Reference number : GC-2017-Q1-01652, amendment number : , agreement type : G, recipient legal name : Chauve, Cedric (Simon Fraser University), recipient country : CA, recipient province : nan, recipient city : British Columbia|Colombie-Britannique, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : Genome rearrangements are rare evolutionary events that alter the order of genes along chromosomes. They can be related to the development of virulence in pathogens, the emergence of new species or the adaptation to new environmental conditions or ecological niches. Over the last 10 years, my collaborators and I, as well as other groups, have made significan t advances in the development of already widely applicable methods for inferring gene order evolution scenarios and ancestral gene orders. 
 
The primary goal of my proposed research program is to expand the existing corpus of methods to reconstruct ancestral gene orders to address some of the remaining challenges. I will develop a comprehensive suite of mathematical models, computational algorithms, software, and data analysis procedures and pipelines, providing to life scientists efficient and relevant tools to investigate the evolution of the structural genome (gene content, gene order) of related groups of species. To be relevant to large­-scale comparative genomics projects, these tools will be developed within a general evolutionary model, that includes gene duplication and loss, horizontal gene transfer and introgression, and genome rearrangements. Moreover, to handle data sets composed of genomes available in various levels of completion in terms of assemblies, I will develop methods that use sequencing data of various kinds and reconstruct jointly, in an integrative framework, gene family evolution, ancestral gene order and improved extant and ancient genome scaffolds. 
 
While these methods will be applicable to many large­-scale data sets, I will study in depth a data set of eighteen genomes of Anopheles mosquitoes, the vector of the malaria parasite. In particular, I will complement the generic approaches described above in order to handle some very specific issues related to Anopheles (and insects in general) evolution, such as polymorphic inversions and introgression. This will result in improved data sets of genome assemblies, gene trees and ancestral gene orders of Anopheles genomes that will be valuable resources for the mosquito biology community. 
 
The second field of application that will be investigated is the pathogen Yersinia pestis , the agent of the bubonic plague. Yersinia pestis is an interesting species for studying genome rearrangement in pathogens, and is of particular interest due to the recent sequencing of several ancient strains involved in historical pandemics (the Justinian Plague and the Black Death). My goal is to take advantage of this unique data set of ancient and extant sequenced genomes to develop novel methods integrating aDNA in pathogen comparative genomics, with an application to refine our understanding of the evolution of a highly dynamic human pathogen at a historical time­-scale., program purpose french : Genome rearrangements are rare evolutionary events that alter the order of genes along chromosomes. They can be related to the development of virulence in pathogens, the emergence of new species or the adaptation to new environmental conditions or ecological niches. Over the last 10 years, my collaborators and I, as well as other groups, have made significan t advances in the development of already widely applicable methods for inferring gene order evolution scenarios and ancestral gene orders. 
 
The primary goal of my proposed research program is to expand the existing corpus of methods to reconstruct ancestral gene orders to address some of the remaining challenges. I will develop a comprehensive suite of mathematical models, computational algorithms, software, and data analysis procedures and pipelines, providing to life scientists efficient and relevant tools to investigate the evolution of the structural genome (gene content, gene order) of related groups of species. To be relevant to large­-scale comparative genomics projects, these tools will be developed within a general evolutionary model, that includes gene duplication and loss, horizontal gene transfer and introgression, and genome rearrangements. Moreover, to handle data sets composed of genomes available in various levels of completion in terms of assemblies, I will develop methods that use sequencing data of various kinds and reconstruct jointly, in an integrative framework, gene family evolution, ancestral gene order and improved extant and ancient genome scaffolds. 
 
While these methods will be applicable to many large­-scale data sets, I will study in depth a data set of eighteen genomes of Anopheles mosquitoes, the vector of the malaria parasite. In particular, I will complement the generic approaches described above in order to handle some very specific issues related to Anopheles (and insects in general) evolution, such as polymorphic inversions and introgression. This will result in improved data sets of genome assemblies, gene trees and ancestral gene orders of Anopheles genomes that will be valuable resources for the mosquito biology community. 
 
The second field of application that will be investigated is the pathogen Yersinia pestis , the agent of the bubonic plague. Yersinia pestis is an interesting species for studying genome rearrangement in pathogens, and is of particular interest due to the recent sequencing of several ancient strains involved in historical pandemics (the Justinian Plague and the Black Death). My goal is to take advantage of this unique data set of ancient and extant sequenced genomes to develop novel methods integrating aDNA in pathogen comparative genomics, with an application to refine our understanding of the evolution of a highly dynamic human pathogen at a historical time­-scale., agreement title english : Computational models, algorithms and methods for comparative genomics, applied to pathogens and anopheles mosquitoes genomes, agreement title french : Computational models, algorithms and methods for comparative genomics, applied to pathogens and anopheles mosquitoes genomes, agreement number : RGPIN, agreement value : 130000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada