Cagri Ozcaglar

TB-Lineage: an online tool for classification and analysis of strains of Mycobacterium tuberculosis complex.

This paper formulates a set of rules to classify genotypes of the Mycobacterium tuberculosis complex (MTBC) into major lineages using spoligotypes and MIRU-VNTR results. The rules synthesize prior literature that characterizes lineages by spacer deletions and variations in the number of repeats seen at locus MIRU24 (alias VNTR2687). A tool that efficiently and accurately implements this rule base is now freely available at http://tbinsight.cs.rpi.edu/run_tb_lineage.html. When MIRU24 data is not available, the system utilizes predictions made by a Naïve Bayes classifier based on spoligotype data. This website also provides a tool to generate spoligoforests in order to visualize the genetic diversity and relatedness of genotypes and their associated lineages. A detailed analysis of the application of these tools on a dataset collected by the CDC consisting of 3198 distinct spoligotypes and 5430 distinct MIRU-VNTR types from 37,066 clinical isolates is presented. The tools were also tested on four other independent datasets. The accuracy of automated classification using both spoligotypes and MIRU24 is >99%, and using spoligotypes alone is >95%. This online rule-based classification technique in conjunction with genotype visualization provides a practical tool that supports surveillance of TB transmission trends and molecular epidemiological studies.

Epidemiological models of Mycobacterium tuberculosis complex infections

The resurgence of tuberculosis in the 1990s and the emergence of drug-resistant tuberculosis in the first decade of the 21st century increased the importance of epidemiological models for the disease. Due to slow progression of tuberculosis, the transmission dynamics and its long-term effects can often be better observed and predicted using simulations of epidemiological models. This study provides a review of earlier study on modeling different aspects of tuberculosis dynamics. The models simulate tuberculosis transmission dynamics, treatment, drug resistance, control strategies for increasing compliance to treatment, HIV/TB co-infection, and patient groups. The models are based on various mathematical systems, such as systems of ordinary differential equations, simulation models, and Markov Chain Monte Carlo methods. The inferences from the models are justified by case studies and statistical analysis of TB patient datasets.

Web tools for molecular epidemiology of tuberculosis

In this study we explore publicly available web tools designed to use molecular epidemiological data to extract information that can be employed for the effective tracking and control of tuberculosis (TB). The application of molecular methods for the epidemiology of TB complement traditional approaches used in public health. DNA fingerprinting methods are now routinely employed in TB surveillance programs and are primarily used to detect recent transmissions and in outbreak investigations. Here we present web tools that facilitate systematic analysis of Mycobacterium tuberculosis complex (MTBC) genotype information and provide a view of the genetic diversity in the MTBC population. These tools help answer questions about the characteristics of MTBC strains, such as their pathogenicity, virulence, immunogenicity, transmissibility, drug-resistance profiles and host-pathogen associativity. They provide an integrated platform for researchers to use molecular epidemiological data to address current challenges in the understanding of TB dynamics and the characteristics of MTBC.

Sublineage structure analysis of Mycobacterium tuberculosis complex strains using multiple-biomarker tensors

BackgroundStrains of Mycobacterium tuberculosis complex (MTBC) can be classified into major lineages based on their genotype. Further subdivision of major lineages into sublineages requires multiple biomarkers along with methods to combine and analyze multiple sources of information in one unsupervised learning model. Typically, spacer oligonucleotide type (spoligotype) and mycobacterial interspersed repetitive units (MIRU) are used for TB genotyping and surveillance. Here, we examine the sublineage structure of MTBC strains with multiple biomarkers simultaneously, by employing a tensor clustering framework (TCF) on multiple-biomarker tensors.ResultsSimultaneous analysis of the spoligotype and MIRU type of strains using TCF on multiple-biomarker tensors leads to coherent sublineages of major lineages with clear and distinctive spoligotype and MIRU signatures. Comparison of tensor sublineages with SpolDB4 families either supports tensor sublineages, or suggests subdivision or merging of SpolDB4 families. High prediction accuracy of major lineage classification with supervised tensor learning on multiple-biomarker tensors validates our unsupervised analysis of sublineages on multiple-biomarker tensors.ConclusionsTCF on multiple-biomarker tensors achieves simultaneous analysis of multiple biomarkers and suggest a new putative sublineage structure for each major lineage. Analysis of multiple-biomarker tensors gives insight into the sublineage structure of MTBC at the genomic level.

