A comparison and assessment of computational method for identifying recombination hotspots in Saccharomyces cerevisiae

IF 6.8 2区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Briefings in bioinformatics Pub Date : 2020-09-25 DOI:10.1093/bib/bbz123

Hui Yang, Wuritu Yang, Fu-Ying Dao, Hao Lv, H. Ding, Wei Chen, Hao Lin

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引用次数: 70

Abstract

Meiotic recombination is one of the most important driving forces of biological evolution, which is initiated by double-strand DNA breaks. Recombination has important roles in genome diversity and evolution. This review firstly provides a comprehensive survey of the 15 computational methods developed for identifying recombination hotspots in Saccharomyces cerevisiae. These computational methods were discussed and compared in terms of underlying algorithms, extracted features, predictive capability and practical utility. Subsequently, a more objective benchmark data set was constructed to develop a new predictor iRSpot-Pse6NC2.0 (http://lin-group.cn/server/iRSpot-Pse6NC2.0). To further demonstrate the generalization ability of these methods, we compared iRSpot-Pse6NC2.0 with existing methods on the chromosome XVI of S. cerevisiae. The results of the independent data set test demonstrated that the new predictor is superior to existing tools in the identification of recombination hotspots. The iRSpot-Pse6NC2.0 will become an important tool for identifying recombination hotspot.

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识别酿酒酵母重组热点的计算方法比较与评价

减数分裂重组是生物进化中最重要的驱动力之一，它是由双链DNA断裂引发的。重组在基因组多样性和进化中具有重要作用。本文首先全面综述了用于识别酿酒酵母重组热点的15种计算方法。从基础算法、提取特征、预测能力和实际应用等方面对这些计算方法进行了讨论和比较。随后，构建更客观的基准数据集，开发新的预测因子iRSpot-Pse6NC2.0 (http://lin-group.cn/server/iRSpot-Pse6NC2.0)。为了进一步证明这些方法的泛化能力，我们将iRSpot-Pse6NC2.0与酿酒酵母XVI染色体上的现有方法进行了比较。独立数据集测试的结果表明，新的预测器在识别重组热点方面优于现有的工具。iRSpot-Pse6NC2.0将成为识别重组热点的重要工具。

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

Briefings in bioinformatics 生物-生化研究方法

CiteScore

13.20

自引率

13.70%

发文量

549

审稿时长

6 months

期刊介绍： Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.