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© Research
Event

C3BI Seminar – Learning of Ultra High-Dimensional Potts Models for Bacterial Population Genomics

Scientific Fields
Diseases
Organisms
Applications
Technique
Date
22
Mar 2018
Time
14:00:00
Institut Pasteur, Paris, France
Address
Building: Batiment Lwoff
Location
2018-03-22 14:00:00 2018-03-22 14:00:00 Europe/Paris C3BI Seminar – Learning of Ultra High-Dimensional Potts Models for Bacterial Population Genomics LEARNING OF ULTRA HIGH-DIMENSIONAL POTTS MODELS FOR BACTERIAL POPULATION GENOMICS  Main speaker : Jukka Corander, from Professor at University of Helsinki and at University of Oslo Bayesian Statistics Group Date : 22-03-2018 at 02:00 pm Location : […] Institut Pasteur, Paris, France

About

LEARNING OF ULTRA HIGH-DIMENSIONAL POTTS MODELS FOR BACTERIAL POPULATION GENOMICS


 Main speaker : Jukka Corander, from Professor at University of Helsinki and at University of Oslo Bayesian Statistics Group Date : 22-03-2018 at 02:00 pm Location : Retrovirus room – LWOFF (22) ,Institut Pasteur, Paris


The potential for genome-wide modeling of epistasis has recently surfaced given the possibility of sequencing densely sampled populations and the emerging families of statistical interaction models. Direct coupling analysis (DCA) has earlier been shown to yield valuable predictions for single protein structures, and has recently been extended to genome-wide analysis of bacteria, identifying novel interactions in the co-evolution between resistance, virulence and core genome elements. However, earlier computational DCA methods have not been scalable to enable model fitting simultaneously to 10000-100000 polymorphisms, representing the amount of core genomic variation observed in analyses of many bacterial species. Here we introduce a novel inference method (SuperDCA) which employs a new scoring principle, efficient parallelization, optimization and filtering on phylogenetic information to achieve scalability for up to 100000 polymorphisms. Using two large population samples of Streptococcus pneumoniae, we demonstrate the ability of SuperDCA to make additional significant biological findings about this major human pathogen. We also show that our method can uncover signals of selection that are not detectable by genome-wide association analysis, even though our analysis does not require phenotypic measurements. SuperDCA thus holds considerable potential in building understanding about numerous organisms at a systems biological level.

Location

Building: Batiment Lwoff
Address: Institut Pasteur, Paris, France