The idea of designing high-resolution genotyping markers via clonal diversity-based extraction of marker variations using machine learning algorithm would be novel and pioneering in this field. Successful completion of this project will lead to large-scale population-level clonal epidemiological study across West Bengal, offering the potential of rapid, powerful and affordable point-of-care diagnostic tool and optimized antibiotic treatment opportunity against shigellosis.
The development of a public resource database incorporating analysis-results to validate and measure robustness of the tool via future studies, and to extend this approach to other pathogens alarmingly circulating across our state and nationwide, is novel as well.

Objectives:

• Core genomic profiling to map core gene-variations of Shigella within and across species.
• Genome-wide association studies of core variations with distinct antibiogram profiles to identify high-resolution genotyping markers.
• Designing a public resource database to aid future population genomics studies.

News

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Relevant Publications:

• Thomas AK, Preetha S, Omanakuttan A, Vidyullata L, Ashokan A, Rajachandran V, Chattopadhyay S, Mutational convergence acts as a major player in adaptive parallel evolution of Shigella spp. Scientific reports. 2019 Mar 1;9(1):3252.
• Chowdhury RR, Dhar J, Robinson SM, Lahiri A, Basak K, Paul S, Banerjee R. MACI: A machine learning-based approach to identify drug classes of antibiotic resistance genes from metagenomic data. Computers in Biology and Medicine. 2023 Dec 1;167:107629