90 results found
Stimson J, Gardy J, Mathema B, et al., 2019, Beyond the SNP Threshold: Identifying Outbreak Clusters Using Inferred Transmissions., Mol Biol Evol, Vol: 36, Pages: 587-603
Whole-genome sequencing (WGS) is increasingly used to aid the understanding of pathogen transmission. A first step in analyzing WGS data is usually to define "transmission clusters," sets of cases that are potentially linked by direct transmission. This is often done by including two cases in the same cluster if they are separated by fewer single-nucleotide polymorphisms (SNPs) than a specified threshold. However, there is little agreement as to what an appropriate threshold should be. We propose a probabilistic alternative, suggesting that the key inferential target for transmission clusters is the number of transmissions separating cases. We characterize this by combining the number of SNP differences and the length of time over which those differences have accumulated, using information about case timing, molecular clock, and transmission processes. Our framework has the advantage of allowing for variable mutation rates across the genome and can incorporate other epidemiological data. We use two tuberculosis studies to illustrate the impact of our approach: with British Columbia data by using spatial divisions; with Republic of Moldova data by incorporating antibiotic resistance. Simulation results indicate that our transmission-based method is better in identifying direct transmissions than a SNP threshold, with dissimilarity between clusterings of on average 0.27 bits compared with 0.37 bits for the SNP-threshold method and 0.84 bits for randomly permuted data. These results show that it is likely to outperform the SNP-threshold method where clock rates are variable and sample collection times are spread out. We implement the method in the R package transcluster.
Mabud TS, de Lourdes Delgado Alves M, Ko AI, et al., 2019, Correction: Evaluating strategies for control of tuberculosis in prisons and prevention of spillover into communities: An observational and modeling study from Brazil., PLoS Med, Vol: 16
[This corrects the article DOI: 10.1371/journal.pmed.1002737.].
The relationship between the underlying contact network over which a pathogen spreads and the pathogen phylogenetic trees that are obtained presents an opportunity to use sequence data to learn about contact networks that are difficult to study empirically. However, this relationship is not explicitly known and is usually studied in simulations, often with the simplifying assumption that the contact network is static in time, though human contact networks are dynamic. We simulate pathogen phylogenetic trees on dynamic Erdős-Renyi random networks and on two dynamic networks with skewed degree distribution, of which one is additionally clustered. We use tree shape features to explore how adding dynamics changes the relationships between the overall network structure and phylogenies. Our tree features include the number of small substructures (cherries, pitchforks) in the trees, measures of tree imbalance (Sackin index, Colless index), features derived from network science (diameter, closeness), as well as features using the internal branch lengths from the tip to the root. Using principal component analysis we find that the network dynamics influence the shapes of phylogenies, as does the network type. We also compare dynamic and time-integrated static networks. We find, in particular, that static network models like the widely used Barabasi-Albert model can be poor approximations for dynamic networks. We explore the effects of mis-specifying the network on the performance of classifiers trained identify the transmission rate (using supervised learning methods). We find that both mis-specification of the underlying network and its parameters (mean degree, turnover rate) have a strong adverse effect on the ability to estimate the transmission parameter. We illustrate these results by classifying HIV trees with a classifier that we trained on simulated trees from different networks, infection rates and turnover rates. Our results point to the importance of correctly est
Mabud TS, Delgado Alves MDL, Ko AI, et al., 2019, Evaluating strategies for control of tuberculosis in prisons and prevention of spillover into communities: An observational and modeling study from Brazil, PLOS MEDICINE, Vol: 16, ISSN: 1549-1676
Ayabina D, Ronning JO, Alfsnes K, et al., 2018, Genome-based transmission modelling separates imported tuberculosis from recent transmission within an immigrant population, MICROBIAL GENOMICS, Vol: 4, ISSN: 2057-5858
Yang C, Lu L, Warren JL, et al., 2018, Internal migration and transmission dynamics of tuberculosis in Shanghai, China: an epidemiological, spatial, genomic analysis, LANCET INFECTIOUS DISEASES, Vol: 18, Pages: 788-795, ISSN: 1473-3099
Kendall ML, Ayabina P, Xu Y, et al., 2018, Estimating Transmission from Genetic and Epidemiological Data: A Metric to Compare Transmission Trees, Statistical Science, Vol: 33, Pages: 70-85, ISSN: 0883-4237
Reconstructing who infected whom is a central challenge in analysing epidemiological data. Recently, advances in sequencing technology have led to increasing interest in Bayesian approaches to inferring who infected whom using genetic data from pathogens. The logic behind such approaches is that isolates that are nearly genetically identical are more likely to have been recently transmitted than those that are very different. A number of methods have been developed to perform this inference. However, testing their convergence, examining posterior sets of transmission trees and comparing methods’ performance are challenged by the fact that the object of inference—the transmission tree—is a complicated discrete structure. We introduce a metric on transmission trees to quantify distances between them. The metric can accommodate trees with unsampled individuals, and highlights differences in the source case and in the number of infections per infector. We illustrate its performance on simple simulated scenarios and on posterior transmission trees from a TB outbreak. We find that the metric reveals where the posterior is sensitive to the priors, and where collections of trees are composed of distinct clusters. We use the metric to define median trees summarising these clusters. Quantitative tools to compare transmission trees to each other will be required for assessing MCMC convergence, exploring posterior trees and benchmarking diverse methods as this field continues to mature.
