Mathieu Lavallee-Adam, Ph.D

Associate Professor at the University of Ottawa in the Department
of Biochemistry, Microbiology and Immunology, affiliated to the Ottawa Institute of Systems Biology

Novel supervised learning algorithms for real-time optimization of mass
spectrometry-based proteomics data acquisition to increase proteome and
interactome coverage

Abstract

Maximizing the information extracted from biological samples using mass spectrometry-based
proteomics remains a challenge. Many data acquisition techniques redundantly collect mass
spectra from abundant proteins at the expense of less abundant ones. Additionally, when coupled to affinity purification, mass spectrometry data acquisition is plagued by contamination events that expend mass spectrometry resources that could be used to detect biologically relevant protein interactions. Computationally identifying proteins in real-time during mass spectrometry experiments can prevent such undesirable data acquisitions and efficiently distribute resources.

Herein, we present a logistic regression classifier that identifies proteins in real-time and
improves data acquisition of the Bruker timsTOF Pro instrument. Our model identifies 98% of
the proteins of a standard experiment using only 72% of the mass spectra. This represents a large gain in efficiency with minimal protein identification losses. We also developed a Bayesian
model to detect contamination events in affinity purification experiments coupled to mass
spectrometry. When tested on a subset of the BioPlex dataset, our method identified a similar
number of protein interactions as the popular SAINT tool, but at a higher confidence level.
Overall, our novel methods use real-time analysis of mass spectrometry data to efficiently
distribute its resources and improve proteome characterization.

Biosketch

Mathieu Lavallée-Adam is an Associate Professor at the University of Ottawa in the Department
of Biochemistry, Microbiology and Immunology and is affiliated to the Ottawa Institute of
Systems Biology. He obtained a B.Sc. in Computer Science and a Ph.D. in Computer Science,
Bioinformatics option, from McGill University. He then performed his postdoctoral research at
The Scripps Research Institute. His research focuses on the development of statistical and machine learning algorithms for the analysis of mass spectrometry-based proteomics data and proteinprotein interaction networks. He also designs computational methods mining proteomics datasets for biological information through their integration with genomics data. Dr. Lavallée-Adam is a recipient of the John Charles Polanyi Prize in Chemistry, rewarding the impact of his bioinformatics algorithms on the mass spectrometry community. One of his publications was also recognized as the 2018 Highlight of the Year by an early career researcher at the Human Proteome Organization (HUPO) World Congress. He was named Early Career Researcher of the Year by the Ottawa Institute for Systems Biology in 2021 and he received the New Investigator Award from the Canadian National Proteomics Network in 2023. He is also Co-Chair of the HUPO Early Career Researcher Initiative and a member of the HUPO Executive Committee, in which he develops training activities, advocates for junior investigators in proteomics, and organizes events highlighting their research on the international stage. As VP Operations of the Canadian National Proteomics Network, Dr. Lavallée-Adam also mentors the Operations Management Team, which builds a set of online activities to bring Canadian proteomics researchers together all year round.

Date
Date(s) - December 14, 2023
6:00 pm - 8:30 pm

Emplacement / Location
Université de Montréal - Campus MIL (Beer and pizza at 18h, conference at 19h in A-4502)