Mathieu Blanchette

Lucas Nelson

Maîtrise recherche - McGill

Adam Stecklov

Maîtrise recherche - McGill

César Miguel Valdez Cordova

Doctorat - McGill

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Yijie Zhang

Doctorat - McGill

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Nicole Zhang

Doctorat - McGill

Co-superviseur⋅e :

Yoshua Bengio

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Publications

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 … (voir 12 de plus)

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

2023-11-28

bioRxiv (prépublication)

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

2023-11-05

International Conference on Climate Informatics (publié)

PhyloGFN: Phylogenetic inference with generative flow networks

Ming Yang Zhou

Zichao Yan

Elliot Layne

Nikolay Malkin

Dinghuai Zhang

Moksh J. Jain

Yoshua Bengio

Phylogenetics is a branch of computational biology that studies the evolutionary relationships among biological entities. Its long history a… (voir plus)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 (prépublication)

arxiv.org

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… (voir plus) 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 (publié)

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

2023-04-19

International Conference on Human Factors in Computing Systems (publié)

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

2022-12-12

BMC Cancer (publié)

Reference panel guided topological structure annotation of Hi-C data

Yanlin Zhang

2022-12-01

Nature Communications (publié)

Leveraging Structure Between Environments: Phylogenetic Regularization Incentivizes Disentangled Representations

Elliot Layne

Jason Hartford

Sébastien Lachapelle

Dhanya Sridhar

Recently, learning invariant predictors across varying environments has been shown to improve the generalization of supervised learning meth… (voir plus)ods. This line of investigation holds great potential for application to biological problem settings, where data is often naturally heterogeneous. Biological samples often originate from different distributions, or environments. However, in biological contexts, the standard "invariant prediction" setting may not completely fit: the optimal predictor may in fact vary across biological environments. There also exists strong domain knowledge about the relationships between environments, such as the evolutionary history of a set of species, or the differentiation process of cell types. Most work on generic invariant predictors have not assumed the existence of structured relationships between environments. However, this prior knowledge about environments themselves has already been shown to improve prediction through a particular form of regularization applied when learning a set of predictors. In this work, we empirically evaluate whether a regularization strategy that exploits environment-based prior information can be used to learn representations that better disentangle causal factors that generate observed data. We find evidence that these methods do in fact improve the disentanglement of latent embeddings. We also show a setting where these methods can leverage phylogenetic information to estimate the number of latent causal features.

2022-07-09

auai.org/UAI/2022/Workshop/CRL (poster)

openreview.net

Leishmania parasites exchange drug-resistance genes through extracellular vesicles

Noélie Douanne

George Dong

Atia Amin

Lorena Bernardo

David Langlais

Martin Olivier

Christopher Fernandez-Prada

2022-07-01

Cell Reports (publié)

PhyloPGM: boosting regulatory function prediction accuracy using evolutionary information

Faizy Ahsan

Zichao Yan

Doina Precup

Abstract Motivation The computational prediction of regulatory function associated with a genomic sequence is of utter importance in -omics … (voir plus)study, which facilitates our understanding of the underlying mechanisms underpinning the vast gene regulatory network. Prominent examples in this area include the binding prediction of transcription factors in DNA regulatory regions, and predicting RNA–protein interaction in the context of post-transcriptional gene expression. However, existing computational methods have suffered from high false-positive rates and have seldom used any evolutionary information, despite the vast amount of available orthologous data across multitudes of extant and ancestral genomes, which readily present an opportunity to improve the accuracy of existing computational methods. Results In this study, we present a novel probabilistic approach called PhyloPGM that leverages previously trained TFBS or RNA–RBP binding predictors by aggregating their predictions from various orthologous regions, in order to boost the overall prediction accuracy on human sequences. Throughout our experiments, PhyloPGM has shown significant improvement over baselines such as the sequence-based RNA–RBP binding predictor RNATracker and the sequence-based TFBS predictor that is known as FactorNet. PhyloPGM is simple in principle, easy to implement and yet, yields impressive results. Availability and implementation The PhyloPGM package is available at https://github.com/BlanchetteLab/PhyloPGM Supplementary information Supplementary data are available at Bioinformatics online.

2022-06-27

Bioinformatics (publié)

Reconstruction of full-length LINE-1 progenitors from ancestral genomes

Laura F. Campitelli

Isaac Yellan

Mihai Tudor Albu

Marjan Barazandeh

Zain M. Patel

T. Hughes

Abstract Sequences derived from the Long INterspersed Element-1 (L1) family of retrotransposons occupy at least 17% of the human genome, wit… (voir plus)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)

Reconstruction of full-length LINE-1 progenitors from ancestral genomes

Laura F Campitelli

Isaac Yellan

Mihai Albu

Marjan Barazandeh

Zain M Patel

Timothy R Hughes

2022-05-12

Genetics (publié)