Unbiased anchors for reliable genome-wide synteny detection.

IF 1.5 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS
Karl K Käther, Andreas Remmel, Steffen Lemke, Peter F Stadler
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引用次数: 0

Abstract

Orthology inference lies at the foundation of comparative genomics research. The correct identification of loci which descended from a common ancestral sequence is not only complicated by sequence divergence but also duplication and other genome rearrangements. The conservation of gene order, i.e. synteny, is used in conjunction with sequence similarity as an additional factor for orthology determination. Current approaches, however, rely on genome annotations and are therefore limited. Here we present an annotation-free approach and compare it to synteny analysis with annotations. We find that our approach works better in closely related genomes whereas there is a better performance with annotations for more distantly related genomes. Overall, the presented algorithm offers a useful alternative to annotation-based methods and can outperform them in many cases.

无偏锚可靠的全基因组同步检测。
同源推断是比较基因组学研究的基础。正确鉴定来自共同祖先序列的基因座不仅因序列分化而复杂化,而且还因重复和其他基因组重排而复杂化。基因顺序的保守性,即synteny,与序列相似性一起作为确定同源性的附加因素。然而,目前的方法依赖于基因组注释,因此受到限制。在这里,我们提出了一种无需注释的方法,并将其与带有注释的句法分析进行了比较。我们发现,我们的方法在密切相关的基因组中工作得更好,而在更远的基因组上有更好的性能。总的来说,本文提出的算法为基于注释的方法提供了一个有用的替代方案,并且在许多情况下优于它们。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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