Portrait de Mathieu Blanchette

Mathieu Blanchette

Membre académique associé
mathieu.blanchette@mila.quebec
Directeur et professeur associé, McGill University, École d'informatique
Sujets de recherche
Apprentissage profond
Biologie computationnelle
Réseaux de neurones en graphes
Site web

Biographie

Mathieu Blanchette est professeur associé et directeur de l'École d'informatique de l'Université McGill.

Après avoir obtenu un doctorat (Université de Washington, 2002) et un postdoctorat (Université de Californie à Santa Cruz, 2003), il s'est joint à l'École d'informatique de l’Université McGill et a fondé le Laboratoire de génomique computationnelle. Les recherches effectuées par son équipe d’exception ont fait l'objet de plus de 70 publications. Récemment élu membre du Collège de nouveaux chercheurs et créateurs en art et science de la Société royale du Canada, il a été boursier Sloan (2009) et a reçu le prix Outstanding Young Computer Scientist Researcher de l'Association canadienne de l'informatique (2012) ainsi que le prix Chris Overton (2006). Il adore enseigner et superviser les étudiant·e·s, et a d’ailleurs reçu le prix Leo Yaffe pour l'enseignement (2008).

Étudiants actuels

Publications

Reference panel guided topological structure annotation of Hi-C data
Yanlin Zhang
Mathieu Blanchette
2022-12-01
Nature Communications (publié)
doi.org
K27M in canonical and noncanonical H3 variants occurs in distinct oligodendroglial cell lineages in brain midline gliomas
Selin Jessa
Abdulshakour Mohammadnia
Ashot S. Harutyunyan
Maud Hulswit
Srinidhi Varadharajan
Hussein Lakkis
Nisha Kabir
Zahedeh Bashardanesh
Steven Hébert
Damien Faury
Maria C. Vladoiu
Samantha Worme
Marie Coutelier
Brian Krug
Augusto Faria Andrade
Manav Pathania
Andrea Bajic
Alexander G. Weil
Benjamin Ellezam
Jeffrey Atkinson … (voir 14 de plus)
Roy W. R. Dudley
Jean-Pierre Farmer
Sebastien Perreault
Benjamin A. Garcia
Valérie Larouche
Mathieu Blanchette
Livia Garzia
Aparna Bhaduri
Keith L. Ligon
Pratiti Bandopadhayay
Michael D. Taylor
Stephen C. Mack
Nada Jabado
Claudia L. Kleinman
Canonical (H3.1/H3.2) and noncanonical (H3.3) histone 3 K27M-mutant gliomas have unique spatiotemporal distributions, partner alterations, a… (voir plus)nd molecular profiles. The contribution of the cell-of-origin to these differences has been challenging to uncouple from the oncogenic reprogramming induced by the mutation. Here, we perform an integrated analysis of 116 tumors, including single-cell transcriptome and chromatin accessibility, 3D chromatin architecture and epigenomic profiles, and show that K27M-mutant gliomas faithfully maintain chromatin configuration at developmental genes consistent with anatomically distinct oligodendrocyte-precursor-like cells (OPC). H3.3K27M thalamic gliomas map to prosomere 2-derived lineages. In turn, H3.1K27M ACVR1-mutant pontine gliomas uniformly mirror early ventral NKX6-1+/SHH-dependent brainstem OPCs, while H3.3K27M gliomas frequently resemble dorsal PAX3+/BMP-dependent progenitors. Our data suggest a context-specific vulnerability in H3.1K27M-mutant SHH-dependent ventral OPCs, which rely on acquisition of ACVR1 mutations to drive aberrant BMP signaling required for oncogenesis. The unifying action of K27M mutations is to restrict H3K27me3 at PRC2 landing sites, while other epigenetic changes are mainly contingent on the cell-of-origin chromatin state and cycling rate.
2022-11-30
Nature Genetics (inconnu)
doi.org
Type B Ultra Long-Range Interactions in PFAS (TULIPs) Are Recurrent Epigenomic Features of PFA Ependymoma
Michael Johnston
John JY Lee
Bo Hu
Ana Nikolic
Audrey Baguette
Seungil Paik
Haifen Chen
Sachin Kumar
Carol Chen
Selin Jessa
Polina Balin
Vernon Fong
Melissa Zwaig
Kulandaimanuvel MichealRaj
Xun Chen
Yanlin Zhang
Srinidhi Varadharajan
Pierre Billon
Nikoleta Juretic
