Qiu Wang-Ren, Xiao Xuan, Chou Kuo-Chen
Computer Department, Jing-De-Zhen Ceramic Institute, Jingdezhen 333046, China.
Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Int J Mol Sci. 2014 Jan 24;15(2):1746-66. doi: 10.3390/ijms15021746.
Meiosis and recombination are the two opposite aspects that coexist in a DNA system. As a driving force for evolution by generating natural genetic variations, meiotic recombination plays a very important role in the formation of eggs and sperm. Interestingly, the recombination does not occur randomly across a genome, but with higher probability in some genomic regions called "hotspots", while with lower probability in so-called "coldspots". With the ever-increasing amount of genome sequence data in the postgenomic era, computational methods for effectively identifying the hotspots and coldspots have become urgent as they can timely provide us with useful insights into the mechanism of meiotic recombination and the process of genome evolution as well. To meet the need, we developed a new predictor called "iRSpot-TNCPseAAC", in which a DNA sample was formulated by combining its trinucleotide composition (TNC) and the pseudo amino acid components (PseAAC) of the protein translated from the DNA sample according to its genetic codes. The former was used to incorporate its local or short-rage sequence order information; while the latter, its global and long-range one. Compared with the best existing predictor in this area, iRSpot-TNCPseAAC achieved higher rates in accuracy, Mathew's correlation coefficient, and sensitivity, indicating that the new predictor may become a useful tool for identifying the recombination hotspots and coldspots, or, at least, become a complementary tool to the existing methods. It has not escaped our notice that the aforementioned novel approach to incorporate the DNA sequence order information into a discrete model may also be used for many other genome analysis problems. The web-server for iRSpot-TNCPseAAC is available at http://www.jci-bioinfo.cn/iRSpot-TNCPseAAC. Furthermore, for the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the current web server to obtain their desired result without the need to follow the complicated mathematical equations.
减数分裂和重组是DNA系统中共存的两个相反方面。作为通过产生自然遗传变异推动进化的驱动力，减数分裂重组在卵子和精子的形成中起着非常重要的作用。有趣的是，重组并非在基因组中随机发生，而是在一些被称为“热点”的基因组区域具有更高的概率，而在所谓的“冷点”区域概率较低。在后基因组时代，随着基因组序列数据量的不断增加，有效识别热点和冷点的计算方法变得迫切，因为它们可以及时为我们提供有关减数分裂重组机制和基因组进化过程的有用见解。为满足这一需求，我们开发了一种名为“iRSpot-TNCPseAAC”的新预测器，其中通过将DNA样本的三核苷酸组成（TNC）和根据其遗传密码从DNA样本翻译的蛋白质的伪氨基酸组成（PseAAC）相结合来构建DNA样本。前者用于纳入其局部或短程序列顺序信息；而后者用于纳入其全局和长程序列顺序信息。与该领域现有的最佳预测器相比，iRSpot-TNCPseAAC在准确率、马修斯相关系数和灵敏度方面都取得了更高的比率，这表明新的预测器可能成为识别重组热点和冷点的有用工具，或者至少成为现有方法的补充工具。我们也注意到，上述将DNA序列顺序信息纳入离散模型的新方法也可用于许多其他基因组分析问题。iRSpot-TNCPseAAC的网络服务器可在http://www.jci-bioinfo.cn/iRSpot-TNCPseAAC上获取。此外，为方便绝大多数实验科学家，还提供了一份逐步指南，介绍如何使用当前的网络服务器获得他们想要的结果，而无需遵循复杂的数学方程。
