Research Projects
Cutting-edge research at the intersection of genetics, systems biology, and computational approaches
Research Focus Areas
Systems Genetics
Focusing on fundamental questions in genetic architecture, phenotypic plasticity, and evolutionary adaptation, primarily using yeast as a model system.
Genomic Diversity
Mapping India’s genetic diversity using genome graphs to advance precision medicine and public health.
AI Tools for Healthcare
Integrating multi-omics data to develop personalized treatment strategies for complex diseases.
How do Genetic Variations and Environmental Factors jointly shape Complex Traits?
We investigate how genetic and environmental factors interact to shape complex traits in yeast, using large-scale data integration and machine learning to map genotype-phenotype relationships. The goal is to uncover the molecular basis of adaptation and evolution by analyzing how gene-gene and gene-environment interactions drive phenotypic diversity
Project Objectives
- To understand how specific environments influence the genetic architecture of yeast, including how genetic and epigenetic factors interact to shape phenotypes. This involves investigating cases where the same genotype produces different, even opposite, effects in different environments, which has significant implications for evolution and adaptation
- To conduct large-scale analyses of genotype-phenotype data in yeast populations, both natural and synthetic, in order to identify how gene-gene interactions and environmental factors collectively determine the impact of genetic variation
- To characterise both additive and epistatic (non-additive) interactions between genes, and to understand how these interactions, together with environmental factors, influence quantitative traits in yeast
- To discover key molecular pathways that are crucial for adaptation to diverse environments, providing insights that can be extended to other organisms
Revealing Novel Biology through Integrative Multi-omics Analysis
We focus on reanalyzing large-scale multi-omics datasets-including genomic, transcriptomic, proteomic, metabolomic, and phenotypic data-to uncover new biological insights and answer emerging scientific questions. By integrating and mining diverse data types, this theme aims to reveal novel patterns in genotype-phenotype relationships and advance understanding of biology
Project Objectives
- To mine existing yeast multi-omics datasets for new biological insights by integrating and reanalysing diverse data types, thereby uncovering previously unrecognized patterns or relationships
- To explore and answer emerging scientific questions about genotype-phenotype relationships, molecular networks, and evolutionary processes using advanced computational and data science approaches
- To refine and expand our understanding of yeast adaptation and gene network evolution by leveraging extensive experimental studies, comprehensive data integration and analytics
- To explore the heterogeneity of ribosomal protein gene expression across human normal tissues and tumors at single-cell resolution, leveraging single-cell transcriptomic data to identify patterns that are specific to cell types, differentiation stages, and tumour progression
Mapping India’s Genetic Diversity: The GenomeIndia Project and the Power of Genome Graphs
The GenomeIndia Project is a nationwide initiative to map the genetic diversity of India by sequencing 10,000 genomes, creating a comprehensive reference that will advance disease research, precision medicine, and public health for the country’s diverse population
Project Objectives
- To create genome graph-based reference genome for Indian population comprehensively representing the novel and known genetic variants in the Indian genomes
- To develop genome graph-based comparison, visualisation and annotation tools to be able to compare, analyse and understnad the role of complex nodes in the genome graph
- To create sub-population-specific genome graphs of different populations of India to be able to identify unique sub-population specific genetic risk factors
Transforming Maternal and Child Healthcare in India: Precision Prediction, Digital Outreach, and Accessible Information
The maternal and child health project aims to significantly reduce preventable maternal, newborn, and infant deaths in India by advancing precision clinical tools and bridging information gaps. Its goals are to develop India-specific models for accurate gestational age and preterm birth prediction, identify key risk factors for adverse outcomes, and empower families with accessible, expert-driven health information through AI-powered, local-language digital platforms. By integrating clinical innovation with community outreach, the project supports timely interventions, promotes informed health-seeking behaviors, and aligns with national and global targets for maternal and child survival, quality care, and universal health coverage
Project Objectives
- Develop, evaluate and deploy India-specific predictive models for accurate gestational age estimation and preterm birth risk, tailored to the unique anthropometric characteristics of Indian populations
- Identify and validate key clinical and phenotypic risk factors associated with adverse maternal and neonatal outcomes, enabling targeted interventions and improved prenatal care
- Bridge the information gap by creating AI-powered, local-language digital tools to deliver reliable, expert-curated maternal and child health guidance directly to the community
Our Collaborators
Dr. Shinjini Bhatnagar
ICMR Distinguished Professor
Dr. Nitya Wadhwa
THSTI Faridabad
Dr. Gianni Liti
IRCAN, Nice, France
Dr. Suresh Sudarsan
DTU Biosustain, Denmark
Dr. Anand Jayasekaran
CSI-NUS, Singapore
Prof. Sriraam Natarajan
The University of Texas at Dallas, USA
Funding Support
Major Grants
- DBT: GenomeIndia: Cataloguing the genetic variation in Indians (2020-2025)
- DBT-ERC: Indo-European Consortium for Next Generation Influenza Vaccine Innovation INCENTIVE (2020-2025)
- DBT-BIRAC: Artificial Intelligence (AI) for Ultrasound program - A Multicentric Evaluation of Indian Population-Specific Tools for Antenatal Estimation of Gestational Age (2025-2027)
- DBT: Exploring the adaptive potential of ribosomal protein variants to develop antifungal drug resistance (2024-2027)
- DBT-BIRAC: Saving Lives, One Query at a Time: An LLM-Powered Native-language Companion for Pregnant Women (2024-2026)
Industry Partnerships
- Excelra: MOOCs on Biological Big Data Analysis