I’m a data scientist in the ithree institute with an interest in understanding the evolutionary processes that drive the evolution of pathogens. I develop phylogenetic algorithms and software to investigate natural selection, evolutionary rates, and the geographic dispersal of viruses such as influenza virus and HIV. My research is part of a bigger picture looking at ways to better manage global issues such as antimicrobial resistance and infectious disease outbreaks.
Can supervise: YES
Hastak, P, Fourment, M, Darling, AE, Gottlieb, T, Cheong, E, Merlino, J, Myers, GSA, Djordjevic, SP & Chowdhury, PR 2020, 'Escherichia coli ST8196 is a novel, locally evolved, and extensively drug resistant pathogenic lineage within the ST131 clonal complex', Emerging Microbes and Infections, pp. 1-35.View/Download from: Publisher's site
© 2020, © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group, on behalf of Shanghai Shangyixun Cultural Communication Co., Ltd. The H30Rx subclade of Escherichia coli ST131 is a clinically important, globally dispersed extra-pathogenic lineage that typically displays resistance to fluoroquinolones and extended spectrum ß-lactams. Here we describe isolates EC233 and EC234, both variants of ST131-H30Rx with a novel sequence type (ST) 8196, from unrelated patients presenting with bacteraemia at Concord Repatriation Hospital in Sydney in 2014. EC233 and EC234 are phylogroup B2, serotype O25:H4A, resistant to ampicillin, amoxicillin, cefoxitin, ceftazidime, ceftriaxone, ciprofloxacin, norfloxacin and gentamicin and are likely clonal. Both isolates carry an IncFII_2 plasmid similar to pSPRC_Ec234-FII (85,199 bp characterised in EC234), two small plasmids and a novel IncI1 plasmid similar to pSPRC_Ec234-I (92,955 bp characterised in EC234). Apart from a chromosomally located bla CTX-M-15 module, the resistance genes are flanked by IS26 and form a complex resistance locus (CRL) on pSPRC_Ec234-FII. SNP-based phylogenetic analysis of the core genome of all ST representatives within the ST131 clonal complex places both isolates in a small subclade with 3 other clinical Australian ST131-H30Rx clade C isolates. MrBayes phylogeny analysis of ST8196 using a global collection of ST131 genomes indicated EC233 and EC234 share a most recent common ancestor with EC70, a MDR ST131-H30Rx clone, isolated from the same Sydney hospital in 2013. Our study identified genomic hallmarks that define the ST131-H30Rx subclade in both the ST8196 isolates and highlights the requirement for unbiased genomic surveillance approaches to identify and track novel high-risk MDR E. coli pathogens that impact healthcare facilities.
Bouckaert, R, Vaughan, TG, Barido-Sottani, J, Duchêne, S, Fourment, M, Gavryushkina, A, Heled, J, Jones, G, Kühnert, D, De Maio, N, Matschiner, M, Mendes, FK, Müller, NF, Ogilvie, HA, du Plessis, L, Popinga, A, Rambaut, A, Rasmussen, D, Siveroni, I, Suchard, MA, Wu, C-H, Xie, D, Zhang, C, Stadler, T & Drummond, AJ 2019, 'BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis.', PLoS computational biology, vol. 15, no. 4.View/Download from: Publisher's site
Elaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data. It is increasingly appreciated that some evolutionary questions can only be adequately answered by combining evidence from multiple independent sources of data, including genome sequences, sampling dates, phenotypic data, radiocarbon dates, fossil occurrences, and biogeographic range information among others. Including all relevant data into a single joint model is very challenging both conceptually and computationally. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing such software frameworks is increasingly a major scientific activity in its own right, and comes with specific challenges, from practical software design, development and engineering challenges to statistical and conceptual modelling challenges. BEAST 2 is one such computational software platform, and was first announced over 4 years ago. Here we describe a series of major new developments in the BEAST 2 core platform and model hierarchy that have occurred since the first release of the software, culminating in the recent 2.5 release.
