Genetic variation in individuals provides a roadmap towards understanding aspects of the past such as population history and evolutionary patterns and also a lens into the future regarding an individual’s predisposition to disease and therapeutic responses. Diverse communities from India have been historically underrepresented in global genomic research. We aim to contribute to the genetic landscape by utilizing cutting edge technologies to identify variations such as SNPs, Structural Variations (SVs) and Short Tandem Repeats (STRs) from healthy Indian populations. This resource would provide a valuable context in disease. Our clinical efforts include devising methodologies in detection and analysis of genetic disorders and cancers.
DNA base modifications such as methylation is one of the key epigenetic mechanisms which regulate gene expression. While cytosine methylation such as 5-methyl-cytosine (5mC) has been heavily studied for decades, the field of adenine methylation is still nascent. Long read sequencing technologies such as Oxford Nanopore (ONT) can simultaneously measure bases and epigenetic states in a single read. While 5mC models are well characterized, there is a dearth of models for 6-methyladenine (6mA) identification from raw ONT data. We work on generating accurate 6mA detection models and apply them to diverse datasets of clinical relevance.
With declining costs of sequencing, there is wider adoption of genomic technologies leading to a boom in data generation. There exists a lacuna for novel methods to mine this data for insights. An area particularly of interest to us is the extensive characterization of the repeat landscape. For this, we’ve built several novel algorithms for rapid identification of Tandem repeats (TRs) and Short Tandem Repeats (STRs). Using these tools, we developed a database housing STR data from > 40K genomes. Ongoing efforts include developing tools for population analysis. In the AI space, we are interested in developing accurate models utilizing data from long read technologies. At present, we are focused on applying our in-house trained methylation models for a variety of clinical analysis.
Genomic surveillance has been in the limelight in view of pandemic preparedness. Wastewater based epidemiology or surveillance is an effective tool for monitoring the spread of infectious diseases in a population-wide way. This approach is currently being utilized to monitor the dynamics of SARS-CoV-2 via viral load in samples to identify infection hotspots. We extend this tool to also perform environmental surveillance of antimicrobial resistance (AMR) across India. Due to misuse of antimicrobials, drug resistant pathogens are increasingly becoming a global threat. We apply metagenomic approaches which can quantify AMR burden and identify genomic signatures contributing to AMR. With this work, we aim to make our surveillance data actionable which can be used to build interventions for public health.