Abstract

Understanding the molecular basis of the adaptive evolution of a population has relevance for important biological questions. For example, the problem of identifying genetic variants which underlie drug resistance, a question of importance for the treatment of pathogens, and of cancer, 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 genetic drift. Fortunately, technological advances driven by next generation 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 our ongoing work on how to use time-resolved sequence data to draw inferences about the evolutionary dynamics of a population under study. Firstly, we 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 how populations respond to such external stresses. This work is a collaboration project with Gianni Liti Lab (Institute of Research on Cancer and Ageing of Nice). Secondly, we report cloneHD, a probabilistic algorithm to perform subclone reconstruction from data generated by high-throughput DNA sequencing and use it to analyse time-resolved samples of chronic lymphocytic leukaemia.