Fourment, M, Magee, AF, Whidden, C, Bilge, A, Matsen, FA & Minin, VN 2019, '19 dubious ways to compute the marginal likelihood of a phylogenetic tree topology.', Systematic Biology.View/Download from: Publisher's site
The marginal likelihood of a model is a key quantity for assessing the evidence provided by the data in support of a model. The marginal likelihood is the normalizing constant for the posterior density, obtained by integrating the product of the likelihood and the prior with respect to model parameters. Thus, the computational burden of computing the marginal likelihood scales with the dimension of the parameter space. In phylogenetics, where we work with tree topologies that are high-dimensional models, standard approaches to computing marginal likelihoods are very slow. Here we study methods to quickly compute the marginal likelihood of a single fixed tree topology. We benchmark the speed and accuracy of 19 different methods to compute the marginal likelihood of phylogenetic topologies on a suite of real datasets under the JC69 model. These methods include several new ones that we develop explicitly to solve this problem, as well as existing algorithms that we apply to phylogenetic models for the first time. Altogether, our results show that the accuracy of these methods varies widely, and that accuracy does not necessarily correlate with computational burden. Our newly developed methods are orders of magnitude faster than standard approaches, and in some cases, their accuracy rivals the best established estimators.
Roy Chowdhury, P, Fourment, M, DeMaere, MZ, Monahan, L, Merlino, J, Gottlieb, T, Darling, AE & Djordjevic, SP 2019, 'Identification of a novel lineage of plasmids within phylogenetically diverse subclades of IncHI2-ST1 plasmids.', Plasmid, vol. 102, pp. 56-61.View/Download from: Publisher's site
IncHI2-ST1 plasmids play an important role in co-mobilizing genes conferring resistance to critically important antibiotics and heavy metals. Here we present the identification and analysis of IncHI2-ST1 plasmid pSPRC-Echo1, isolated from an Enterobacter hormaechei strain from a Sydney hospital, which predates other multi-drug resistant IncHI2-ST1 plasmids reported from Australia. Our time-resolved phylogeny analysis indicates pSPRC-Echo1 represents a new lineage of IncHI2-ST1 plasmids and show how their diversification relates to the era of antibiotics.
Whidden, C, Claywell, BC, Fisher, T, Magee, AF, Fourment, M & Matsen, FA 2019, 'Systematic exploration of the high likelihood set of phylogenetic tree topologies.', Systematic Biology.View/Download from: Publisher's site
Bayesian Markov chain Monte Carlo explores tree space slowly, in part because it frequently returns to the same tree topology. An alternative strategy would be to explore tree space systematically, and never return to the same topology. In this paper, we present an efficient parallelized method to map out the high likelihood set of phylogenetic tree topologies via systematic search, which we show to be a good approximation of the high posterior set of tree topologies on the data sets analyzed. Here "likelihood" of a topology refers to the tree likelihood for the corresponding tree with optimized branch lengths. We call this method "phylogenetic topographer" (PT). The PT strategy is very simple: starting in a number of local topology maxima (obtained by hill-climbing from random starting points), explore out using local topology rearrangements, only continuing through topologies that are better than some likelihood threshold below the best observed topology. We show that the normalized topology likelihoods are a useful proxy for the Bayesian posterior probability of those topologies. By using a non-blocking hash table keyed on unique representations of tree topologies, we avoid visiting topologies more than once across all concurrent threads exploring tree space. We demonstrate that PT can be used directly to approximate a Bayesian consensus tree topology. When combined with an accurate means of evaluating per-topology marginal likelihoods, PT gives an alternative procedure for obtaining Bayesian posterior distributions on phylogenetic tree topologies.
Kretzschmar, AL, Verma, A, Murray, S, Kahlke, T, Fourment, M & Darling, A 2019, 'Trial by phylogenetics - Evaluating the Multi-Species Coalescent for phylogenetic inference on taxa with high levels of paralogy (Gonyaulacales, Dinophyceae)'.View/Download from: Publisher's site
ABSTRACT From publicly available next-gen sequencing datasets of non-model organisms, such as marine protists, arise opportunities to explore their evolutionary relationships. In this study we explored the effects that dataset and model selection have on the phylogenetic inference of the Gonyaulacales, single celled marine algae of the phylum Dinoflagellata with genomes that show extensive paralogy. We developed a method for identifying and extracting single copy genes from RNA-seq libraries and compared phylogenies inferred from these single copy genes with those inferred from commonly used genetic markers and phylogenetic methods. Comparison of two datasets and three different phylogenetic models showed that exclusive use of ribosomal DNA sequences, maximum likelihood and gene concatenation showed very different results to that obtained with the multi-species coalescent. The multi-species coalescent has recently been recognized as being robust to the inclusion of paralogs, including hidden paralogs present in single copy gene sets (pseudoorthologs). Comparisons of model fit strongly favored the multi-species coalescent for these data, over a concatenated alignment (single tree) model. Our findings suggest that the multi-species coalescent (inferred either via Maximum Likelihood or Bayesian Inference) should be considered for future phylogenetic studies of organisms where accurate selection of orthologs is difficult.