Kendall M, Ayabina D, Xu Y, et al., 2018, Estimating Transmission from Genetic and Epidemiological Data: A Metric to Compare Transmission Trees, Publisher: INST MATHEMATICAL STATISTICS
Yaesoubi R, Trotter C, Colijn C, et al., 2018, The cost-effectiveness of alternative vaccination strategies for polyvalent meningococcal vaccines in Burkina Faso: A transmission dynamic modeling study, PLOS MEDICINE, Vol: 15, ISSN: 1549-1676
Colijn C, Plazzotta G, 2018, A Metric on Phylogenetic Tree Shapes, SYSTEMATIC BIOLOGY, Vol: 67, Pages: 113-126, ISSN: 1063-5157
Lees JA, Kendall M, Parkhill J, et al., 2018, Evaluation of phylogenetic reconstruction methods using bacterial whole genomes: a simulation based study., Wellcome Open Res, Vol: 3, ISSN: 2398-502X
Background: Phylogenetic reconstruction is a necessary first step in many analyses which use whole genome sequence data from bacterial populations. There are many available methods to infer phylogenies, and these have various advantages and disadvantages, but few unbiased comparisons of the range of approaches have been made. Methods: We simulated data from a defined "true tree" using a realistic evolutionary model. We built phylogenies from this data using a range of methods, and compared reconstructed trees to the true tree using two measures, noting the computational time needed for different phylogenetic reconstructions. We also used real data from Streptococcus pneumoniae alignments to compare individual core gene trees to a core genome tree. Results: We found that, as expected, maximum likelihood trees from good quality alignments were the most accurate, but also the most computationally intensive. Using less accurate phylogenetic reconstruction methods, we were able to obtain results of comparable accuracy; we found that approximate results can rapidly be obtained using genetic distance based methods. In real data we found that highly conserved core genes, such as those involved in translation, gave an inaccurate tree topology, whereas genes involved in recombination events gave inaccurate branch lengths. We also show a tree-of-trees, relating the results of different phylogenetic reconstructions to each other. Conclusions: We recommend three approaches, depending on requirements for accuracy and computational time. Quicker approaches that do not perform full maximum likelihood optimisation may be useful for many analyses requiring a phylogeny, as generating a high quality input alignment is likely to be the major limiting factor of accurate tree topology. We have publicly released our simulated data and code to enable further comparisons.
