| Telephone: +44(0)141-330-6458
AddressRoom 801 - Rankine Building
School of Engineering
The computational microbial genomics group at the University of Glasgow was created in December 2009 as the result of an award of an EPSRC Career Acceleration Fellowship worth over £800K to Dr Christopher Quince: "Pioneering the New Genomics Era in Environmental Microbiology for Engineering Design". The aim of the group is to build on the prior success of Dr Quince's research, and develop novel algorithms for the analysis of next-generation sequencing data that are capable of accurately determining microbial community structure. This data will then be used in both statistical models, that allow diversity patterns to be correlated with environmental parameters, and mathematical models that can predict community dynamics. There is an emphasis within the group on communities associated with engineering systems, and there are ongoing projects on the genomics of slow sand filters and microbial fuel cells, but the algorithms are universal, and are being applied through international collaborations, to a whole range of environments and organisms, from marine metazoans to HIV. Further funding has been secured to pursue this research, principally a Unilever direct research grant, a competitively awarded University studentship, and we are responsible for the bioinformatics component of a £2.9M TSB funded collaboration with Unilever, Liverpool CGR and two SMEs, "Development of instrumental and bioinformatic pipelines to accelerate commercial applications of metagenomics approaches". In total, the group now consists of the PI, three post-doctoral researchers and six graduate students and encompasses researchers from a wide range of backgrounds. Many of these graduate students are jointly supervised by Prof. William Sloan.
2011: Permanent appointment as Lecturer in Biological Systems Modelling; School of Engineering; University of Glasgow
2010-2015: EPSRC Career Acceleration Fellowship "Pioneering the New Genomics Era in Environmental Microbiology for Engineering Design"; University of Glasgow
2007-2010: Lord Kelvin research fellowship "Mathematical Modelling of Microbial Communities"; University of Glasgow
2005-2007: Postdoctoral Research Associate "Using life history theory to improve management strategies for recreational fisheries on inland lakes"; Postdoctoral advisors Profs Peter Abrams and Brian Shuter; University of Toronto
2003-2005: Postdoctoral Research Associate "Applications of Statistical Physics to Population Dynamics"; Postdoctoral advisor Dr Tim Newman; Arizona State University
2000-2003: PhD thesis "Modelling Coevolution in Communities of Interacting Species"; PhD supervisors Profs Paul G. Higgs and Alan J. McKane; University of Manchester
A current focus of my research is how to accurately determine microbial diversity in a community. Microbial diversity in an environment can only be directly measured by sequencing homologous genes, typically the 16S rRNA. A recently developed tool that allows large numbers of reads to be obtained cheaply is 454 pyrosequencing. However, because 454 generates such large sample sizes and resequencing to increase accuracy is not possible it is vitally important to distinguish noise from true diversity. To address this in 2009 I developed the PyroNoise algorithm for removing noise from 454 amplicons (Quince et al. 2009). The software and data sets from this paper can be downloaded here. More recently I have revised and perfected this algorithm to produce the two stage AmpliconNoise as described in Quince et al. 2011. The data from this paper is available here.
Even with the large data sets generated by 454 it will only be possible to sample a fraction of the individuals in a microbial community. I developed a set of Bayesian tools for fitting an abundance distribution to the observed taxa and their abundances (Quince et al. 2008). From this it is possible to infer both the true number of taxa in the community and the sampling effort necessary to obtain any given level of diversity. The Bayesian diversity estimation software can be downloaded here. We have applied this strategy in collaboration with Pete Turnbaugh to estimate the total diversity of the human gut (Turnbaugh et al. 2010).
In addition to using Bayesian models to estimate the total diversity of a community, we can use them to describe differences between communities. In collaboration with my PDRA Keith Harris and Ian Holmes (Berkeley) , we have developed a flexible modelling framework for microbial community data, Dirichlet-multinomial mixtures Holmes et al. 2012 . We demonstrate how these models can be used for clustering and classification of microbial communities. In particular, it allow model-based clustering for the topical question of determining enterotypes in the human gut. We actually found four rather than three enterotypes fit the data best but would rather emphasise not the number of clusters but that any given clustering is subject to uncertainty that can be naturally represented using Bayesian probability.
