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Big Data Analytics in Bioinformatics
Dr. Manas Pradhan From Different Corners

Big Data Analytics in Bioinformatics

Bioinformatics is an interdisciplinary field that helps in analysing and solving biological problems by integrating theories and practices from computer science, mathematics, and statistics to interpret biological data. It plays a major role in managing data in in modern biology and medicine. Bioinformatics provides the tools, databases, and computational methods to store, analyse, and make sense of that data. Modern biological experiments use high-throughput technologies generate enormous biological datasets that require big data tools for analysis.

The biological data available in huge volume and multidimensional format and a hence a challenge for researchers and data scientists. Big data analytics plays a vital role to process such complex datasets to uncover meaningful patterns and insights that lead to better decision-making for many biological cases. The objective of this article to give a broad review of recent development of big data analytics used in biomedical and health informatics with a base to bioinformatics.

 

The major areas of Bioinformatics are:

 

Genomics: deals with DNA sequences data (genomic data) to identify target genes that are useful in disease detection and revolutionized gene-based drug discovery for personalized medicines by understanding the genetic basis of disease.

 

Proteomics: addresses analysing protein structures their functions using computational tools.

 

The applications of bioinformatics are catering to different areas such as drug discovery, diagnosis genetic diseases, agriculture genomics and forensic science. It focuses on the methodologies of data collection, storage, preprocessing, and data driven approaches of machine learning, statistical analysis, and visual analytics. The basic bioinformatics tools of sequence alignment (BLAST and Clustal) used for comparing DNA or protein sequences and the public database of NCBI uses for searching and retrieving biological data have greater contribution to biological study of data.

 

Big data analytics plays major role bioinformatics in revolutionized biological research and healthcare. Different methodologies and tools of big data analytics use in bioinformatics to study genomics, proteomics, and as a whole systems biology.

 

Big data analytics uses different data Storage and management tools to handle the massive biological datasets such as NCBI GenBank, EMBL-EBI, NoSQL Databases, AWS genomics, Google Life Sciences etc. to store DNA, RNA, Protein sequences and facilitating cloud base scalable storage. There are different big data processing frameworks used such as Apache Hadoop, Snake flow, Open MPI etc. for parallel processing of huge biological data to automate and run genomic simulations.

 

The biological patterns analysis usually done using various data mining tools of big data analytics pertaining to statistical machine learning, gene expression tools and variant analysis tools. The predictive modelling of genomics data to produce next generation sequencing data for further research with extensive use of Python, TensorFlow, DEQSeq2, Limma, GATK, BCFtools etc. The data integration and visualize are the challenges of bioinformatics to merge multidata sets catering to genomic, proteomic and metabolomic which solved by implementing big data analytics-based tools such as BioMart, UCSC Genome Browser, Tableau, Power BI etc.

 

The artificial intelligence and deep learning platform such as PyTorch, AlphaFold, and Deep Variant have given added advantage for intelligent predictions and pattern extraction of biological big data to predict gene function, protein structure and drug design.

 

The genomic bigdata management and large-scale sequencing has led major IT giants Google, AWS and Microsoft to create Google Cloud Life Sciences, AWS Genomics Workflows and Microsoft Bioinformatics.

 

The job and career perspectives relating to bioinformatics and big data are catered to different role. As bioinformatician typically is a professional with combine knowledge of molecular biology, genetics, computer programming and statistical analysis. A biostatistician is a role for preparing reports that summarize the result out of using different techniques of data analysis. research lab, and areas of focus may be epidemiology, genetics, or ecology. A bioinformatics data scientist is primarily involved in developing new computational algorithms, and tools for analysing biological data. 

 

Challenges such as data integration, computational complexity, and ethical concerns are still leading to a futuristic need of advanced computational algorithms where both bioinformatics tools and big data analytics will play a major role. This interdisciplinary domain enormous   potential for upcoming generation of biological science and medicine.

 

References:

  • [1] Vohra, D. S. (2024). Big data analytics in bioinformatics: Transforming genomics and proteomics for advanced biological insights. In Proceedings of the 2024 International Conference on Big Data Analytics in Bioinformatics (DABCon) (pp. 1–5). IEEE. https://doi.org/10.1109/DABCon63472.2024.10919359
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