Claywell, BC, Dinh, V, Fourment, M, McCoy, CO & Matsen, FA 2018, 'A Surrogate Function for One-Dimensional Phylogenetic Likelihoods', Molecular Biology and Evolution, vol. 35, no. 1, pp. 242-246.View/Download from: Publisher's site
© The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: email@example.com. Phylogenetics has seen a steady increase in data set size and substitution model complexity, which require increasing amounts of computational power to compute likelihoods. This motivates strategies to approximate the likelihood functions for branch length optimization and Bayesian sampling. In this article, we develop an approximation to the 1D likelihood function as parametrized by a single branch length. Our method uses a four-parameter surrogate function abstracted from the simplest phylogenetic likelihood function, the binary symmetric model. We show that it offers a surrogate that can be fit over a variety of branch lengths, that it is applicable to a wide variety of models and trees, and that it can be used effectively as a proposal mechanism for Bayesian sampling. The method is implemented as a stand-Alone open-source C library for calling from phylogenetics algorithms; it has proven essential for good performance of our online phylogenetic algorithm sts.
Time-resolved phylogenetic methods use information about the time of sample collection to estimate the rate of evolution. Originally, the models used to estimate evolutionary rates were quite simple, assuming that all lineages evolve at the same rate, an assumption commonly known as the molecular clock. Richer and more complex models have since been introduced to capture the phenomenon of substitution rate variation among lineages. Two well known model extensions are the local clock, wherein all lineages in a clade share a common substitution rate, and the uncorrelated relaxed clock, wherein the substitution rate on each lineage is independent from other lineages while being constrained to fit some parametric distribution. We introduce a further model extension, called the flexible local clock (FLC), which provides a flexible framework to combine relaxed clock models with local clock models. We evaluate the flexible local clock on simulated and real datasets and show that it provides substantially improved fit to an influenza dataset. An implementation of the model is available for download from https://www.github.com/4ment/flc.
Fourment, M, Claywell, BC, Dinh, V, McCoy, C, Matsen Iv, FA & Darling, AE 2018, 'Effective Online Bayesian Phylogenetics via Sequential Monte Carlo with Guided Proposals.', Systematic Biology, vol. 67, no. 3, pp. 490-502.View/Download from: Publisher's site
Modern infectious disease outbreak surveillance produces continuous streams of sequence data which require phylogenetic analysis as data arrives. Current software packages for Bayesian phylogenetic inference are unable to quickly incorporate new sequences as they become available, making them less useful for dynamically unfolding evolutionary stories. This limitation can be addressed by applying a class of Bayesian statistical inference algorithms called sequential Monte Carlo (SMC) to conduct online inference, wherein new data can be continuously incorporated to update the estimate of the posterior probability distribution. In this article, we describe and evaluate several different online phylogenetic sequential Monte Carlo (OPSMC) algorithms. We show that proposing new phylogenies with a density similar to the Bayesian prior suffers from poor performance, and we develop "guided" proposals that better match the proposal density to the posterior. Furthermore, we show that the simplest guided proposals can exhibit pathological behavior in some situations, leading to poor results, and that the situation can be resolved by heating the proposal density. The results demonstrate that relative to the widely used MCMC-based algorithm implemented in MrBayes, the total time required to compute a series of phylogenetic posteriors as sequences arrive can be significantly reduced by the use of OPSMC, without incurring a significant loss in accuracy.