The theory of birth-and-death processes forms a natural framework for describing processes that operate on the level of individuals in Ecology and Population Genetics. In collaboration with Todd Parsons from the Mathematics department of the University of Toronto, I have been studying an application of this framework to f ixation in haploid populations experiencing density dependent population dynamics (Parsons and Quince 2007a and Parsons and Quince 2007b). We approximated the discrete stochastic system with a continuous Fokker-Planck equation and then derived asymptotic analytic solutions to this equation. We then compared these approximate solutions with exact numerical solutions of the original discrete system. This process yielded valuable insights into the fixation of alleles in populations changing deterministically in size and fluctuating about equilibriums due to demographic stochasticity. We then calculated fixation times for this model (Parsons et al. 2008) and summarised its relevance to population genetics (Parsons et al. 2010).
There is a direct mathematical connection between the dynamics of alleles in haploid populations and the clonal population dynamics of microbial organisms. I aim to exploit this connection in my current position using techniques from Population Genetics to develop a mathematical and computational framework for the description of microbial communities.
The other major focus of my research is life history strategies and in particular models of optimal resource allocation between growth and reproduction in seasonally reproducing fish. For this research I focused on lake trout (Salvelinus namaycush), a species which shows substantial life history variation and for which we have good quality growth and maturation data. I derived a growth model based on optimal resource allocation and determined, using Bayesian statistics, the extent to which the model assumptions are supported by hatchery data on individual growth and compared fitted parameters across wild populations (Quince et al. 2008a and Quince et al. 2008b).
The majority of my early published research focused on food web models of community coevolution. I used simulations of these models to study the structure, resistance to perturbation and relationship between ecological role and evolutionary history in food web communities.
No studentships are currently available.
Tso, F. Y., D. C. Tully, S. Gonzalez, C. Quince, O. Ho, P. Polacino, R. M. Ruprecht, S.-L. Hu and C. Wood, 2012. Dynamics of Envelope Evolution in Clade C SHIV-infected Pig-tailed Macaques during Disease Progression analyzed by Ultra-deep Pyrosequencing. PLoS One 7, e32827 link.
Vos, M., C. Quince, A. Pijl, M. De Hollander and G. Kowalchuk, 2012. The use of rpoB as an alternative marker to 16S rRNA in pyrosequencing studies of bacterial diversity. PLoS One 7, e30600 link.
Holmes, I., K. Harris and C. Quince, 2012. Dirichlet multinomial mixtures: Generative models for microbial metagenomics. PLoS One 7, e30126 link.
Fonseca, V. G., B. Nichols, D. Lallias, C. Quince, G. R. Carvalho, D. M. Power and S. Creer, 2012. Sample richness and genetic diversity as drivers of chimera formation in nSSU metagenetic analyses. Nucleic Acids Reserch doi: 10.1093/nar/gks002 link .
Gobet, A., S. I. Böer, S. M. Huse, J. E. E. van Beusekom, C. Quince, M. L. Sogin, A. Boetius and Alban Ramette, 2012. Diversity and dynamics of rare and of resident bacterial populations in coastal sands. The ISME Journal 6, 542-553 link.
Gubry-Rangin, C., B. Hai, C. Quince, M. Engel, B. C. Thompson, P. James, M. Schloter, R. I. Griffiths, J. I. Prosser and G. W. Nicol, 2011. Niche specialization of terrestrial archaeal ammonia oxidizers. Proceedings of the National Academy of Sciences USA 108, 21206-21211.
Haig, S. J., G. Collins, R. L. Davies, C. C. Dorea and C. Quince, 2011. Biological aspects of slow sand filtration: past, present and future. Water Science & Technology: Water Supply 11, 468–472 preprint .
Nilsson, R. H., L. Tedersoo, B D. Lindahl, R. Kjøller, T. Carlsen, C. Quince, K. Abarenkov, T. Pennanen, J. Stenlid, T. Bruns, K.-H. Larsson, U. Kõljalg and H. Kauserud, 2011. Towards standardization of the description and publication of next-generation sequencing datasets of fungal communities. New Phytologist 191, 314–318.
