Weipan Wang , Bing Zhang , Manman Li

Hainan Institute of Biotechnology, Haikou, 570206, Hainan, China
Author    Correspondence author
Computational Molecular Biology, 2024, Vol. 14, No. 3   doi: 10.5376/cmb.2024.14.0012
Received: 29 Mar., 2024    Accepted: 22 May, 2024    Published: 02 Jun., 2024

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Wang W.P., Zhang B., and Li M.M., 2024, Big data analytics in biology: a systematic review of methods for large-scale data processing, Computational Molecular Biology, 14(3): 97-105 (doi: 10.5376/cmb.2024.14.0012)

This study explores various methods and tools developed for large-scale data processing in biological research. We studied comprehensive toolkits such as TBtools, which provide user-friendly interfaces for complex data analysis, as well as distributed computing frameworks such as MapReduce, which solve the problem of imbalance in large DNA datasets. In addition, we discussed the challenges posed by the heterogeneity and complexity of big biological data, emphasizing the need for powerful and scalable analytical frameworks, such as bigSCale for single-cell RNA sequencing, in order to gain a comprehensive understanding of the current status and future directions of big data analysis in the field of biology.

Big data analytics; Bioinformatics; High-throughput sequencing; Machine learning; Distributed computing