Sequence-dependent prediction of recombination hotspots in Saccharomyces cerevisiae
Sequence-dependent prediction of recombination hotspots in Saccharomyces cerevisiae
复制标题
酿酒酵母重组热点的序列依赖性预测
DOI：
10.1016/j.jtbi.2011.10.004
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发表时间：
2012-01-21
影响因子：
2
通讯作者：
Cai, Lu
中科院分区：
文献类型：
--
作者：
Liu, Guoqing;Liu, Jia;Cai, Lu
减数分裂重组不会在基因组中随机发生，而是在一些基因组区域（热点）中以相对高的频率发生，而在其他区域（冷点）中以相对低的频率发生。热点和冷点的出现有助于揭示复合的机理，但如何准确预测热点/冷点的存在仍是一个悬而未决的问题。在这项研究中，我们提出了一个模型来预测酵母中的热点/冷点，使用增量的多样性结合二次判别分析（IDQD）的基础上序列k-met频率。5-交叉验证显示总预测准确率为80.3%。与其他机器学习算法相比，IDQD方法与随机森林（RF）一样强大，并且在识别热点和冷点方面优于支持向量机（SVM）。我们还预测了转录起始位点上游区域和转录终止位点下游区域的重组率增加。此外，IDQD模型获得的酵母全基因组重组图谱与实验生成的图谱非常一致，特别是对于峰位置，尽管存在一些细微的差异。我们的研究结果突出了重组的序列依赖性。(C)2011爱思唯尔有限公司保留所有权利。
Meiotic recombination does not occur randomly across the genome, but instead occurs at relatively high frequencies in some genomic regions (hotspots) and relatively low frequencies in others (coldspots). Hotspots and coldspots would shed light on the mechanism of recombination, but the accurate prediction of hot/cold spots is still an open question. In this study, we presented a model to predict hot/cold spots in yeast using increment of diversity combined with quadratic discriminant analysis (IDQD) based on sequence k-met frequencies. 5-fold cross validation showed a total prediction accuracy of 80.3%. Compared with other machine-learning algorithms, IDQD approach is as powerful as random forest (RF) and outperforms support vector machine (SVM) in identifying hotspots and coldspots. We also predicted increased recombination rates in the upstream regions of transcription start sites and in the downstream regions of transcription termination sites. Additionally, genome-wide recombination map in yeast obtained by IDQD model is in close agreement with the experimentally generated map, especially for the Peak locations, although some fine-scale differences exist. Our results highlight the sequence dependency of recombination. (C) 2011 Elsevier Ltd. All rights reserved.