Edgar, R. C., B. J. Haas, J. C. Clemente, C. Quince and R. Knight, 2011. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194-2200.
Hartmann, M., C. G. Howes, V. Veldre, S. Schneider, P. A. Vaishampayan, A. C. Yannarell, C. Quince, P. Johansson, K. J. Björkroth, K. Abarenkov, S. J. Hallam, W. W. Mohn, R. H. Nilsson, 2011. V-REVCOMP: automated high-throughput detection of reverse complementary 16S rRNA gene sequences in large environmental and taxonomic dataset. FEMS Microbiolgy Letters 319, 140-145.
Quince, C., A. Lanzen, R. J. Davenport and P. J. Turnbaugh, 2011. Removing Noise From Pyrosequenced Amplicons. BMC Bioinformatics 12:38 pdf.
Parsons, T. L., C. Quince and J. B. Plotkin, 2010. Some consequences of demographic stochasticity in population genetics. Genetics 185, 1345-1354 pdf.
Gobet, A., C. Quince and A. Ramette, 2010. Multivariate cutoff level analysis (MultiCoLA) of large community datasets. Nucleic Acids Research 38, e155.
Turnbaugh, P. J., C. Quince, J. J. Faith, A. McHardy, M. Egholm, B. Henrissat, R. Knight and J. I. Gordon, 2010. Organismal, genetic, and transcriptional variation in the deeply-sequenced gut microbiomes of identical twins. Proc. Natl. Acad. Sci. USA 107, 7503-7508 pdf.
Wang, G. P., S. A. Sherill-Mix, K. Chang, C. Quince and F. D. Bushman, 2010. HCV transmission and diversification in infected subjects analyzed by deep sequencing. Journal of Virology 84, 6218-6228.
Quince, C., A. Lanzen, T. P. Curtis, R. J. Davenport, N. Hall, I. M. Head, L. F. Read and W. T. Sloan 2009. Accurate determination of microbial diversity from 454 pyrosequencing data. Nature Met. 6, 639-641 pdf.
Parsons, T. L., C. Quince and J. B. Plotkin 2008. Absorption and fixation times for neutral and quasi-neutral populations with density dependence. Theor. Pop. Biol. 4, 302-310 pdf.
Quince, C., T. P. Curtis and W. T. Sloan 2008. The rational exploration of microbial diversity. Nature ISME 2, 997-1006 pdf.
Quince, C., B. J. Shuter, P. A. Abrams and N. P. Lester 2008. Biphasic growth in fish II: Empirical Assessment. J. Theor. Biol. 254, 207-214 pdf.
Quince, C., P. A. Abrams, B. J. Shuter and N. P. Lester 2008. Biphasic growth in fish I: Theoretical foundations. J. Theor. Biol. 254, 197-206 pdf.
Parsons, T. L. and C. Quince 2007. Fixation in haploid populations exhibiting density dependence II: The quasi-neutral case. Theor. Pop. Biol. 72, 468-479 pdf.
Parsons, T. L. and C. Quince 2007. Fixation in haploid populations exhibiting density dependence I: The non-neutral case. Theor. Pop. Biol. 72, 131-135 pdf.
Abrams, P. A. and C. Quince, 2005. The impact of mortality on predator population size and stability in systems with stage-structured prey. Theor. Pop. Biol. 68, 253-266.
Quince, C., P. G. Higgs and A. J. McKane, 2005. Topological structure and interaction strengths in model food webs. Ecol. Model. 187, 389-412.
Quince, C., P. G. Higgs and A. J. McKane, 2005. Deleting species from model food webs. Oikos 110, 283-296.
Drossel, B., A. J. McKane and C. Quince, 2004. The impact of non-linear functional responses on the long-term evolution of food web structure. J. Theor. Biol. 229, 539-548.
Newman T. J., J.-B. Ferdy and C. Quince, 2004. Extinction times and moment closure in the stochastic logistic process. Theor. Pop. Biol. 65, 115-126.
Quince, C., P. G. Higgs and A. J. McKane, 2002. Food web structure and the evolution of ecological communities, in Biological evolution and statistical physics, Lecture Notes in Physics 585, 281-298, Lassig, M. and A. Valleriani eds., Springer-Verlag.