Fourment, Darling, AE & Holmes, EC 2017, 'The Impact of Migratory Flyways on the Spread of Avian Influenza Virus in North America', BMC Evolutionary Biology, vol. 17, no. 1.View/Download from: Publisher's site
Wild birds are the major reservoir hosts for influenza A viruses (AIVs) and have been implicated in the emergence of pandemic events in livestock and human populations. Understanding how AIVs spread within and across continents is therefore critical to the development of successful strategies to manage and reduce the impact of influenza outbreaks. In North America many bird species undergo seasonal migratory movements along a North-South axis, thereby fostering opportunities for viruses to spread over long distances. However, the role played by such avian flyways in shaping the genetic structure of AIV populations has proven controversial. To assess the relative contribution of bird migration along flyways to the genetic structure of AIV we performed a large-scale phylogeographic study of viruses sampled in the USA and Canada, involving the analysis of 3805 to 4505 sequences from 36 to 38 geographic localities depending on the gene data set. To assist this we developed a maximum likelihood-based genetic algorithm to explore a wide range of complex spatial models, thereby depicting a more complete picture of the migration network than previous studies. Based on phylogenies estimated from nucleotide data sets, our results show that AIV migration rates within flyways are significantly higher than those between flyways, indicating that the migratory patterns of birds play a key role in pathogen dispersal. These findings provide valuable insights into the evolution, maintenance and transmission of AIVs, in turn allowing the development of improved programs for surveillance and risk assessment.
Fourment, M, Claywell, B, Dinh, V, McCoy, C, Matsen, F & Darling, A 2017, 'Effective online Bayesian phylogenetics via sequential Monte Carlo with guided proposals'.View/Download from: Publisher's site
A bstract Modern infectious disease outbreak surveillance produces continuous streams of sequence data which require phylogenetic analysis as data arrives. Current software packages for Bayesian phy-logenetic inference are unable to quickly incorporate new sequences as they become available, making them less useful for dynamically unfolding evolutionary stories. This limitation can be addressed by applying a class of Bayesian statistical inference algorithms called sequential Monte Carlo (SMC) to conduct online inference , wherein new data can be continuously incorporated to update the estimate of the posterior probability distribution. In this paper we describe and evaluate several different online phylogenetic sequential Monte Carlo (OPSMC) algorithms. We show that proposing new phylogenies with a density similar to the Bayesian prior suffers from poor performance, and we develop 'guided' proposals that better match the proposal density to the posterior. Furthermore, we show that the simplest guided proposals can exhibit pathological behavior in some situations, leading to poor results, and that the situation can be resolved by heating the proposal density. The results demonstrate that relative to the widely-used MCMC-based algorithm implemented in MrBayes, the total time required to compute a series of phylogenetic posteriors as sequences arrive can be significantly reduced by the use of OPSMC, without incurring a significant loss in accuracy.
Bos, KI, Herbig, A, Sahl, J, Waglechner, N, Fourment, M, Forrest, SA, Klunk, J, Schuenemann, VJ, Poinar, D, Kuch, M, Golding, GB, Dutour, O, Keim, P, Wagner, DM, Holmes, EC, Krause, J & Poinar, HN 2016, 'Eighteenth century Yersinia pestis genomes reveal the long-term persistence of an historical plague focus', ELIFE, vol. 5.View/Download from: Publisher's site
Duchene, S, Holt, KE, Weill, F-X, Le Hello, S, Hawkey, J, Edwards, DJ, Fourment, M & Holmes, EC 2016, 'Genome-scale rates of evolutionary change in bacteria', MICROBIAL GENOMICS, vol. 2, no. 11.View/Download from: Publisher's site
Accurate multiple sequence alignment is central to bioinformatics and molecular evolutionary analyses. Although sophisticated sequence alignment programs are available, manual adjustments are often required to improve alignment quality. Unfortunately, few programs offer a simple and intuitive way to edit sequence alignments.We present Seqotron, a sequence editor that reads and writes files in a wide variety of sequence formats. Sequences can be easily aligned and manually edited using the mouse and keyboard. The program also allows the user to estimate both phylogenetic trees and distance matrices.Seqotron will benefit researchers who need to manipulate and align complex sequence data. Seqotron is a Mac OS X compatible open source project and is available from Github https://github.com/4ment/seqotron/.
