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SUMMARY:Using time-resolved genetic data to study the evolution of drug re
 sistance. - Ville Mustonen (Sanger Institute)
DTSTART:20140203T160000Z
DTEND:20140203T170000Z
UID:TALK47989@talks.cam.ac.uk
CONTACT:Florian Markowetz
DESCRIPTION:Understanding the molecular basis of the adaptive evolution of
  a population has relevance for important biological questions. For exampl
 e\, the problem of identifying genetic variants which underlie drug resist
 ance\, a question of importance for the treatment of pathogens\, and of ca
 ncer\, can be understood as a matter of inferring selection. A key problem
  in discovering variants under positive selection is the complexity of the
  underlying evolutionary dynamics\, which may involve an interplay between
  several contributing processes\, including mutation\, recombination and g
 enetic drift.  Fortunately\, technological advances driven by next generat
 ion sequencing are making it possible to systematically follow across time
  how the genomic composition of a population evolves. However\, such data 
 needs new quantitative methods to fulfil its potential. We here present ou
 r ongoing work on how to use time-resolved sequence data to draw inference
 s about the evolutionary dynamics of a population under study. Firstly\, w
 e describe an analysis of a laboratory evolution experiment where a yeast 
 cross was exposed to a number of cancer drugs to study the genetic basis h
 ow populations respond to such external stresses. This work is a collabora
 tion project with Gianni Liti Lab (Institute of Research on Cancer and Age
 ing of Nice). Secondly\, we report cloneHD\, a probabilistic  algorithm to
  perform subclone reconstruction from data generated by high-throughput DN
 A sequencing and use it  to analyse time-resolved samples of chronic lymph
 ocytic leukaemia. \n
LOCATION:Cancer Research UK Cambridge Institute\, Lecture Theatre
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