Grandjean L, Gilman RH, Iwamoto T, et al., 2017, Convergent evolution and topologically disruptive polymorphisms among multidrug-resistant tuberculosis in Peru, PLOS ONE, Vol: 12, ISSN: 1932-6203
Jombart T, Kendall M, Almagro-Garcia J, et al., 2017, treespace: Statistical exploration of landscapes of phylogenetic trees, MOLECULAR ECOLOGY RESOURCES, Vol: 17, Pages: 1385-1392, ISSN: 1755-098X
Ratmann O, Wymant C, Colijn C, et al., 2017, HIV-1 Full-Genome Phylogenetics of Generalized Epidemics in Sub-Saharan Africa: Impact of Missing Nucleotide Characters in Next-Generation Sequences, AIDS RESEARCH AND HUMAN RETROVIRUSES, Vol: 33, Pages: 1083-1098, ISSN: 0889-2229
Sartelli M, Weber DG, Ruppe E, et al., 2017, Antimicrobials: a global alliance for optimizing their rational use in intra-abdominal infections (AGORA) (vol 11, 33, 2016), WORLD JOURNAL OF EMERGENCY SURGERY, Vol: 12, ISSN: 1749-7922
Cobey S, Baskerville EB, Colijn C, et al., 2017, Host population structure and treatment frequency maintain balancing selection on drug resistance, JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 14, ISSN: 1742-5689
Fyson N, King J, Belcher T, et al., 2017, A curated genome-scale metabolic model of Bordetella pertussis metabolism, PLOS COMPUTATIONAL BIOLOGY, Vol: 13, ISSN: 1553-734X
Klinkenberg D, Backer JA, Didelot X, et al., 2017, Simultaneous inference of phylogenetic and transmission trees in infectious disease outbreaks, PLOS COMPUTATIONAL BIOLOGY, Vol: 13, ISSN: 1553-734X
Didelot X, Fraser C, Gardy J, et al., 2017, Genomic Infectious Disease Epidemiology in Partially Sampled and Ongoing Outbreaks, MOLECULAR BIOLOGY AND EVOLUTION, Vol: 34, Pages: 997-1007, ISSN: 0737-4038
Colijn C, Jones N, Johnston IG, et al., 2017, Toward Precision Healthcare: Context and Mathematical Challenges, FRONTIERS IN PHYSIOLOGY, Vol: 8, ISSN: 1664-042X
Ratmann O, Hodcroft EB, Pickles M, et al., 2017, Phylogenetic Tools for Generalized HIV-1 Epidemics: Findings from the PANGEA-HIV Methods Comparison, MOLECULAR BIOLOGY AND EVOLUTION, Vol: 34, Pages: 185-203, ISSN: 0737-4038
Plazzotta G, Colijn C, 2016, ASYMPTOTIC FREQUENCY OF SHAPES IN SUPERCRITICAL BRANCHING TREES, Publisher: CAMBRIDGE UNIV PRESS
Ayabina D, Hendon-Dunn C, Bacon J, et al., 2016, Diverse drug-resistant subpopulations of Mycobacterium tuberculosis are sustained in continuous culture, JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 13, ISSN: 1742-5689
Kendall M, Colijn C, 2016, Mapping Phylogenetic Trees to Reveal Distinct Patterns of Evolution, MOLECULAR BIOLOGY AND EVOLUTION, Vol: 33, Pages: 2735-2743, ISSN: 0737-4038
Sartelli M, Weber DG, Ruppe E, et al., 2016, Antimicrobials: a global alliance for optimizing their rational use in intra-abdominal infections (AGORA), WORLD JOURNAL OF EMERGENCY SURGERY, Vol: 11, ISSN: 1749-7922
Aanensen DM, Feil EJ, Holden MTG, et al., 2016, Whole-Genome Sequencing for Routine Pathogen Surveillance in Public Health: a Population Snapshot of Invasive Staphylococcus aureus in Europe, MBIO, Vol: 7, ISSN: 2150-7511
Hatherell H-A, Didelot X, Pollock SL, et al., 2016, Declaring a tuberculosis outbreak over with genomic epidemiology, MICROBIAL GENOMICS, Vol: 2, ISSN: 2057-5858
Kendall ML, Boyd M, Colijn C, 2016, phyloTop
Tools for calculating and viewing topological properties of phylogenetic trees.
Hatherell H-A, Colijn C, Stagg HR, et al., 2016, Interpreting whole genome sequencing for investigating tuberculosis transmission: a systematic review, BMC MEDICINE, Vol: 14, ISSN: 1741-7015
Plazzotta G, Kwan C, Boyd M, et al., 2016, Effects of memory on the shapes of simple outbreak trees, SCIENTIFIC REPORTS, Vol: 6, ISSN: 2045-2322
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