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ICAR-IASRI New Delhi

Cultivating Agricultural Advancements: Bioinformatics Initiatives on Agri-Genomic Repository and Intelligent Analytical System at ICAR-IASRI New Delhi

The Bioinformatics Center at ICAR-IASRI focuses on research, teaching, and training in bioinformatics across all domains of Agriculture namely Farm Animals, Agriculture (including various crops and horticulture), and Fisheries, making it unique among other institutions. The primary objective is to develop a need-based computational methodology, algorithms/tools, and software for the analysis of biological data in agriculture. This includes creating genomic data warehouses/databases for the efficient storage, analysis, and dissemination of large biological information. The center aims to support agricultural bioinformatics projects across institutions, providing computational support and assistance for bioinformatics analysis. Furthermore, the goal is to develop human resources through teaching, training, workshops, and conferences. The center has successfully trained 22 Masters/B-Tech students in Bioinformatics. The Center utilizes the latest technologies related to genomics, proteomics, and metagenomics data analysis and has developed several databases and R packages.

One significant accomplishment is the creation of a database for the transcriptome data of Bada Gokhuru, a valuable medicinal herb. Utilizing the LAMP approach, the database was analyzed to identify different transcripts and microsatellites in the plant. The center also employed a drug repurposing approach to identify therapeutics for COVID-19, targeting the main protease (Mpro) enzyme. In-silico screening of 400 bioactive inhibitors revealed ten compounds with higher binding affinity for Mpro than the reference compound, and three compounds (MMV1782211, MMV1782220, and MMV 1578574) were found to actively interact with the catalytic domain of Mpro. The study suggests that MMV1782211 is a potent molecule to target Mpro and can be explored in vitro and in vivo to combat COVID-19.

Additionally, the center explores the functional activity of STAT3 and its downstream kinase effector, PIM1, and their interaction with docetaxel. Molecular dynamics simulation and analysis revealed stable heterodimeric STAT3-PIM1-Docetaxel complexes with strong interaction. Despite docetaxel decreasing cell viability and changing cellular morphology in breast cancer cells, constitutive active STAT3 and PIM1 expression increased after docetaxel treatment, causing EMT in cancer cells. The center also developed a machine learning-based methodology for predicting methylation states in the genome and an R package called “EpiSemble,” an Ensemble-Based Machine Learning Approach for Predicting Methylation States (https://CRAN.R project.org/package=EpiSemble ).