Visualization of tuberculosis patient and Mycobacterium tuberculosis complex genotype data via host-pathogen maps

DNA fingerprints of Mycobacterium tuberculosis complex bacteria (MTBC) are routinely gathered from tuberculosis (TB) patient isolates for all TB patients in the United States to support TB tracking and control efforts, but few tools are available for visualizing and discovering host-pathogen relationships. We present a new visualization approach, host-pathogen maps, for simultaneously examining MTBC strains genotyped by multiple DNA fingerprinting methods such as spoligotyping and restriction fragment length polymorphisms (RFLP) typing along with associated patient surveillance data. The host-pathogen maps are dynamically coupled with spoligoforests or other phylogenetic tree approaches to allow easy navigation within the pathogen genotyping space. Visualization of New York State and New York City (NYC) TB patient data from 2001–2007 is used to illustrate how host-pathogen maps can be used to rapidly identify potential instances of uncontrolled spread of tuberculosis versus disease resulting from latent reactivation of prior infection, a critical distinction in tuberculosis control. Host-pathogen maps also reveal trends and anomalies in the relationships between patient groups and MTBC genetic lineages which can provide critical clues in epidemiology and contact investigations of TB.

Knowledge-based Bayesian network for the classification of Mycobacterium tuberculosis complex sublineages

We develop a novel knowledge-based Bayesian network (KBBN) that models our knowledge of the Mycobacterium tuberculosis complex (MTBC) obtained from expert-defined rules and large DNA fingerprint databases to classify strains of MTBC into fifty-one genetic sublineages. The model uses two high-throughput biomarkers: spacer oligonucleotide types (spoligotypes) and mycobacterial interspersed repetitive units (MIRU) types to represent strains of MTBC, since these are routinely gathered from MTBC isolates of tuberculosis (TB) patients. KBBN provides an elegant and simple way to incorporate existing widely accepted visual rules for MTBC sublineages into a classifier designed to capture known properties of the MTBC biomarkers. Unlike prior knowledge-based SVM approaches which require rules expressed as polyhedral sets, KBBN directly incorporates the rules without any modification. Computational results show that KBBN achieves much higher accuracy than methods based purely on rules, and than Bayesian networks trained on biomarker data alone.

Inferred Spoligoforest Topology Unravels Spatially Bimodal Distribution of Mutations in the DR Region

Biomarkers of Mycobacterium tuberculosis complex (MTBC) mutate over time. Among the biomarkers of MTBC, spacer oligonucleotide type (spoligotype) and mycobacterium interspersed repetitive unit (MIRU) patterns are commonly used to genotype clinical MTBC strains. In this study, we present an evolution model of spoligotype rearrangements using MIRU patterns to disambiguate the ancestors of spoligotypes. We use a large patient dataset from the United States Centers for Disease Control and Prevention (CDC) to generate this model. Based on the contiguous deletion assumption and rare observation of convergent evolution, we first generate the most parsimonious forest of spoligotypes, called a spoligoforest, using three genetic distance measures. An analysis of topological attributes of the spoligoforest and number of variations at the direct repeat (DR) locus of each strain reveals interesting properties of deletions in the DR region. First, we compare our mutation model to existing mutation models of spoligotypes and find that our mutation model produces as many within-lineage mutation events as other models, with slightly higher segregation accuracy. Second, based on our mutation model, the number of descendant spoligotypes follows a power law distribution. Third, contrary to prior studies, the power law distribution does not plausibly fit to the mutation length frequency. Moreover, we find that the total number of mutation events at consecutive spacers follows a spatially bimodal distribution. The two modes are spacers 13 and 40, which are hotspots for chromosomal rearrangements, and the change point is spacer 34, which is absent in most MTBC strains. Based on this observation, we built two alternative models for mutation length frequency: the Starting Point Model (SPM) and the Longest Block Model (LBM). Both models are plausibly good fits to the mutation length frequency distribution, as verified by the goodness-of-fit test. We also apply SPM and LBM to a dataset from Institut Pasteur de Guadeloupe and verify that these models hold for different strain datasets.

