Portrait of Mathieu Blanchette

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
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 :
Yoshua Bengio
Github
Google Scholar
Yijie Zhang
PhD - McGill University
Website
Google Scholar

Publications

Reconstruction of full-length LINE-1 progenitors from ancestral genomes
Laura F. Campitelli
Isaac Yellan
Mihai Tudor Albu
Marjan Barazandeh
Zain M. Patel
Mathieu Blanchette
T. Hughes
Abstract Sequences derived from the Long INterspersed Element-1 (L1) family of retrotransposons occupy at least 17% of the human genome, wit… (see more)h 67 distinct subfamilies representing successive waves of expansion and extinction in mammalian lineages. L1s contribute extensively to gene regulation, but their molecular history is difficult to trace, because most are present only as truncated and highly mutated fossils. Consequently, L1 entries in current databases of repeat sequences are composed mainly of short diagnostic subsequences, rather than full functional progenitor sequences for each subfamily. Here, we have coupled 2 levels of sequence reconstruction (at the level of whole genomes and L1 subfamilies) to reconstruct progenitor sequences for all human L1 subfamilies that are more functionally and phylogenetically plausible than existing models. Most of the reconstructed sequences are at or near the canonical length of L1s and encode uninterrupted ORFs with expected protein domains. We also show that the presence or absence of binding sites for KRAB-C2H2 Zinc Finger Proteins, even in ancient-reconstructed progenitor L1s, mirrors binding observed in human ChIP-exo experiments, thus extending the arms race and domestication model. RepeatMasker searches of the modern human genome suggest that the new models may be able to assign subfamily resolution identities to previously ambiguous L1 instances. The reconstructed L1 sequences will be useful for genome annotation and functional study of both L1 evolution and L1 contributions to host regulatory networks.
2022-05-12
Genetics (published)
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
2022-05-12
Genetics (published)
doi.org
Phylogenetic Manifold Regularization: A semi-supervised approach to predict transcription factor binding sites
Faizy Ahsan
Alexandre Drouin
Franccois Laviolette
Doina Precup
Mathieu Blanchette
The computational prediction of transcription factor binding sites remains a challenging problems in bioinformatics, despite significant met… (see more)hodological developments from the 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 of the a pproach. G ains in prediction accuracy are obtained over a broad set of transcription factors and cell types.
2020-12-16
2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (published)
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 … (see 21 more)
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
2012-10-01
Lecture Notes in Computer Science (published)
doi.org