Current Status and Prospects of Genomics Data Analysis Methods

doi:10.11871/jfdc.issn.2096-742X.2020.02.001

Abstract

Abstract:

[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.

Key words: genome, transcriptome, epigenome, big data analysis, multi-source heterogeneous data integration

Chen Meili,Ma Yingke,Li Rujiao,Bao Yiming. Current Status and Prospects of Genomics Data Analysis Methods[J]. Frontiers of Data and Computing, 2020, 2(2): 1-19.

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