Archive Page: Mustonen Group | Population genomics of adaptation

Archive Page: Mustonen Group | Population genomics of adaptation

Archive Page: Mustonen Group

The Mustonen Group left the Sanger Institute in September 2016. This page is being maintained as a historical record of the research the group conducted at the Sanger Institute.
screenhd3.png(from Fischer et al. Cell Reports)
Reconstruction of clonal heterogeneity

Our Research and Approach

High-throughput sequencing has opened up a new chapter in the study of molecular evolution and genetics by allowing deep sequencing of whole populations of organisms and cells.

We are in a unique position to study in detail how genetic composition of populations change as they respond to external pressures such as drug therapies. We can ask: What is the role of genetics in a person's susceptibility to develop a cancer, or another potentially fatal disease? Are the observed differences between individuals mostly a result of neutral evolution or do they bear a fitness advantage? These questions are not only interesting for understanding evolution but can also make a fundamental contribution to biomedical applications. The promise of personalised medicine will critically depend on finding and understanding molecular disease phenotypes and on developing algorithms to help bring actionable insights to clinics.

However, data alone will not solve the problem of resistance. The development of cancer or the spread of infection within a host's cell population are dynamic processes. Similarly, therapeutic interventions against them will cause time-dependent responses.

Therefore, new evolutionary-theory based computational methods and ideas are needed to analyse these data. These methods can help to characterise the emergence of drug resistance in model systems and to design experiments that ultimately lead to novel approaches in combating resistance.

Our group contributed to this effort by developing scalable methods for biomedical applications of data. We further used these data to address basic biological research questions such as how drug resistance arises.

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People

Mustonen, Ville
Dr Ville Mustonen
Group Leader

Ville developed computational methods to discover and understand functionally relevant genetic and phenotypic variation.

Publications

  • The value of monitoring to control evolving populations.

    Fischer A, Vázquez-García I and Mustonen V

    Proceedings of the National Academy of Sciences of the United States of America 2015;112;4;1007-12

  • Pathway and network analysis of cancer genomes.

    Mutation Consequences and Pathway Analysis working group of the International Cancer Genome Consortium

    Nature methods 2015;12;7;615-21

  • Identifying selection in the within-host evolution of influenza using viral sequence data.

    Illingworth CJ, Fischer A and Mustonen V

    PLoS computational biology 2014;10;7;e1003755

  • High-definition reconstruction of clonal composition in cancer.

    Fischer A, Vázquez-García I, Illingworth CJ and Mustonen V

    Cell reports 2014;7;5;1740-52

  • Computational approaches to identify functional genetic variants in cancer genomes.

    Gonzalez-Perez A, Mustonen V, Reva B, Ritchie GR, Creixell P et al.

    Nature methods 2013;10;8;723-9

  • EMu: probabilistic inference of mutational processes and their localization in the cancer genome.

    Fischer A, Illingworth CJ, Campbell PJ and Mustonen V

    Genome biology 2013;14;4;R39

  • Components of selection in the evolution of the influenza virus: linkage effects beat inherent selection.

    Illingworth CJ and Mustonen V

    PLoS pathogens 2012;8;12;e1003091

  • Quantifying selection acting on a complex trait using allele frequency time series data.

    Illingworth CJ, Parts L, Schiffels S, Liti G and Mustonen V

    Molecular biology and evolution 2012;29;4;1187-97

  • Pathway and network analysis of cancer genomes.

    Mutation Consequences and Pathway Analysis working group of the International Cancer Genome Consortium

    Nature methods 2015;12;7;615-21

  • The value of monitoring to control evolving populations.

    Fischer A, Vázquez-García I and Mustonen V

    Proceedings of the National Academy of Sciences of the United States of America 2015;112;4;1007-12

  • Identifying selection in the within-host evolution of influenza using viral sequence data.

    Illingworth CJ, Fischer A and Mustonen V

    PLoS computational biology 2014;10;7;e1003755

  • High-definition reconstruction of clonal composition in cancer.

    Fischer A, Vázquez-García I, Illingworth CJ and Mustonen V

    Cell reports 2014;7;5;1740-52

  • High-resolution mapping of complex traits with a four-parent advanced intercross yeast population.

    Cubillos FA, Parts L, Salinas F, Bergström A, Scovacricchi E et al.

    Genetics 2013;195;3;1141-55

  • Computational approaches to identify functional genetic variants in cancer genomes.

    Gonzalez-Perez A, Mustonen V, Reva B, Ritchie GR, Creixell P et al.

    Nature methods 2013;10;8;723-9

  • EMu: probabilistic inference of mutational processes and their localization in the cancer genome.

    Fischer A, Illingworth CJ, Campbell PJ and Mustonen V

    Genome biology 2013;14;4;R39

  • Components of selection in the evolution of the influenza virus: linkage effects beat inherent selection.

    Illingworth CJ and Mustonen V

    PLoS pathogens 2012;8;12;e1003091

  • From fitness landscapes to seascapes: non-equilibrium dynamics of selection and adaptation.

    Mustonen V and Lässig M

    Trends in genetics : TIG 2009;25;3;111-9

  • Energy-dependent fitness: a quantitative model for the evolution of yeast transcription factor binding sites.

    Mustonen V, Kinney J, Callan CG and Lässig M

    Proceedings of the National Academy of Sciences of the United States of America 2008;105;34;12376-81

  • Molecular evolution under fitness fluctuations.

    Mustonen V and Lässig M

    Physical review letters 2008;100;10;108101

  • Adaptations to fluctuating selection in Drosophila.

    Mustonen V and Lässig M

    Proceedings of the National Academy of Sciences of the United States of America 2007;104;7;2277-82

  • Evolutionary population genetics of promoters: predicting binding sites and functional phylogenies.

    Mustonen V and Lässig M

    Proceedings of the National Academy of Sciences of the United States of America 2005;102;44;15936-41