基因组学数据分析方法现状和展望

Current Status and Prospects of Genomics Data Analysis Methods

【目的】全面阐述基因组学数据分析方法的现状和未来发展趋势,为精准医学、精准育种、生物安全、生物多样性、分子进化等的相关组学数据分析算法的研究与工具开发提供参考。【结果】基因组学数据分析主要包括基因组、转录组、表观组数据分析,当前基因组学数据主要面临着海量、多维、异构等挑战。本文详细地阐述了基因组学数据分析算法和工具开发的现状、应用、存在的问题和面临的挑战。【结论】充分利用人工智能、统计模型、知识图谱等先进技术,不断地优化和开发更先进的算法和更鲁棒的模型,使其兼具高容错、高准确、高效、计算资源低耗等优点,匹配海量、多维、异构基因组学大数据分析的需求,是未来基因组学数据分析算法和工具开发的方向。

[Objective]Through a comprehensive review of the current status and future development of genomics data analysis methods,we provide suggestions for the improvement of algorithm and tool development of related omics data analysis in precision medicine,precision breeding,biosafety,biodiversity and molecular evolution.[Results]The analysis of genomics data mainly includes that of genomic,transcriptomic and epigenomic data.At present,the analysis of genomics data faces challenges primarily because the data are massive,multidimensional and heterogeneous.This review will elaborate on the current status,applications,challenges,and prospects of algorithm and tool development for genomics data analysis.[Conclusions]The future directions of algorithm and tool development for genomics data analysis are to make full use of advanced technologies such as artificial intelligence,statistical models,and knowledge graphs,and to continuously optimize and develop more advanced algorithms and robust models that are of error tolerance,high accuracy,and high efficiency with low cost of computing resources.