Biological big-data sources, problems of storage, computational issues, and applications: a comprehensive review

Biological big data are a massive amount of data generated from multi-omics experiments, such as genomics, transcriptomics, proteomics, metabolomics, phenomics, glycomics, epigenomics, and other omics. These data are used to study biological processes and to gain insights into how living systems work. It can also be used to develop new treatments for diseases and understand the causes of certain conditions. The storage and analysis of these data present several challenges owing to their sheer size and complexity. Storing these data efficiently requires a large amount of storage space and processing power. Furthermore, there are certain limitations in terms of the kind of insights that can be gained from multi-omics data because of their complexity. Despite these challenges, biological big data offers great potential for advancing our understanding of biology and developing new treatments for diseases. Big-data research is a rapidly growing field, with numerous applications. As the amount of data continues to increase, it is important to understand its storage, utility, limitations, and challenges. In this review article, various sources of big-data research and their storage capacities, limitations, and challenges are discussed. Factors affecting the data quality and accuracy have been reported. It will be helpful for researchers to understand the available big data in biology for their further utilization and integration into novel discovery.