Tezpur University
Unveiling Microbial Mysteries: A Comprehensive Exploration of Computational Biology in the North East Region at Tezpur University
The Bioinformatics Centre at Tezpur University is aimed to understand microbial biodiversity in the North East Region, specifically in Assam and Arunachal Pradesh, utilizing predominantly in- silico approaches in an integrated manner. The center places a strong emphasis on machine learning methods, pipelines, and workflows, utilizing them to analyze and integrate Omics scale heterogeneous data to generate knowledge in the field of biodiversity including evolutionary dynamics, microbial adaptation, the discovery of novel genes, gene expression, genome engineering, biocatalysis, protein-protein interaction, and protein DNA/ligand interaction. Additionally, the center conducts regular training sessions, workshops, and seminars in the field of bioinformatics to develop and train manpower in this domain.
The Centre is actively involved in research activities aimed at addressing challenges in molecular evolution, microbial biodiversity characterization and conservation, development of supervised/unsupervised machine learning algorithms/tools, molecular simulation, and modeling. Specific projects include genome sequence analysis of microbes like Ralstonia solanacearum and Pseudomonas putida, with a plan to develop a webserver for public access. The center has also developed algorithms to understand mutation spectrum, codon usage bias, and dN/dS, contributing significantly to molecular evolution studies.
Anticipated to evolve into a prominent cross-faculty interdisciplinary research hub in the field of computational biology and bioinformatics in the north-eastern region, the center will work on understanding the molecular basis of microbial diversity in relation to their adaptation in different habitats of Assam and Arunachal Pradesh, developing of a cloud based curated public database on regional biodiversity establishing a portal for cross-platform collaborative research, creation of supervised and unsupervised machine learning tools to support knowledge extraction, dissemination of knowledge and capacity building through training of students/faculty members in interdisciplinary research and outreach programs, understanding the potency and application of microorganisms in traditional practices for sustainable development, and elucidating the role of microbes in plant products such as aromatics, medicinal values, and horticultures.