Abstract
The human genome was first sequenced in 1994. It took 10 years of cooperation between numerous international research organizations to reveal a preliminary human DNA sequence. Genomics labs can now sequence an entire genome in only a few days. Here, we talk about how the advent of high-performance sequencing platforms has paved the way for Big Data in biology and contributed to the development of modern bioinformatics, which in turn has helped to expand the scope of biology and allied sciences. New technologies and methodologies for the storage, management, analysis, and visualization of big data have been shown to be necessary. Not only does modern bioinformatics have to deal with the challenge of processing massive amounts of heterogeneous data, but it also has to deal with different ways of interpreting and presenting those results, as well as the use of different software programs and file formats. Solutions to these problems are tried to present in this chapter. In order to store massive amounts of data and provide a reasonable period for completing search queries, new database management systems other than relational ones will be necessary. Emerging advance programing approaches, such as machine learning, Hadoop, and MapReduce, aim to provide the capacity to easily construct one’s own scripts for data processing and address the issue of the diversity of genomic and proteomic data formats in bioinformatics.
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Gupta, A., Kumar, S., Kumar, A. (2024). Big Data in Bioinformatics and Computational Biology: Basic Insights. In: Mandal, S. (eds) Reverse Engineering of Regulatory Networks. Methods in Molecular Biology, vol 2719. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3461-5_9
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DOI: https://doi.org/10.1007/978-1-0716-3461-5_9
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