Portrait of Mathieu Blanchette

Mathieu Blanchette

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

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
noah.elrimawi-fine@mila.quebec
Elliot Layne
PhD - McGill University
elliot.layne@mila.quebec
Lucas Nelson
Undergraduate
lucas.nelson@mila.quebec
Cesar Miguel Valdez Cordova
PhD - McGill University
cesar.valdez@mila.quebec
Nicole Zhang
PhD - McGill University
Co-supervisor :
xi.zhang@mila.quebec

Publications

Improving microbial phylogeny with citizen science within a mass-market video game
Roman Sarrazin-Gendron
Parham Ghasemloo Gheidari
Alexander Butyaev
Timothy Keding
Eddie Cai
Jiayue Zheng
Renata Mutalova
Julien Mounthanyvong
Yuxue Zhu
Elena Nazarova
Chrisostomos Drogaris
Kornél Erhart
David Bélanger
Amélie Brouillette
Michael Bouffard
Gabriel Richard
Joshua Davidson
Randy Pitchford
Mathieu Falaise
Sébastien Caisse … (see 14 more)
Vincent Fiset
Steven Hebert
Daniel McDonald
Dan Hewitt
Rob Knight
Jonathan Huot
Attila Szantner
Seung Kim
Jérôme Waldispühl
Jonathan Moreau-Genest
David Najjab
Steve Prince
Ludger Saintélien
Posterior inference of Hi-C contact frequency through sampling
Yanlin Zhang
Christopher J. F. Cameron
Hi-C is one of the most widely used approaches to study three-dimensional genome conformations. Contacts captured by a Hi-C experiment are r… (see more)epresented in a contact frequency matrix. Due to the limited sequencing depth and other factors, Hi-C contact frequency matrices are only approximations of the true interaction frequencies and are further reported without any quantification of uncertainty. Hence, downstream analyses based on Hi-C contact maps (e.g., TAD and loop annotation) are themselves point estimations. Here, we present the Hi-C interaction frequency sampler (HiCSampler) that reliably infers the posterior distribution of the interaction frequency for a given Hi-C contact map by exploiting dependencies between neighboring loci. Posterior predictive checks demonstrate that HiCSampler can infer highly predictive chromosomal interaction frequency. Summary statistics calculated by HiCSampler provide a measurement of the uncertainty for Hi-C experiments, and samples inferred by HiCSampler are ready for use by most downstream analysis tools off the shelf and permit uncertainty measurements in these analyses without modifications.
Multi-ancestry polygenic risk scores using phylogenetic regularization
Elliot Layne
Shadi Zabad
PERFUMES: pipeline to extract RNA functional motifs and exposed structures
Arnaud Chol
Roman Sarrazin-Gendron
Éric Lécuyer
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).
Graphylo: A deep learning approach for predicting regulatory DNA and RNA sites from whole-genome multiple alignments
Dongjoon Lim
Changhyun Baek
PhyloGFN: Phylogenetic inference with generative flow networks
Ming Yang Zhou
Zichao Yan
Elliot Layne
Nikolay Malkin
Dinghuai Zhang
Moksh J. Jain
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
Khadija Wilson
Benjamin A. Garcia
Rajesh Kumar Soni
Marco Gallo
Michael D. Taylor
Claudia Kleinman
Jacek Majewski
Nada Jabado
Chao Lu
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
Attila Szantner
Jérôme Waldispühl
Reference panel-guided super-resolution inference of Hi-C data
Yanlin Zhang
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.
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
Attila Szantner
Jérôme Waldispühl
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
Michael D. Taylor
Marco Gallo
Claudia Kleinman
Nada Jabado
Jiannis Ragoussis
Reference panel guided topological structure annotation of Hi-C data
Yanlin Zhang