Event Details
Thursday, February 26, 2026
5:00 PM - 6:30 PM
Room ED 265, Education Building South, University of Alberta
University of Alberta, Edmonton, AB
Description
Population-scale human phenotypes to find novel drug targets and insights into the global burden of disease
Gane Wong
Professor
Department of Biological Sciences
University of Alberta
This presentation explores a new approach to finding new medicines using data from millions of people. Instead of developing drugs in the traditional way, this method uses health records and genetic information combined together.
How it works:
The approach uses two key pieces of information: health data from large populations and genetic/molecular data (omics). By combining these, researchers can identify which genes or proteins might be good targets for new drugs.
Who benefits:
Doctors and hospitals get new treatment options for complex diseases that affect many body systems. Pharmaceutical and biotech companies get a better way to predict which drug targets will be approved by regulators. Researchers can use data science to predict how drugs will affect different diseases.
The big picture:
This method relies on having access to massive healthcare databases that include patient health records and genetic information. The UK Biobank is currently the best example of this type of combined dataset. However, large healthcare systems in Asia have the potential to become leaders in this field because they have access to even larger populations of patient data.
This approach could transform how we discover new medicines and understand disease.
TransXplorer: An Automated End-to-End Web Server for Translational RNA-seq Analysis and Therapeutic Discovery
Varinder Madhav Verma
PhD Candidate
Department of Biological Sciences
University of Alberta
TransXplorer bridges the gap between RNA-seq data and therapeutic discovery. This free, no-login web server automates complex bioinformatics workflows, including:
- Quantitative batch effect detection and correction using quantitative metrics (PVCA, kBET, Silhouette)
- Multi-algorithm differential expression analysis (DESeq2/edgeR/limma)
- Pathway enrichment analysis
- WGCNA co-expression networks
- Cell-type deconvolution
Drug-Target Discovery Module: Its unique drug-target discovery module queries DGIdb and Open Targets in real time, mapping differentially expressed genes directly to FDA-approved drugs and clinical trial candidates, enabling immediate hypothesis generation for translational research.
Clinical Validation: It also helps in clinical validation of human candidate genes against the TCGA (The Cancer Genome Atlas) database by providing Kaplan-Meier survival analysis results.
Topics
phenotypes drug targets RNA-Seq bioinformaticsPart of: YegBUG Seminar Series 2025-2026