Sébastien Lemieux

Associate Academic Member

Associate Professor, Université de Montréal, Department of Computer Science and Operations Research and Department of Biochemistry and Molecular Medicine

Research Topics

Computational Biology

Molecular Modeling

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Biography

Sébastien Lemieux trained as a microbiologist but turned to bioinformatics in 1997, completing his MSc and PhD at Université de Montréal under the supervision of François Major. After obtaining his PhD in 2002, he headed to the private sector for postdoctoral training at Elitra Canada (now Merck & Co) under the supervision of Bo Jiang. There he acquired skills in sequence analysis and the analysis of DNA microarray data, as well as in the integration of experimental data with computational techniques.

Lemieux joined Université de Montréal in 2005, first at the Institute for Research in Immunology and Cancer (IRIC). In 2018, he was appointed associate professor in the Department of Biochemistry and Molecular Medicine of the Faculty of Medicine.

Current Students

Serena Chan

Master's Research - Université de Montréal

Website

Léa Kaufmann

Master's Research - Université de Montréal

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Github

Publications

CAMAP: Artificial neural networks unveil the role of 1 codon arrangement in modulating MHC-I peptides 2 presentation discovery of minor histocompatibility with

Tariq Daouda

Maude Dumont-Lagacé

Albert Feghaly

Yahya Benslimane

6. Rébecca

Panes

Mathieu Courcelles

Mohamed Benhammadi

Lea Harrington

Pierre Thibault

François Major

Yoshua Bengio

Étienne Gagnon

Sébastien Lemieux

Claude Perreault

30 MHC-I associated peptides (MAPs) play a central role in the elimination of virus-infected and 31 neoplastic cells by CD8 T cells. However… (see more), accurately predicting the MAP repertoire remains 32 difficult, because only a fraction of the transcriptome generates MAPs. In this study, we 33 investigated whether codon arrangement (usage and placement) regulates MAP biogenesis. We 34 developed an artificial neural network called Codon Arrangement MAP Predictor (CAMAP), 35 predicting MAP presentation solely from mRNA sequences flanking the MAP-coding codons 36 (MCCs), while excluding the MCC per se . CAMAP predictions were significantly more accurate 37 when using original codon sequences than shuffled codon sequences which reflect amino acid 38 usage. Furthermore, predictions were independent of mRNA expression and MAP binding affinity 39 to MHC-I molecules and applied to several cell types and species. Combining MAP ligand scores, 40 transcript expression level and CAMAP scores was particularly useful to increaser MAP prediction 41 accuracy. Using an in vitro assay, we showed that varying the synonymous codons in the regions 42 flanking the MCCs (without changing the amino acid sequence) resulted in significant modulation 43 of MAP presentation at the cell surface. Taken together, our results demonstrate the role of codon 44 arrangement in the regulation of MAP presentation and support integration of both translational 45 and post-translational events in predictive algorithms to ameliorate modeling of the 46 immunopeptidome. 47 48 49 they modulated the levels of SIINFEKL presentation in both constructs, but enhanced translation efficiency could only be detected for OVA-RP. These data show that codon arrangement can modulate MAP presentation strength without any changes in the amino

Factorized embeddings learns rich and biologically meaningful embedding spaces using factorized tensor decomposition

Claude Perreault

2020-07-01

Bioinform. (published)

doi.org