Abstract Wild birds are the major reservoir hosts for influenza A viruses (AIVs) and have been implicated in the emergence of pandemic events in livestock and human populations. Understanding how AIVs spread within and across continents is therefore critical to the development of successful strategies to manage and reduce the impact of influenza outbreaks. In North America many bird species undergo seasonal migratory movements along a North-South axis, thereby fostering opportunities for viruses to spread over long distances. However, the role played by such avian flyways in shaping the genetic structure of AIV populations has proven controversial. To assess the relative contribution of bird migration along flyways to the genetic structure of AIV we performed a large-scale phylogeographic study of viruses sampled in the USA and Canada, involving the analysis of 3805 to 4505 sequences from 36 to 38 geographic localities depending on the gene data set. To assist this we developed a maximum likelihood-based genetic algorithm to explore a wide range of complex spatial models, thereby depicting a more complete picture of the migration network than previous studies. Based on phylogenies estimated from nucleotide data sets, our results show that AIV migration rates within flyways are significantly higher than those between flyways, indicating that the migratory patterns of birds play a key role in pathogen dispersal. These findings provide valuable insights into the evolution, maintenance and transmission of AIVs, in turn allowing the development of improved programs for surveillance and risk assessment. Significance Statement Avian influenza viruses infect a wide variety of wild bird species and represent a potential disease threat to the poultry industry and hence to human and livestock populations. However, the ecological factors that drive the geographic spread and evolution of these viruses are both poorly understood and controversial at the cont...
Sam, I-C, Su, YCF, Chan, YF, Nor'E, SS, Hassan, A, Jafar, FL, Joseph, U, Halpin, RA, Ghedin, E, Hooi, PS, Fourment, M, Hassan, H, AbuBakar, S, Wentworth, DE & Smith, GJD 2015, 'Evolution of Influenza B Virus in Kuala Lumpur, Malaysia, between 1995 and 2008', JOURNAL OF VIROLOGY, vol. 89, no. 18, pp. 9689-9692.View/Download from: Publisher's site
Vijaykrishna, D, Holmes, EC, Joseph, U, Fourment, M, Su, YCF, Halpin, R, Lee, RTC, Deng, Y-M, Gunalan, V, Lin, X, Stockwell, TB, Fedorova, NB, Zhou, B, Spirason, N, Kuehnert, D, Boskova, V, Stadler, T, Costa, A-M, Dwyer, DE, Huang, QS, Jennings, LC, Rawlinson, W, Sullivan, SG, Hurt, AC, Maurer-Stroh, S, Wentworth, DE, Smith, GJD & Barr, IG 2015, 'The contrasting phylodynamics of human infuenza B viruses', ELIFE, vol. 4.View/Download from: Publisher's site
Fourment, M & Holmes, EC 2014, 'Novel non-parametric models to estimate evolutionary rates and divergence times from heterochronous sequence data.', BMC Evolutionary Biology, vol. 14, pp. 1-12.View/Download from: Publisher's site
BACKGROUND: Early methods for estimating divergence times from gene sequence data relied on the assumption of a molecular clock. More sophisticated methods were created to model rate variation and used auto-correlation of rates, local clocks, or the so called "uncorrelated relaxed clock" where substitution rates are assumed to be drawn from a parametric distribution. In the case of Bayesian inference methods the impact of the prior on branching times is not clearly understood, and if the amount of data is limited the posterior could be strongly influenced by the prior. RESULTS: We develop a maximum likelihood method--Physher--that uses local or discrete clocks to estimate evolutionary rates and divergence times from heterochronous sequence data. Using two empirical data sets we show that our discrete clock estimates are similar to those obtained by other methods, and that Physher outperformed some methods in the estimation of the root age of an influenza virus data set. A simulation analysis suggests that Physher can outperform a Bayesian method when the real topology contains two long branches below the root node, even when evolution is strongly clock-like. CONCLUSIONS: These results suggest it is advisable to use a variety of methods to estimate evolutionary rates and divergence times from heterochronous sequence data. Physher and the associated data sets used here are available online at http://code.google.com/p/physher/.
