Mathieu Blanchette

Associate Academic Member

Director and Associate Professor, McGill University, School of Computer Science

Research Topics

Computational Biology

Deep Learning

Graph Neural Networks

Website

Biography

Mathieu Blanchette is an associate professor and director of the School of Computer Science at McGill University.

After completing his PhD (University of Washington, 2002) and a postdoc (UC Santa Cruz, 2003), Blanchette joined the School of Computer Science at McGill, where he founded the Computational Genomics Lab. Research carried out in this lab has resulted in more than seventy publications.

Blanchette is an elected member of the College of New Scholars, Artists and Scientists of the Royal Society of Canada and a 2009 Sloan Research Fellow. He was awarded the Outstanding Young Computer Scientist Researcher Prize by the Canadian Association for Computer Science in 2012, and the Overton Prize in 2006.

He loves teaching and supervising students, for which he was honoured with McGill’s Leo Yaffe Award for Excellence in Teaching (2008).

Current Students

Noah El Rimawi-Fine

Master's Research - McGill University

Co-supervisor :

Siamak Ravanbakhsh

Github

Lucas Nelson

Master's Research - McGill University

Adam Stecklov

Master's Research - McGill University

Github

César Miguel Valdez Cordova

PhD - McGill University

Github

Google Scholar

Nicole Zhang

PhD - McGill University

Co-supervisor :

Yijie Zhang

PhD - McGill University

Website

Google Scholar

Publications

Multi-ancestry polygenic risk scores using phylogenetic regularization

2024-02-17

bioRxiv (preprint)

doi.org

PERFUMES: pipeline to extract RNA functional motifs and exposed structures

Arnaud Chol

Roman Sarrazin-Gendron

Éric Lécuyer

Mathieu Blanchette

Jérôme Waldispühl

Abstract Motivation Up to 75% of the human genome encodes RNAs. The function of many non-coding RNAs relies on their ability to fold into 3D… (see more) structures. Specifically, nucleotides inside secondary structure loops form non-canonical base pairs that help stabilize complex local 3D structures. These RNA 3D motifs can promote specific interactions with other molecules or serve as catalytic sites. Results We introduce PERFUMES, a computational pipeline to identify 3D motifs that can be associated with observable features. Given a set of RNA sequences with associated binary experimental measurements, PERFUMES searches for RNA 3D motifs using BayesPairing2 and extracts those that are over-represented in the set of positive sequences. It also conducts a thermodynamics analysis of the structural context that can support the interpretation of the predictions. We illustrate PERFUMES’ usage on the SNRPA protein binding site, for which the tool retrieved both previously known binder motifs and new ones. Availability and implementation PERFUMES is an open-source Python package (https://jwgitlab.cs.mcgill.ca/arnaud_chol/perfumes).

2024-01-30

Bioinform. (published)

doi.org

Graphylo: A deep learning approach for predicting regulatory DNA and RNA sites from whole-genome multiple alignments

Dongjoon Lim

Changhyun Baek

Mathieu Blanchette

2024-01-26

iScience (published)

doi.org

PhyloGFN: Phylogenetic inference with generative flow networks

Ming Yang Zhou

Moksh J. Jain

2024-01-16

ICLR.cc/2024/Conference (poster)

doi.org

openreview.net

Learning the Game: Decoding the Differences between Novice and Expert Players in a Citizen Science Game with Millions of Players

Eddie Cai

Roman Sarrazin-Gendron

Renata Mutalova

Parham Ghasemloo Gheidari

Alexander Butyaev

Gabriel Richard

Sébastien Caisse

Rob Knight

Mathieu Blanchette

Attila Szantner

Jérôme Waldispühl

2024-01-01

FDG (published)

doi.org

H3K27me3 spreading organizes canonical PRC1 chromatin architecture to regulate developmental programs

Brian Krug

Bo Hu

Haifen Chen

Adam Ptack

Xiao Chen

Kristjan H. Gretarsson

Shriya Deshmukh

Nisha Kabir

Augusto Faria Andrade

Elias Jabbour

Ashot S. Harutyunyan

John J. Y. Lee

Maud Hulswit

Damien Faury

Caterina Russo

Xinjing Xu

Michael Johnston

Audrey Baguette

Nathan A. Dahl

Alexander G. Weil … (see 12 more)

