Webinar: Rapid leukemia classification using nanopore sequencing
Nanopore sequencing technology can generate high-resolution transcriptomic data in real-time and at low cost, which heralds new opportunities for molecular medicine. We demonstrated the potential clinical utility of real-time transcriptomic profiling by processing RNA sequencing data from childhood acute lymphoblastic leukemia (ALL) clinical research samples on-the-fly with a trained neural network classifier. Here are our major findings:
- The pre-trained feedforward neural network accurately classified 11 out of 12 leukemia samples among 17 ALL molecular subtypes
- As little as 5 minutes of sequencing is sufficient to accurately classify ALL with more than 90% probability
- Inexpensive and disposable Flongle Flow Cells can be used to predict ALL subtype
Meet the speaker
Mélanie Sagniez is bioinformatic PhD student at the CHU Sainte-Justine Research Center & Department of Biochemistry and Molecular Medicine, Université de Montréal. After obtaining a biological engineering degree, Mélanie gained experience as a biobank manager and a clinical trials quality assurance officer in France. In 2020, she then took the decision to pursue a PhD in bioinformatics in Montréal, focusing on the future potential of rapid diagnosis and stratification of pediatric leukemias using nanopore sequencing.