Craig Daniels … (voir 21 de plus)
Caterina Giannini
Eric Thompson
Peter Hauser
Seung-Ki Kim
Kyu-Chang Wang
Ji Yeoun Lee
Wieslawa Grajkowska
Sameer Agnihotri
Stephen C. Mack
Benjamin Ellezam
Alex Weil
Guillaume Bourque
Jennifer Chan
Mathieu Lupien
Jiannis Ragoussis
Claudia Kleinman
Jacek Majewski
Nada Jabado
Michael Taylor
Mathieu Blanchette
Marco Gallo
Posterior Fossa Group A (PFA) ependymomas are pediatric brain tumors with extremely poor survival outcomes. As protein-coding mutations in P… (voir plus)FA are exceedingly rare, the underlying etiology of these tumors remains elusive. Elevated CpG island methylation and depletion of H3K27me3 have been described in PFA, leading to the hypothesis that PFA may be driven by a dysregulated epigenetic state. In this study, we sought to determine how three-dimensional (3D) genome features (such as DNA loops, domains, and compartments) differ between pediatric brain tumors. We performed Hi-C sequencing on a collection of 64 patient specimens and patient-derived primary cultures that collectively span multiple subgroups of ependymoma, medulloblastoma, high-grade glioma, and non-neoplastic brain. For certain samples, we further performed RNA-seq, histone modification ChIP-seq, or whole-genome bisulfite sequencing to allow multiomic data integration. Overall, the 3D genome organization of PFA samples appeared distinct from other tumor types. We identified and defined TULIPs: a subset of type B compartments, separated by genomic distances greater than 10 Mbp, that exhibit a striking fivefold increase in reciprocal interaction strength. These TULIPs recurred at the same genomic positions across the vast majority of PFA samples with minimal representation among other tumor or non-tumor samples. TULIPs displayed enrichment for heterochromatic features such as H3K9me3 and late replication timing and were depleted of euchromatic features such as H3K27ac and protein-coding genes. By using immuno-fluorescence for H3K9me3 and oligo-FISH to label TULIP regions, we demonstrated that TULIP regions are more compact in PFA than other tumors. Finally, by applying inhibitors of H3K9 lysine methylation to PFA cultures we showed that TULIPs become more diffuse and cell viability is reduced. Altogether, this work defines TULIPs as highly recurrent epigenetic features of PFA tumors.
2022-10-31
Neuro-Oncology (publié)
doi.org
Leishmania parasites exchange drug-resistance genes through extracellular vesicles
Noélie Douanne
George Dong
Atia Amin
Lorena Bernardo
Mathieu Blanchette
David Langlais
Martin Olivier
Christopher Fernandez-Prada
2022-06-30
Cell Reports (publié)
doi.org
Reconstruction of full-length LINE-1 progenitors from ancestral genomes
Laura F Campitelli
Isaac Yellan
Mihai Albu
Marjan Barazandeh
Zain M Patel
Mathieu Blanchette
Timothy R Hughes
2022-05-11
Genetics (publié)
doi.org
PhyloPGM: boosting regulatory function prediction accuracy using evolutionary information
Faizy Ahsan
Zichao Yan
Doina Precup
Mathieu Blanchette
Supplementary data are available at Bioinformatics online.
2022-04-13
Bioinformatics (publié)
doi.org
Integrated pretraining with evolutionary information to improve RNA secondary structure prediction
Zichao Yan
William Hamilton
Mathieu Blanchette
A bstract RNA secondary structure prediction is a fundamental task in … (voir plus)computational and molecular biology. While machine learning approaches in this area have been shown to improve upon traditional RNA folding algorithms, performance remains limited for several reasons such as the small number of experimentally determined RNA structures and suboptimal use of evolutionary information. To address these challenges, we introduce a practical and effective pretraining strategy that enables learning from a larger set of RNA sequences with computationally predicted