Benjamin Ellezam

Rola Dali

Mathieu Blanchette

Khadija Wilson

Benjamin A. Garcia

Rajesh Kumar Soni

Marco Gallo

Michael D. Taylor

Claudia Kleinman

Jacek Majewski

Nada Jabado

Chao Lu

2023-11-28

bioRxiv (preprint)

doi.org

Player-Guided AI outperforms standard AI in Sequence Alignment Puzzles

Renata Mutalova

Roman Sarrazin-Gendron

Parham Ghasemloo Gheidari

Eddie Cai

Gabriel Richard

Sébastien Caisse

Rob Knight

Mathieu Blanchette

Attila Szantner

Jérôme Waldispühl

2023-11-05

International Conference on Climate Informatics (published)

doi.org

PhyloGFN: Phylogenetic inference with generative flow networks

Ming Yang Zhou

Moksh J. Jain

Phylogenetics is a branch of computational biology that studies the evolutionary relationships among biological entities. Its long history a… (see more)nd numerous applications notwithstanding, inference of phylogenetic trees from sequence data remains challenging: the high complexity of tree space poses a significant obstacle for the current combinatorial and probabilistic techniques. In this paper, we adopt the framework of generative flow networks (GFlowNets) to tackle two core problems in phylogenetics: parsimony-based and Bayesian phylogenetic inference. Because GFlowNets are well-suited for sampling complex combinatorial structures, they are a natural choice for exploring and sampling from the multimodal posterior distribution over tree topologies and evolutionary distances. We demonstrate that our amortized posterior sampler, PhyloGFN, produces diverse and high-quality evolutionary hypotheses on real benchmark datasets. PhyloGFN is competitive with prior works in marginal likelihood estimation and achieves a closer fit to the target distribution than state-of-the-art variational inference methods. Our code is available at https://github.com/zmy1116/phylogfn.

2023-10-12

ArXiv (preprint)

doi.org

arxiv.org

Reference panel-guided super-resolution inference of Hi-C data

Yanlin Zhang

Mathieu Blanchette

Abstract Motivation Accurately assessing contacts between DNA fragments inside the nucleus with Hi-C experiment is crucial for understanding… (see more) the role of 3D genome organization in gene regulation. This challenging task is due in part to the high sequencing depth of Hi-C libraries required to support high-resolution analyses. Most existing Hi-C data are collected with limited sequencing coverage, leading to poor chromatin interaction frequency estimation. Current computational approaches to enhance Hi-C signals focus on the analysis of individual Hi-C datasets of interest, without taking advantage of the facts that (i) several hundred Hi-C contact maps are publicly available and (ii) the vast majority of local spatial organizations are conserved across multiple cell types. Results Here, we present RefHiC-SR, an attention-based deep learning framework that uses a reference panel of Hi-C datasets to facilitate the enhancement of Hi-C data resolution of a given study sample. We compare RefHiC-SR against tools that do not use reference samples and find that RefHiC-SR outperforms other programs across different cell types, and sequencing depths. It also enables high-accuracy mapping of structures such as loops and topologically associating domains. Availability and implementation https://github.com/BlanchetteLab/RefHiC.

2023-06-30

Bioinformatics (published)

doi.org

Playing the System: Can Puzzle Players Teach us How to Solve Hard Problems?

Renata Mutalova

Roman Sarrazin-Gendron

Eddie Cai

Gabriel Richard

Parham Ghasemloo Gheidari

Sébastien Caisse

Rob Knight

Mathieu Blanchette

Attila Szantner

Jérôme Waldispühl

2023-04-19

International Conference on Human Factors in Computing Systems (published)

doi.org

Detection and genomic analysis of BRAF fusions in Juvenile Pilocytic Astrocytoma through the combination and integration of multi-omic data

Melissa Zwaig

Audrey Baguette

Bo Hu

Michael Johnston

Hussein Lakkis

Emily M. Nakada

Damien Faury

Nikoleta Juretic

Benjamin Ellezam

Alexandre G. Weil

Jason Karamchandani

Jacek Majewski

Mathieu Blanchette

Michael D. Taylor

Marco Gallo

Claudia Kleinman

Nada Jabado

Jiannis Ragoussis

2022-12-12

BMC Cancer (published)

doi.org

Reference panel guided topological structure annotation of Hi-C data

Yanlin Zhang

Mathieu Blanchette

2022-12-01

Nature Communications (published)

doi.org

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Leading in a New Era

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Mathieu Blanchette

Biography

Current Students

Publications

Speed Science

Leading in a New Era

Supervision Requests

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Mathieu Blanchette

Biography

Current Students

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