Wagner, DM, Klunk, J, Harbeck, M, Devault, A, Waglechner, N, Sahl, JW, Enk, J, Birdsell, DN, Kuch, M, Lumibao, C, Poinar, D, Pearson, T, Fourment, M, Golding, B, Riehm, JM, Earn, DJ, Dewitte, S, Rouillard, JM, Grupe, G, Wiechmann, I, Bliska, JB, Keim, PS, Scholz, HC, Holmes, EC & Poinar, H 2014, 'Yersinia pestis and the plague of Justinian 541-543 AD: a genomic analysis.', The Lancet Infectious Diseases, vol. 14, no. 4, pp. 319-326.View/Download from: Publisher's site
BACKGROUND: Yersinia pestis has caused at least three human plague pandemics. The second (Black Death, 14-17th centuries) and third (19-20th centuries) have been genetically characterised, but there is only a limited understanding of the first pandemic, the Plague of Justinian (6-8th centuries). To address this gap, we sequenced and analysed draft genomes of Y pestis obtained from two individuals who died in the first pandemic. METHODS: Teeth were removed from two individuals (known as A120 and A76) from the early medieval Aschheim-Bajuwarenring cemetery (Aschheim, Bavaria, Germany). We isolated DNA from the teeth using a modified phenol-chloroform method. We screened DNA extracts for the presence of the Y pestis-specific pla gene on the pPCP1 plasmid using primers and standards from an established assay, enriched the DNA, and then sequenced it. We reconstructed draft genomes of the infectious Y pestis strains, compared them with a database of genomes from 131 Y pestis strains from the second and third pandemics, and constructed a maximum likelihood phylogenetic tree. FINDINGS: Radiocarbon dating of both individuals (A120 to 533 AD [plus or minus 98 years]; A76 to 504 AD [plus or minus 61 years]) places them in the timeframe of the first pandemic. Our phylogeny contains a novel branch (100% bootstrap at all relevant nodes) leading to the two Justinian samples. This branch has no known contemporary representatives, and thus is either extinct or unsampled in wild rodent reservoirs. The Justinian branch is interleaved between two extant groups, 0.ANT1 and 0.ANT2, and is distant from strains associated with the second and third pandemics. INTERPRETATION: We conclude that the Y pestis lineages that caused the Plague of Justinian and the Black Death 800 years later were independent emergences from rodents into human beings. These results show that rodent species worldwide represent important reservoirs for the repeated emergence of diverse lineages of Y pestis into hum...
Bahl, J, Krauss, S, Kuehnert, D, Fourment, M, Raven, G, Pryor, SP, Niles, LJ, Danner, A, Walker, D, Mendenhall, IH, Su, YCF, Dugan, VG, Halpin, RA, Stockwell, TB, Webby, RJ, Wentworth, DE, Drummond, AJ, Smith, GJD & Webster, RG 2013, 'Influenza A Virus Migration and Persistence in North American Wild Birds', PLOS PATHOGENS, vol. 9, no. 8.View/Download from: Publisher's site
Shanmuganatham, K, Feeroz, MM, Jones-Engel, L, Smith, GJD, Fourment, M, Walker, D, McClenaghan, L, Alam, SMR, Hasan, MK, Seiler, P, Franks, J, Danner, A, Barman, S, McKenzie, P, Krauss, S, Webby, RJ & Webster, RG 2013, 'Antigenic and Molecular Characterization of Avian Influenza A(H9N2) Viruses, Bangladesh', EMERGING INFECTIOUS DISEASES, vol. 19, no. 9, pp. 1393-1402.View/Download from: Publisher's site
Vijaykrishna, D, Deng, Y-M, Su, YCF, Fourment, M, Iannello, P, Arzey, GG, Hansbro, PM, Arzey, KE, Kirkland, PD, Warner, S, O'Riley, K, Barr, IG, Smith, GJD & Hurt, AC 2013, 'The Recent Establishment of North American H10 Lineage Influenza Viruses in Australian Wild Waterfowl and the Evolution of Australian Avian Influenza Viruses', JOURNAL OF VIROLOGY, vol. 87, no. 18, pp. 10182-10189.View/Download from: Publisher's site
Wertheim, JO, Fourment, M & Pond, SLK 2012, 'Inconsistencies in Estimating the Age of HIV-1 Subtypes Due to Heterotachy', MOLECULAR BIOLOGY AND EVOLUTION, vol. 29, no. 2, pp. 451-456.View/Download from: Publisher's site
Westgeest, KB, de Graaf, M, Fourment, M, Bestebroer, TM, van Beek, R, Spronken, MIJ, de Jong, JC, Rimmelzwaan, GF, Russell, CA, Osterhaus, ADME, Smith, GJD, Smith, DJ & Fouchier, RAM 2012, 'Genetic evolution of the neuraminidase of influenza A (H3N2) viruses from 1968 to 2009 and its correspondence to haemagglutinin evolution', JOURNAL OF GENERAL VIROLOGY, vol. 93, pp. 1996-2007.View/Download from: Publisher's site
