Current Students

Supervisor: John Calarco, Department of Cell and Systems Biology

Alternative splicing is a tightly regulated process which forms a crucial layer of gene expression and exerts its effects in a tissue-specific manner. My project is centred on identifying the elements involved in the regulation of alternative splicing. The cis-elements are sequence determinants of alternative splicing which are recognized by trans-factors which result in diverse splicing patterns. We have employed a random library approach to identify these elements and study their effects on splicing where minigene reporters with diverse random decamers as potential cis-elements are introduced into C.elegans and parallel in-vivo measurements are made by RNAseq. This leads to identification of activators, repressors and cryptic splice site inducers. This is followed by wet-lab validation involving reverse transcription PCR, bioinformatic analysis for identification of interacting trans-factors and locating these elements genome-wide. My main emphasis would be on developing sophisticated computational models to understand the regulation of alternative splicing. 

Supervisor: Benjamin Blencowe, Computational Biology in Molecular Genetics (CBMG)

The unifying theme of my graduate research is the development and application of computational and statistical approaches to uncover regulatory features that globally impact mRNA level and translation efficiency, leveraging large omics datasets. A key focus is modeling codon usage bias, particularly in identifying and characterizing rare codon patches and understanding their roles in RNA regulatory networks linked to critical biological processes. Using sliding window approaches, string-searching algorithms, and Monte Carlo simulations, I have identified subsets of genes with intriguing patterns of codon bias that contribute to the coordinated control of regulatory networks, with implications for understanding gene regulation in fundamental biological processes. Altogether, my project is expected to reveal critical insights about the nature and function of biological information stored in coding sequences through codon bias.

Supervisor: Farzad Khalvati, Institute of Medical Science

My Ph.D. thesis is focused on identifying molecular biomarkers of pediatric low-grade glioma (pLGG) using MRI and Artificial Intelligence (AI), as an alternative to the invasive procedure of biopsy. Additionally, I emphasize reproducible research, translational medicine, and Human-AI interaction. To that end, I have introduced OpenRadiomics, which provides the research community with the largest and most comprehensive open-source AI-ready radiomics data. OpenRadiomics also proposes a reproducible research protocol, stressing generalizable training and evaluation pipelines instead of individual trained models. The vision of my research is to make the pipelines end-to-end and extend them beyond pLGG.