Hongpeng  Wang , Shiying Yu

Biotechnology Research Center, Cuixi Academy of Biotechnology, Zhuji, 311800, Zhejiang, China
Author    Correspondence author
Computational Molecular Biology, 2024, Vol. 14, No. 6   
Received: 12 Nov., 2024    Accepted: 13 Dec., 2024    Published: 26 Dec., 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.

With the rapid development of sequencing and imaging technologies, an increasing amount of biological data is being generated, making the storage, processing, and analysis of vast amounts of data a challenge nowadays. To address this issue, High-Performance Computing (HPC) has emerged, enabling scientists to swiftly process these big data through parallel computing and cloud platforms, thus becoming a crucial tool for handling biological big data. HPC finds applications in various fields, such as genome assembly, protein structure prediction, and multi-omics integration. HPC encompasses a range of tools, including Slurm, Hadoop, BLAST+, GROMACS, and others. HPC plays a significant role in cancer research, drug development, biodiversity monitoring, and many other aspects. Nowadays, the integration of deep learning, adaptive sampling, and HPC with cloud platforms has also opened up new opportunities. Every coin has two sides, and HPC has its drawbacks as well. Its usage cost is relatively high, operation is complex, and there are issues with data integration. However, on the whole, HPC is gradually transforming the way biological research is conducted and holds great potential for development.

High-performance computing (HPC); Biological big data; Parallel computing; Genomics and multi-omics; Cloud integration in biology