Pond, SLK, Murrell, B, Fourment, M, Frost, SDW, Delport, W & Scheffler, K 2011, 'A Random Effects Branch-Site Model for Detecting Episodic Diversifying Selection', MOLECULAR BIOLOGY AND EVOLUTION, vol. 28, no. 11, pp. 3033-3043.View/Download from: Publisher's site
Duval, L, Fourment, M, Nerrienet, E, Rousset, D, Sadeuh, SA, Goodman, SM, Andriaholinirina, NV, Randrianarivelojosia, M, Paul, RE, Robert, V, Ayala, FJ & Ariey, F 2010, 'African apes as reservoirs of Plasmodium falciparum and the origin and diversification of the Laverania subgenus', PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, vol. 107, no. 23, pp. 10561-10566.View/Download from: Publisher's site
Fourment, M, Mardy, S, Channa, M & Buchy, P 2010, 'Evidence for Persistence of and Antiviral Resistance and Reassortment Events in Seasonal Influenza Virus Strains Circulating in Cambodia', JOURNAL OF CLINICAL MICROBIOLOGY, vol. 48, no. 1, pp. 295-297.View/Download from: Publisher's site
Fourment, M, Wood, JT, Gibbs, AJ & Gibbs, MJ 2010, 'Evolutionary dynamics of the N1 neuraminidases of the main lineages of influenza A viruses', MOLECULAR PHYLOGENETICS AND EVOLUTION, vol. 56, no. 2, pp. 526-535.View/Download from: Publisher's site
Buchy, P, Fourment, M, Mardy, S, Sorn, S, Holl, D, Ly, S, Vong, S, Enouf, V, Peiris, JSM & van der Werf, S 2009, 'Molecular Epidemiology of Clade 1 Influenza A Viruses (H5N1), Southern Indochina Peninsula, 2004–2007', Emerging Infectious Diseases, vol. 15, no. 10, pp. 1641-1644.View/Download from: Publisher's site
Duval, L, Nerrienet, E, Rousset, D, Mba, SAS, Houze, S, Fourment, M, Le Bras, J, Robert, V & Ariey, F 2009, 'Chimpanzee Malaria Parasites Related to Plasmodium ovale in Africa', PLOS ONE, vol. 4, no. 5.View/Download from: Publisher's site
Fourment, M & Gibbs, MJ 2008, 'The VirusBanker database uses a Java program to allow flexible searching through Bunyaviridae sequences', BMC BIOINFORMATICS, vol. 9.View/Download from: Publisher's site
Fourment, M, Gibbs, AJ & Gibbs, MJ 2008, 'SWeBLAST: A sliding window web-based BLAST tool for recombinant analysis', JOURNAL OF VIROLOGICAL METHODS, vol. 152, no. 1-2, pp. 98-101.View/Download from: Publisher's site
Zheng, L, Wayper, PJ, Gibbs, AJ, Fourment, M, Rodoni, BC & Gibbs, MJ 2008, 'Accumulating Variation at Conserved Sites in Potyvirus Genomes Is Driven by Species Discovery and Affects Degenerate Primer Design', PLOS ONE, vol. 3, no. 2.View/Download from: Publisher's site
Gibbs, MJ, Wayper, P, Fourment, MLA, Wood, JT, Ohshima, K, Armstrong, JS & Gibbs, AJ 2007, 'The variable codons of H3 influenza A virus haemagglutinin genes', ARCHIVES OF VIROLOGY, vol. 152, no. 1, pp. 11-24.View/Download from: Publisher's site
Abstract Recent advances in statistical machine learning techniques have led to the creation of probabilistic programming frameworks. These frameworks enable probabilistic models to be rapidly prototyped and fit to data using scalable approximation methods such as variational inference. In this work, we explore the use of the Stan language for probabilistic programming in application to phylogenetic models. We show that many commonly used phylogenetic models including the general time reversible (GTR) substitution model, rate heterogeneity among sites, and a range of coalescent models can be implemented using a probabilistic programming language. The posterior probability distributions obtained via the black box variational inference engine in Stan were compared to those obtained with reference implementations of Markov chain Monte Carlo (MCMC) for phylogenetic inference. We find that black box variational inference in Stan is less accurate than MCMC methods for phylogenetic models, but requires far less compute time. Finally, we evaluate a custom implementation of mean-field variational inference on the Jukes-Cantor substitution model and show that a specialized implementation of variational inference can be two orders of magnitude faster and more accurate than a general purpose probabilistic implementation.