structures and in the meantime, tapping into the rich evolutionary information available in databases such as Rfam. Coupled with a flexible and scalable neural architecture that can navigate different learning scenarios while providing ease of integrating evolutionary information, our approach significantly improves upon state-of-the-art across a range of benchmarks, including both single sequence and alignment based structure prediction tasks, with particularly notable benefits on new, less well-studied RNA families. Our source code, data and packaged RNA secondary structure prediction software RSSMFold can be accessed at https://github.com/HarveyYan/RSSMFold .
2022-01-27
bioRxiv (prépublication)
doi.org
Phylogenetic Manifold Regularization: A semi-supervised approach to predict transcription factor binding sites
Faizy Ahsan
Alexandre Drouin
Francois Laviolette
Doina Precup
Mathieu Blanchette
The computational prediction of transcription factor binding sites remains a challenging problems in bioinformatics, despite significant m e… (voir plus)thodological d evelopments f rom t he field of machine learning. Such computational models are essential to help interpret the non-coding portion of human genomes, and to learn more about the regulatory mechanisms controlling gene expression. In parallel, massive genome sequencing efforts have produced assembled genomes for hundred of vertebrate species, but this data is underused. We present PhyloReg, a new semi-supervised learning approach that can be used for a wide variety of sequence-to-function prediction problems, and that takes advantage of hundreds of millions of years of evolution to regularize predictors and improve accuracy. We demonstrate that PhyloReg can be used to better train a previously proposed deep learning model of transcription factor binding. Simulation studies further help delineate the benefits o f t he a pproach. G ains in prediction accuracy are obtained over a broad set of transcription factors and cell types.
2020-12-15
2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (publié)
doi.org
3DGV: Immersive Exploration of 3D Genome Structures using Virtual Reality
Éric Zhang
Chrisostomos Drogaris
Antoine Gédon
Aaron Sossin
Rajae Faraj
Haifen Chen
Yan Cyr
Jacek Majewski
Mathieu Blanchette
Jérôme Waldispühl
The analysis of 3D genomic data is expected to revolutionize our understanding of genome organization and regulatory mechanisms. Yet, the co… (voir plus)mplex spatial organization of this information can be difficult to interpret with 2D viewers. Virtual Reality (VR) technologies offer an opportunity to rethink our methods to visualize and navigate 3D objects. In this paper, we introduce the Virtual Reality 3D Genome Viewer ( 3DGV ), an open platform to experiment and develop VR solutions to explore 3D genome structures. http://3dgv.cs.mcgill.ca/
2019-11-25
bioRxiv (prépublication)
doi.org
Prediction of Cell Type Specific Transcription Factor Binding Site Occupancy
Faizy Ahsan
Doina Precup
Mathieu Blanchette
2016-10-01
ACM International Conference on Bioinformatics, Computational Biology and Biomedicine (publié)
doi.org
Algorithms in Bioinformatics
P. Agarwal
Tatsuya Akutsu
Amir Amihood
Alberto Apostolico
C. Benham
Gary Gustaf Benson
Mathieu Blanchette
Nadia El-Mabrouk
Olivier Gascuel
Raffaele Giancarlo
R. Guigó
Michael Hallet
D. Huson
G. Kucherov
Michelle R. Lacey
Jens Lagergren
Giuseppe Lancia
Gad M. Landau
Thierry. Lecroq
B. Moret … (voir 21 de plus)
S. Morishita
Elchanan Mossel
Vincent Moulton
Lior S. Pachter
Knut Reinert
I. Rigoutsos
David Sankoff
Sophie Schbath
Eran Segal
Charles Semple
J. Setubal
Roded Sharan
S. Skiena
Jens Stoye
Esko Ukkonen
Lisa Allen Vawter
Alfonso Valencia
Tandy J. Warnow
Lusheng Wang
Rita Casadio
Gene Myers
2012-09-30
Lecture Notes in Computer Science (publié)
doi.org