Ayushman Baruah
Undergraduate Student
College of Agriculture, Assam Agricultural University, Jorhat-785 013
E-mail: ayushman.baruah.aj23@aau.a.cin
Introduction
In the vast landscape of biological sciences, where DNA sequences whisper secrets about life itself, lies a fascinating intersection: bioinformatics. This field marries biology with data science, unlocking hidden patterns in genetic information. In this article, we’ll embark on a journey through the double helix, exploring how the generation and analyses of big data are revolutionizing our understanding of life.
So, what is Big Data? Big data has been defined as a broad, loose, and rapidly expanding body of digital data that is not easily analysed with traditional data analysis methods. It is characterized by the three V’s: volume (processing a massive number of data), velocity (data being generated more frequently), and variety (data in the form of structured and unstructured). Big data is a revolutionary field where big genomic data can be analysed from which the approach of precision medicine can be developed along with the study of various complex biological processes. Given the evolution of big data, it has become possible to analyse large datasets generated from high throughput methods like NGS. This has led to significant improvement in the genomics, proteomics, and other ‘-omics’ fields and the rise of pharmacogenomics and therapeutic which is the use of tailored treatments depending on one’s genotype. It can therefore be argued that the adoption of big data into the field of bioinformatics is a major step-up in the functionality of genomic research and its residual effects in the sphere of medicine and disease control.
Decoding the Genome
The Human Genome Project (2003) was a huge success in Bioinformatics and genomics. It unravelled the 3 billion base pairs that constitute the genetic blueprint of human beings. Imagine – all the instructions for building and maintaining a human encoded in a string of A’s, T’s, C’s, and G’s. This colossal dataset paved the way for personalized medicine, ancestry tracing, disease prediction, and many more.
Big Data in Bioinformatics
The advent of Next-Generation Sequencing (NGS) machines flooded databases with petabytes of raw DNA data. These machines churn out sequences at breakneck speeds, revealing genetic variations, mutations, and regulatory elements. Big data bioinformatics is associated with creation and management of repositories of data, better computing capacities, and tools to manipulate as well as analyse data.
Bioinformaticians wield algorithms like magic wands. They align sequences, predict protein structures, and identify disease-causing mutations. One such gem is BLAST (Basic Local Alignment Search Tool), which compares sequences across species. It’s like finding a needle in a genomic haystack.
Data Mining
Mining biological databases unearth treasures. GenBank, UniProt, and Ensembl databases house sequences of genomes, proteins, and their annotations. Researchers sift through these digital gold mines, seeking clues to cancer, evolution, and biodiversity. The more the data, the richer the insights.
Challenges and Opportunities
Taking into account biological research direction, the emergence of big data itself and its analytical counterparts has drastically changed the way biological scientists solve multifactorial system issues.
Big data analytics hold significant importance in the agriculture sector because they contribute to the evolution of precision farming. Through the analysis of the big data gathered from sensors and satellite imagery, drones, and IoT devices, researchers can come up with the right decision regarding crop planting, the status of soils, and the distribution of resources. It also results in conservation of inputs like water, fertilizers, and pesticides that help in cutting unnecessary expenses and going green. A major application of big data in farming sector is in acknowledgment of plant varieties that yield high, as well as displaying tolerance to stresses. Genome-wide association studies and predictive modelling can help identify genetic markers for desired traits; hence, mankind can speed up the breeding of crops that are suitable for changing climate conditions. Additionally, big data analytics help in crop and livestock management and diagnoses of diseases as well as coming up with predictions on the same. Such actions are proactive thereby reducing huge losses and ensuring food security in the country and to the consumers. Weather conditions and climate data analysis help improve the bar on forecasting farm yields, which assists farmers in planning and managing risks in the sector. However, the possibilities of using big data in the biological field are not limited to agriculture. In bioinformatics, it is applied for analysing the inexhaustible and complicated biological networks and for systems biology. It can even lead to the discovery of a biological signature, a new drug, or a different therapeutic approach, thus stimulating the advancement of medicine and healthcare.
Storing petabytes of genomic data isn’t a child’s play. Cloud computing and distributed databases have come to the rescue. But beware! Data breaches could reveal your genetic secrets. Machine learning algorithms predict gene functions, drug interactions, and disease risks. Imagine an AI whispering, “Your genes hint at a penchant for spicy food.” Exciting, right? However, the analysis and the use of big data in bioinformatics are still in descriptive stages and there are some issues involved. A significant drawback is the problem with data in higher dimensions where it may carry additional information that makes it hard to interpret the results from the analysis. It has also been a central theme in achieving the goal of creating highly precise models as well as models that can be easily interpreted to handle the large amount of data generated by such approaches as next-generation sequencing technologies. Another risk is associated with the inconsistency between separate database systems, as the data are stored in dissimilar types of requirements and need much mutualization. It also becomes imperative to invest in sustainable tools and resources to accommodate the generation of even more data as other facilities in the research community improve in their capacities. Furthermore, a major issue that remains to be solved is the recruitment process that involves the scientific community in support of new systems and standards.
Conclusion
Bioinformatics dances at the crossroads of science and technology. It’s where data meets destiny. As we decode genomes, let’s remember that every base pair tells a story. So, next time you sip your coffee, ponder the caffeine metabolism genes within you. The double helix awaits more revelations, and we’re just getting started.
References:
Demirbaga, Ü.; Singh Aujla, G.; Jindal, A. and Kalyon, O. (2024). Big Data Analytics in Bioinformatics. In: Big Data Analytics, pp. 265-284. Springer.
Srivastava, A. and Naik, A. (2021). Big Data Analysis in Bioinformatics. In: Advances in Bioinformatics, pp. 405-429. Springer.
Anonymous (2023). Big data unites all disciplines in the life sciences. Nature.com
Anonymous (2020). Impact of Big Data on Bioinformatics. GeeksforGeeks.com
Campos-Guillén, J., Moreno-Andrade, V., Rico-Rodriguez, M.A., et al. (2020) ‘The Use of Big Data in the Modern Biology: The Case of Agriculture’, in Intelligent and Complex Systems in Economics and Business. Springer.
