Below you will find scientific publications authored by our members or those enabled by our platform services.
2018
Caron, Maxime; St-Onge, Pascal; Drouin, Simon; Richer, Chantal; Sontag, Thomas; Busche, Stephan; Bourque, Guillaume; Pastinen, Tomi; Sinnett, Daniel
In: PLOS ONE, vol. 13, no. 11, pp. e0207250, 2018, ISSN: 1932-6203, (Publisher: Public Library of Science).
Abstract | Links | BibTeX | Tags: Blood, Cancers and neoplasms, Chromatin, DNA methylation, epigenetics, Histones, Human genomics, RNA sequencing
@article{caron_very_2018,
title = {Very long intergenic non-coding RNA transcripts and expression profiles are associated to specific childhood acute lymphoblastic leukemia subtypes},
author = {Maxime Caron and Pascal St-Onge and Simon Drouin and Chantal Richer and Thomas Sontag and Stephan Busche and Guillaume Bourque and Tomi Pastinen and Daniel Sinnett},
url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0207250},
doi = {10.1371/journal.pone.0207250},
issn = {1932-6203},
year = {2018},
date = {2018-01-01},
urldate = {2021-05-19},
journal = {PLOS ONE},
volume = {13},
number = {11},
pages = {e0207250},
abstract = {Very long intergenic non-coding RNAs (vlincRNAs) are a novel class of long transcripts (textasciitilde50 kb to 1 Mb) with cell type- or cancer-specific expression. We report the discovery and characterization of 256 vlincRNAs from a cohort of 64 primary childhood pre-B and pre-T acute lymphoblastic leukemia (cALL) samples, of which 61% are novel and specifically expressed in cALL. Validation was performed in 35 pre-B and pre-T cALL primary samples. We show that their expression is cALL immunophenotype and molecular subtype-specific and correlated with epigenetic modifications on their promoters, much like protein-coding genes. While the biological functions of these vlincRNAs are still unknown, our results suggest they could play a role in cALL etiology or progression.},
note = {Publisher: Public Library of Science},
keywords = {Blood, Cancers and neoplasms, Chromatin, DNA methylation, epigenetics, Histones, Human genomics, RNA sequencing},
pubstate = {published},
tppubtype = {article}
}
Very long intergenic non-coding RNAs (vlincRNAs) are a novel class of long transcripts (textasciitilde50 kb to 1 Mb) with cell type- or cancer-specific expression. We report the discovery and characterization of 256 vlincRNAs from a cohort of 64 primary childhood pre-B and pre-T acute lymphoblastic leukemia (cALL) samples, of which 61% are novel and specifically expressed in cALL. Validation was performed in 35 pre-B and pre-T cALL primary samples. We show that their expression is cALL immunophenotype and molecular subtype-specific and correlated with epigenetic modifications on their promoters, much like protein-coding genes. While the biological functions of these vlincRNAs are still unknown, our results suggest they could play a role in cALL etiology or progression.
2017
Mezlini, Aziz M; Goldenberg, Anna
Incorporating networks in a probabilistic graphical model to find drivers for complex human diseases Journal Article
In: PLOS Computational Biology, vol. 13, no. 10, pp. e1005580, 2017, ISSN: 1553-7358, (Publisher: Public Library of Science).
Abstract | Links | BibTeX | Tags: Cancers and neoplasms, Gene prediction, Genetic networks, Mutation, Ovarian cancer, Protein interaction networks, Protein-protein interactions, Schizophrenia
@article{mezlini_incorporating_2017,
title = {Incorporating networks in a probabilistic graphical model to find drivers for complex human diseases},
author = {Aziz M Mezlini and Anna Goldenberg},
url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005580},
doi = {10.1371/journal.pcbi.1005580},
issn = {1553-7358},
year = {2017},
date = {2017-01-01},
urldate = {2021-05-19},
journal = {PLOS Computational Biology},
volume = {13},
number = {10},
pages = {e1005580},
abstract = {Discovering genetic mechanisms driving complex diseases is a hard problem. Existing methods often lack power to identify the set of responsible genes. Protein-protein interaction networks have been shown to boost power when detecting gene-disease associations. We introduce a Bayesian framework, Conflux, to find disease associated genes from exome sequencing data using networks as a prior. There are two main advantages to using networks within a probabilistic graphical model. First, networks are noisy and incomplete, a substantial impediment to gene discovery. Incorporating networks into the structure of a probabilistic models for gene inference has less impact on the solution than relying on the noisy network structure directly. Second, using a Bayesian framework we can keep track of the uncertainty of each gene being associated with the phenotype rather than returning a fixed list of genes. We first show that using networks clearly improves gene detection compared to individual gene testing. We then show consistently improved performance of Conflux compared to the state-of-the-art diffusion network-based method Hotnet2 and a variety of other network and variant aggregation methods, using randomly generated and literature-reported gene sets. We test Hotnet2 and Conflux on several network configurations to reveal biases and patterns of false positives and false negatives in each case. Our experiments show that our novel Bayesian framework Conflux incorporates many of the advantages of the current state-of-the-art methods, while offering more flexibility and improved power in many gene-disease association scenarios.},
note = {Publisher: Public Library of Science},
keywords = {Cancers and neoplasms, Gene prediction, Genetic networks, Mutation, Ovarian cancer, Protein interaction networks, Protein-protein interactions, Schizophrenia},
pubstate = {published},
tppubtype = {article}
}
Discovering genetic mechanisms driving complex diseases is a hard problem. Existing methods often lack power to identify the set of responsible genes. Protein-protein interaction networks have been shown to boost power when detecting gene-disease associations. We introduce a Bayesian framework, Conflux, to find disease associated genes from exome sequencing data using networks as a prior. There are two main advantages to using networks within a probabilistic graphical model. First, networks are noisy and incomplete, a substantial impediment to gene discovery. Incorporating networks into the structure of a probabilistic models for gene inference has less impact on the solution than relying on the noisy network structure directly. Second, using a Bayesian framework we can keep track of the uncertainty of each gene being associated with the phenotype rather than returning a fixed list of genes. We first show that using networks clearly improves gene detection compared to individual gene testing. We then show consistently improved performance of Conflux compared to the state-of-the-art diffusion network-based method Hotnet2 and a variety of other network and variant aggregation methods, using randomly generated and literature-reported gene sets. We test Hotnet2 and Conflux on several network configurations to reveal biases and patterns of false positives and false negatives in each case. Our experiments show that our novel Bayesian framework Conflux incorporates many of the advantages of the current state-of-the-art methods, while offering more flexibility and improved power in many gene-disease association scenarios.