Data-Driven Insights into Deletions of Mycobacterium tuberculosis Complex Chromosomal DR Region Using Spoligoforests

Biomarkers of Mycobacterium tuberculosis complex (MTBC) mutate over time. Among the biomarkers of MTBC, spacer oligonucleotide type (spoligotype) and Mycobacterium Interspersed Repetitive Unit (MIRU) patterns are commonly used to genotype clinical MTBC strains. In this study, we present an evolution model of spoligotype rearrangements using MIRU patterns to disambiguate the ancestors of spoligotypes, in a large patient dataset from the United States Centers for Disease Control and Prevention (CDC). Based on the contiguous deletion assumption and rare observation of convergent evolution, we first generate the most parsimonious forest of spoligotypes, called a spoligo forest, using three genetic distance measures. An analysis of topological attributes of the spoligo forest and number of variations at the direct repeat (DR) locus of each strain reveals interesting properties of deletions in the DR region. First, we compare our mutation model to existing mutation models of spoligotypes and find that our mutation model produces as many within-lineage mutation events as other models, with slightly higher segregation accuracy. Second, based on our mutation model, the number of descendant spoligotypes follows a power law distribution. Third, contrary to prior studies, the power law distribution does not plausibly fit to the mutation length frequency. Finally, the total number of mutation events at consecutive DR loci follows a bimodal distribution, which results in accumulation of shorter deletions in the DR region. The two modes are spacers 13 and 40, which are hotspots for chromosomal rearrangements. The change point in the bimodal distribution is spacer 34, which is absent in most MTBC strains. This bimodal separation results in accumulation of shorter deletions, which explains why a power law distribution is not a plausible fit to the mutation length frequency.

Examining the sublineage structure of Mycobacterium tuberculosis complex strains with multiple-biomarker tensors

Strains of the Mycobacterium tuberculosis complex (MTBC) can be classified into coherent lineages of similar traits based on their genotype. We present a tensor clustering framework to group MTBC strains into sublineages of the known major lineages based on two biomarkers: spacer oligonucleotide type (spoligotype) and mycobacterial interspersed repetitive units (MIRU). We represent genotype information of MTBC strains in a high-dimensional array in order to include information about spoligotype, MIRU, and their coexistence using multiple-biomarker tensors. We use multiway models to transform this multidimensional data about the MTBC strains into two-dimensional arrays and use the resulting score vectors in a stable partitive clustering algorithm to classify MTBC strains into sublineages. We validate clusterings using cluster stability and accuracy measures, and find stabilities of each cluster. Based on validated clustering results, we present a sublineage structure of MTBC strains and compare it to the sublineage structures of SpolDB4 and MIRU-VNTRplus.

Preserving proximity relations and minimizing edge-crossings in high dimensional graph visualizations

We propose a novel approach to drawing graphs that simultaneously optimizes two criteria (i) preserving proximity relations as measured by some embedding objective, and (ii) minimizing edge-crossings, to create a clear representation of the underlying graph structure. Frequently, the nodes of the graph represent objects that have their own intrinsic properties with associated distances or similarity measures. In particular, we investigate graphing of spoligoforests to visualize the genetic relatedness between strains of the Mycobacterium tuberculosis complex using multiple genetic markers. It is often desirable, that drawings of such graphs map nodes from high-dimensional feature space to low-dimensional vectors that preserve these pairwise distances. This desired quality is frequently expressed as a function of the embedding and then optimized, eg. Multidimensional Scaling (MDS), the goal is to minimize the difference between the actual distances and Euclidean distances between all nodes in the embedding.