Optimal estimation of transposition rates of insertion sequences for molecular epidemiology

Tanaka, Mark M.; Rosenberg, Noah A.

doi:10.1002/sim.910

Cited by 17 publications

(1 citation statement)

References 31 publications

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“…Such genotypic comparison can translate into meaningful epidemiological inference only if the dynamics of IS6110 evolution are well understood. Therefore, accurate estimation of rates of changes of IS6110 -based genotypes is critical for using these genotypes in epidemiological studies [Tanaka and Rosenberg (2001)].…”

Section: 2mentioning

confidence: 99%

Fitting birth–death processes to panel data with applications to bacterial DNA fingerprinting

Doss¹,

Suchard²,

Holmes³

et al. 2013

Ann. Appl. Stat.

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Continuous-time linear birth–death-immigration (BDI) processes are frequently used in ecology and epidemiology to model stochastic dynamics of the population of interest. In clinical settings, multiple birth–death processes can describe disease trajectories of individual patients, allowing for estimation of the effects of individual covariates on the birth and death rates of the process. Such estimation is usually accomplished by analyzing patient data collected at unevenly spaced time points, referred to as panel data in the biostatistics literature. Fitting linear BDI processes to panel data is a nontrivial optimization problem because birth and death rates can be functions of many parameters related to the covariates of interest. We propose a novel expectation–maximization (EM) algorithm for fitting linear BDI models with covariates to panel data. We derive a closed-form expression for the joint generating function of some of the BDI process statistics and use this generating function to reduce the E-step of the EM algorithm, as well as calculation of the Fisher information, to one-dimensional integration. This analytical technique yields a computationally efficient and robust optimization algorithm that we implemented in an open-source R package. We apply our method to DNA fingerprinting of Mycobacterium tuberculosis, the causative agent of tuberculosis, to study intrapatient time evolution of IS6110 copy number, a genetic marker frequently used during estimation of epidemiological clusters of Mycobacterium tuberculosis infections. Our analysis reveals previously undocumented differences in IS6110 birth–death rates among three major lineages of Mycobacterium tuberculosis, which has important implications for epidemiologists that use IS6110 for DNA fingerprinting of Mycobacterium tuberculosis.

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Section: 2mentioning

confidence: 99%

Fitting birth–death processes to panel data with applications to bacterial DNA fingerprinting

Doss¹,

Suchard²,

Holmes³

et al. 2013

Ann. Appl. Stat.

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The Evolution of Strain Typing in the Mycobacterium tuberculosis Complex

Merker

Kohl

Niemann

et al. 2017

Advances in Experimental Medicine and Biology

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Tuberculosis (TB) is a contagious disease with a complex epidemiology. Therefore, molecular typing (genotyping) of Mycobacterium tuberculosis complex (MTBC) strains is of primary importance to effectively guide outbreak investigations, define transmission dynamics and assist global epidemiological surveillance of the disease. Large-scale genotyping is also needed to get better insights into the biological diversity and the evolution of the pathogen. Thanks to its shorter turnaround and simple numerical nomenclature system, mycobacterial interspersed repetitive unit-variable-number tandem repeat (MIRU-VNTR) typing, based on 24 standardized plus 4 hypervariable loci, optionally combined with spoligotyping, has replaced IS6110 DNA fingerprinting over the last decade as a gold standard among classical strain typing methods for many applications. With the continuous progress and decreasing costs of next-generation sequencing (NGS) technologies, typing based on whole genome sequencing (WGS) is now increasingly performed for near complete exploitation of the available genetic information. However, some important challenges remain such as the lack of standardization of WGS analysis pipelines, the need of databases for sharing WGS data at a global level, and a better understanding of the relevant genomic distances for defining clusters of recent TB transmission in different epidemiological contexts. This chapter provides an overview of the evolution of genotyping methods over the last three decades, which culminated with the development of WGS-based methods. It addresses the relative advantages and limitations of these techniques, indicates current challenges and potential directions for facilitating standardization of WGS-based typing, and provides suggestions on what method to use depending on the specific research question.

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Methods of quantifying and visualising outbreaks of tuberculosis using genotypic information

Tanaka

Francis

2005

Infection, Genetics and Evolution

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Genotypic data from pathogenic isolates are often used to measure the extent of infectious disease transmission. These methods include phylogenetic reconstruction and the evaluation of clustering indices. The first aim of this paper is to critique current methods used to analyse genotypic data from molecular epidemiological studies of tuberculosis. In particular, by not accounting for the mutation rate of markers, errors arise in making inferences about outbreaks based on genotypic information. The second aim is to suggest a new way to represent genotypic data visually, involving graphs and trees. We also discuss some interpretations and modifications of existing indices. Although our focus is tuberculosis, the methods we discuss are generally applicable to any directly transmissible clonal pathogen.

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Optimal estimation of transposition rates of insertion sequences for molecular epidemiology

Cited by 17 publications

References 31 publications

Fitting birth–death processes to panel data with applications to bacterial DNA fingerprinting

Fitting birth–death processes to panel data with applications to bacterial DNA fingerprinting

The Evolution of Strain Typing in the Mycobacterium tuberculosis Complex

Methods of quantifying and visualising outbreaks of tuberculosis using genotypic information

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