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iRSpot-Pse6NC:通过将六聚体组成纳入通用 PseKNC 来识别 中的重组热点。

iRSpot-Pse6NC: Identifying recombination spots in by incorporating hexamer composition into general PseKNC.

机构信息

Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.

Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, 333403, China.

出版信息

Int J Biol Sci. 2018 May 22;14(8):883-891. doi: 10.7150/ijbs.24616. eCollection 2018.

Abstract

Meiotic recombination caused by meiotic double-strand DNA breaks. In some regions the frequency of DNA recombination is relatively higher, while in other regions the frequency is lower: the former is usually called "recombination hotspot", while the latter the "recombination coldspot". Information of the hot and cold spots may provide important clues for understanding the mechanism of genome revolution. Therefore, it is important to accurately predict these spots. In this study, we rebuilt the benchmark dataset by unifying its samples with a same length (131 bp). Based on such a foundation and using SVM (Support Vector Machine) classifier, a new predictor called "iRSpot-Pse6NC" was developed by incorporating the key hexamer features into the general PseKNC (Pseudo K-tuple Nucleotide Composition) via the binomial distribution approach. It has been observed via rigorous cross-validations that the proposed predictor is superior to its counterparts in overall accuracy, stability, sensitivity and specificity. For the convenience of most experimental scientists, the web-server for iRSpot-Pse6NC has been established at http://lin-group.cn/server/iRSpot-Pse6NC, by which users can easily obtain their desired result without the need to go through the detailed mathematical equations involved.

摘要

由减数分裂双链 DNA 断裂引起的减数分裂重组。在一些区域,DNA 重组的频率相对较高,而在其他区域则较低:前者通常称为“重组热点”,后者称为“重组冷点”。热点和冷点的信息可能为理解基因组革命的机制提供重要线索。因此,准确预测这些热点非常重要。在这项研究中,我们通过统一其样本长度(131bp)来重建基准数据集。基于这一基础,并使用支持向量机(Support Vector Machine)分类器,我们通过二项式分布方法将关键六聚体特征纳入一般 PseKNC(伪 K- 核苷酸组成)中,开发了一种称为“iRSpot-Pse6NC”的新预测器。通过严格的交叉验证观察到,与同类相比,该预测器在整体准确性、稳定性、灵敏度和特异性方面都具有优势。为了方便大多数实验科学家,我们在 http://lin-group.cn/server/iRSpot-Pse6NC 上建立了 iRSpot-Pse6NC 的网络服务器,用户可以轻松获得所需的结果,而无需了解涉及的详细数学方程。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1dad/6036749/7bf41d3b5c42/ijbsv14p0883g001.jpg

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