Job Listings

Project Title: The H5Nx Genomic Landscape: Predictive Modelling of Host and Antigenic Transitions A funded Postdoctoral Fellow opportunity is available in under the co-supervision of Dr. Zvonimir Poljak (University of Guelph) and Dr. Finlay Maguire (Dalhousie University). This is a full-time one-year position, with the possibility of extension, at the University of Guelph. This position is supported by the Canadian Institute of Health Research/Public Health Agency of Canada’s Avian Influenza One Health Research Funding Opportunity with contributions from NSERC.  

The graduate project focuses on the role of intrinsically disordered regions (IDRs) in plant-pathogen interactions. IDRs are protein regions that lack a stable 3D structure. IDRs are known to facilitate protein localization and protein-protein interactions, and proteins with IDRs often serve as functional hubs in regulatory and signaling networks. IDRs are also found in many pathogen secreted effectors proteins and it is believed that IDR domains are critical for the localization and function of these virulence factors. We are looking for a student to systematically identify IDRs in pathogen effectors and the proteome of the plant host. We will then use machine learning to predict the co-localization of these protein in the plant host cell. Ultimately, we will test these predictions in vivo to determine the role of IDRs in disease (this component of the project is outside the scope of the bioinformatics project, but will be available to the student if interested). The student will join the laboratory of David Guttman (Guttman Lab, PubMed, ‪Google Scholar‬) and work in collaboration with the Desveaux lab (Desveaux Lab). This study builds on an exciting and innovative project that has already resulted in numerous high-impact publications, including: The pan-genome effector-triggered immunity landscape of a host-pathogen interaction | Science Cooperative virulence via the collective action of secreted pathogen effectors | Nature Microbiology The effector-triggered immunity landscape of tomato against Pseudomonas syringae | Nature Communications Metaeffector interactions modulate the type III effector-triggered immunity load of Pseudomonas syringae | PLOS Pathogens The Arabidopsis effector-triggered immunity landscape is conserved in oilseed crops | Scientific Reports

Project Background. We are seeking a motivated PhD student to join our research team in developing advanced methods for phylogenetic network analysis, with a specific focus on network consensus algorithms. Significant progress has been made in consensus tree methodologies. A consensus tree is a phylogenetic tree that synthesizes multiple phylogenetic trees, each with the same leaf labels but possibly differing topologies. These trees are often generated through bootstrapping or other sampling techniques. Traditional approaches to consensus tree construction focus primarily on topological aspects, often overlooking the importance of branch length, which captures the temporal progression of genetic mutations. However, in the context of consensus networks, very few studies have introduced relevant concepts. Project Objective. Our project addresses this limitation by integrating branch-length data not only into the construction of consensus trees but also into network consensus construction. This more comprehensive approach aims to provide a richer and more accurate representation of evolutionary relationships by combining topological structure, branch frequency, clade frequency, and branch length. The candidate must hold a Master’s degree in Mathematics, Computer Science, or Bioinformatics with a strong overall GPA. Compensation: non-taxable scholarship of +20,000 CAD per year. To apply, please send your CV, list of peer-reviewed articles (optional), and a cover letter to: Prof. Nadia Tahiri

Étudiant de doctorat en Informatique ou en Bioinformatique à l’Université du Québec à Montréal (Montréal, Canada) Contexte du projet La comparaison de séquences génétiques et génomiques est essentielle pour comprendre la diversité du vivant. Les arbres et réseaux phylogénétiques ont grandement contribué à notre compréhension de l’histoire du vivant. Cependant, l’augmentation exponentielle des séquences génétiques disponibles nécessite de nouvelles méthodes pour explorer la diversité des données évolutives. Le projet est mené par une équipe multidisciplinaire de l’Université du Québec à Montréal (UQAM) (Prof. Vladimir Makarenkov), de l’Université de Sherbrooke (Prof. Guillaume Blanchet et Prof. Nadia Tahiri) et de l’Université de Montréal (Prof. Pierre Legendre). Objectif du projet Le projet utilisera des réseaux de similarité (ou graphe de similarité) où chaque nœud représente une séquence génétique. Les nœuds de ces réseaux sont connectés par des arêtes si leurs séquences montrent une similarité significative. Ce type de structures en réseau doit permettre une analyse plus souple et complète des données génomiques et métagénomiques, surpassant les limitations des méthodes actuelles basées sur les arbres et réseaux phylogénétiques. L’objectif principal de notre projet est de développer des méthodes informatiques et mathématiques pour analyser de grands jeux de données biologiques et bioinformatiques via des réseaux de similarité. Le candidat doit posséder un diplôme de maîtrise en Bioinformatique, en Informatique ou en Mathématiques avec une bonne moyenne générale. Rémunération: bourse non imposable de 29 000 CAD par année.