Lu Ying, Li Mengfei, Gao Zhendong, Ma Hongming, Chong Yuqing, Hong Jieyun, Wu Jiao, Wu Dongwang, Xi Dongmei, Deng Weidong
Yunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China.
State Key Laboratory for Conservation and Utilization of Bio-Resource in Yunnan, Kunming 650201, China.
Int J Mol Sci. 2025 Jan 4;26(1):372. doi: 10.3390/ijms26010372.
With the rapid advancement of high-throughput sequencing technologies, whole genome sequencing (WGS) has emerged as a crucial tool for studying genetic variation and population structure. Utilizing population genomics tools to analyze resequencing data allows for the effective integration of selection signals with population history, precise estimation of effective population size, historical population trends, and structural insights, along with the identification of specific genetic loci and variations. This paper reviews current whole genome sequencing technologies, detailing primary research methods, relevant software, and their advantages and limitations within population genomics. The goal is to examine the application and progress of resequencing technologies in this field and to consider future developments, including deep learning models and machine learning algorithms, which promise to enhance analytical methodologies and drive further advancements in population genomics.
随着高通量测序技术的迅速发展,全基因组测序(WGS)已成为研究遗传变异和群体结构的关键工具。利用群体基因组学工具分析重测序数据,能够将选择信号与群体历史进行有效整合,精确估计有效群体大小、历史群体趋势和结构见解,同时还能识别特定的基因位点和变异。本文综述了当前的全基因组测序技术,详细介绍了主要研究方法、相关软件及其在群体基因组学中的优缺点。目的是探讨重测序技术在该领域的应用和进展,并思考未来的发展方向,包括深度学习模型和机器学习算法,这些有望增强分析方法并推动群体基因组学的进一步发展。