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SUMMARY:Genome-wide epistasis in bacteria\, new statistical tools and fres
 h biological insight - Prof Jukka Corander\, University of Oslo
DTSTART:20171003T150000Z
DTEND:20171003T160000Z
UID:TALK76752@talks.cam.ac.uk
CONTACT:Alison Quenault
DESCRIPTION:The potential for genome-wide modeling of epistasis has recent
 ly surfaced given the possibility of sequencing densely sampled population
 s and the emerging families of statistical interaction models. Direct coup
 ling analysis (DCA) has earlier been shown to yield valuable predictions f
 or single protein structures\, and has recently been extended to genome-wi
 de analysis of bacteria\, identifying novel interactions in the co-evoluti
 on between resistance\, virulence and core genome elements. However\, earl
 ier computational DCA methods have not been scalable to enable model fitti
 ng simultaneously to 104-105 polymorphisms\, representing the amount of co
 re genomic variation observed in analyses of many bacterial species. Here 
 we introduce a novel inference method (SuperDCA) which employs a new scori
 ng principle\, efficient parallelization\, optimization and filtering on p
 hylogenetic information to achieve scalability for up to 105 polymorphisms
 . Using two large population samples of Streptococcus pneumoniae\, we demo
 nstrate 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 assoc
 iation analysis\, even though our analysis does not require phenotypic mea
 surements. SuperDCA thus holds considerable potential in building understa
 nding about numerous organisms at a systems biological level.
LOCATION:Large  Seminar Room\, 1st Floor\, Institute of Public Health\, Un
 iversity Forvie Site\, Robinson Way\, Cambridge
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