无家族重排对基因同源推断的潜力。

IF 0.9 4区 生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Diego P Rubert, Daniel Doerr, Marília D V Braga
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引用次数: 3

摘要

最近,我们提出了一个有效的ILP公式[Rubert DP, Martinez FV, Braga MDV, Natural family-free genomic distance, Algorithms Mol Biol 16:4, 2021],用于精确计算两个基因组在无家族环境下的重排距离。在这种情况下,既没有预先将基因分类为家族,也没有对基因组施加进一步的限制。给定两个基因组,上述ILP计算出基因的最佳匹配,同时考虑到由基因相似性引起的局部突变和大规模基因组重排。在这里,我们探索了使用这种ILP来推断几个物种的同源物群的潜力。更准确地说,给定一组基因组,我们的方法首先计算所有成对的最佳基因匹配,然后在第二步将其整合到基因家族中。我们的方法被实现到一个包含基因相似性预计算的管道中。可以从gitlab.ub. unit -bielefeld.de/gi/FFGC下载。在模拟数据和实际数据上进行了实验,得到了令人满意的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The potential of family-free rearrangements towards gene orthology inference.

Recently, we proposed an efficient ILP formulation [Rubert DP, Martinez FV, Braga MDV, Natural family-free genomic distance, Algorithms Mol Biol 16:4, 2021] for exactly computing the rearrangement distance of two genomes in a family-free setting. In such a setting, neither prior classification of genes into families, nor further restrictions on the genomes are imposed. Given two genomes, the mentioned ILP computes an optimal matching of the genes taking into account simultaneously local mutations, given by gene similarities, and large-scale genome rearrangements. Here, we explore the potential of using this ILP for inferring groups of orthologs across several species. More precisely, given a set of genomes, our method first computes all pairwise optimal gene matchings, which are then integrated into gene families in the second step. Our approach is implemented into a pipeline incorporating the pre-computation of gene similarities. It can be downloaded from gitlab.ub.uni-bielefeld.de/gi/FFGC. We obtained promising results with experiments on both simulated and real data.

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来源期刊
Journal of Bioinformatics and Computational Biology
Journal of Bioinformatics and Computational Biology MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
2.10
自引率
0.00%
发文量
57
期刊介绍: The Journal of Bioinformatics and Computational Biology aims to publish high quality, original research articles, expository tutorial papers and review papers as well as short, critical comments on technical issues associated with the analysis of cellular information. The research papers will be technical presentations of new assertions, discoveries and tools, intended for a narrower specialist community. The tutorials, reviews and critical commentary will be targeted at a broader readership of biologists who are interested in using computers but are not knowledgeable about scientific computing, and equally, computer scientists who have an interest in biology but are not familiar with current thrusts nor the language of biology. Such carefully chosen tutorials and articles should greatly accelerate the rate of entry of these new creative scientists into the field.
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