Jin Xin, Jiang Qian, Chen Yanyan, Lee Shin-Jye, Nie Rencan, Yao Shaowen, Zhou Dongming, He Kangjian
School of Information, Yunnan University, Kunming, Yunnan Province, China.
School of Life Sciences, Yunnan University, Kunming, Yunnan Province, China.
J Mol Graph Model. 2017 Sep;76:342-355. doi: 10.1016/j.jmgm.2017.07.019. Epub 2017 Jul 20.
DNA sequence similarity/dissimilarity analysis is a fundamental task in computational biology, which is used to analyze the similarity of different DNA sequences for learning their evolutionary relationships. In past decades, a large number of similarity analysis methods for DNA sequence have been proposed due to the ever-growing demands. In order to learn the advances of DNA sequence similarity analysis, we make a survey and try to promote the development of this field. In this paper, we first introduce the related knowledge of DNA similarities analysis, including the data sets, similarities distance and output data. Then, we review recent algorithmic developments for DNA similarity analysis to represent a survey of the art in this field. At last, we summarize the corresponding tendencies and challenges in this research field. This survey concludes that although various DNA similarity analysis methods have been proposed, there still exist several further improvements or potential research directions in this field.
DNA序列相似性/不相似性分析是计算生物学中的一项基本任务,用于分析不同DNA序列的相似性以了解它们的进化关系。在过去几十年中,由于需求不断增长,已经提出了大量用于DNA序列的相似性分析方法。为了了解DNA序列相似性分析的进展,我们进行了一项调查并试图推动该领域的发展。在本文中,我们首先介绍DNA相似性分析的相关知识,包括数据集、相似性距离和输出数据。然后,我们回顾了DNA相似性分析的最新算法发展,以呈现该领域的技术现状。最后,我们总结了该研究领域的相应趋势和挑战。这项调查得出的结论是,尽管已经提出了各种DNA相似性分析方法,但该领域仍存在一些进一步改进或潜在的研究方向。