Frontiers of Data and Computing ›› 2020, Vol. 2 ›› Issue (2): 1-19.doi: 10.11871/jfdc.issn.2096-742X.2020.02.001

• Special Issue: Data Analysis Technology & Application •     Next Articles

Current Status and Prospects of Genomics Data Analysis Methods

Chen Meili1,Ma Yingke1,Li Rujiao1,*(),Bao Yiming1,2,*()   

  1. 1. National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics (China National Center for Bioinformation), Chinese Academy of Sciences, Beijing 100101, China
    2. School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2020-01-21 Online:2020-04-20 Published:2020-06-03
  • Contact: Rujiao Li,Yiming Bao E-mail:lirj@big.ac.cn;baoym@big.ac.cn

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