通过确定这些组群的相对位置和方向来完成组群组装过程。它可以看作是一个被称为“脚手架图”的特定图的路径和循环覆盖问题。结果:对该问题给出了一些np -硬度和不可逼近性的结果。我们还在完全图上采用了贪婪逼近算法，使其适用于旨在接近真实实例的特殊类。所描述的算法是针对非完全图问题设计的第一个多项式时间逼近算法。结论:在一组模拟实例上的测试表明，我们的算法比完全图上的版本提供了更好的结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

On a greedy approach for genome scaffolding.

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On a greedy approach for genome scaffolding.

Background: Scaffolding is a bioinformatics problem aimed at completing the contig assembly process by determining the relative position and orientation of these contigs. It can be seen as a paths and cycles cover problem of a particular graph called the "scaffold graph".

Results: We provide some NP-hardness and inapproximability results on this problem. We also adapt a greedy approximation algorithm on complete graphs so that it works on a special class aiming to be close to real instances. The described algorithm is the first polynomial-time approximation algorithm designed for this problem on non-complete graphs.

Conclusion: Tests on a set of simulated instances show that our algorithm provides better results than the version on complete graphs.

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来源期刊

Algorithms for Molecular Biology 生物-生化研究方法

CiteScore

2.40

自引率

10.00%

发文量

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.