Amina Shabbeer

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.

A conformal Bayesian network for classification of Mycobacterium tuberculosis complex lineages

BackgroundWe present a novel conformal Bayesian network (CBN) to classify strains of Mycobacterium tuberculosis Complex (MTBC) into six major genetic lineages based on two high-throuput biomarkers: mycobacterial interspersed repetitive units (MIRU) and spacer oligonucleotide typing (spoligotyping). MTBC is the causative agent of tuberculosis (TB), which remains one of the leading causes of disease and morbidity world-wide. DNA fingerprinting methods such as MIRU and spoligotyping are key components in the control and tracking of modern TB.ResultsCBN is designed to exploit background knowledge about MTBC biomarkers. It can be trained on large historical TB databases of various subsets of MTBC biomarkers. During TB control efforts not all biomarkers may be available. So, CBN is designed to predict the major lineage of isolates genotyped by any combination of the PCR-based typing methods: spoligotyping and MIRU typing. CBN achieves high accuracy on three large MTBC collections consisting of over 34,737 isolates genotyped by different combinations of spoligotypes, 12 loci of MIRU, and 24 loci of MIRU. CBN captures distinct MIRU and spoligotype signatures associated with each lineage, explaining its excellent performance. Visualization of MIRU and spoligotype signatures yields insight into both how the model works and the genetic diversity of MTBC.ConclusionsCBN conforms to the available PCR-based biological markers and achieves high performance in identifying major lineages of MTBC. The method can be readily extended as new biomarkers are introduced for TB tracking and control. An online tool (http://www.cs.rpi.edu/~bennek/tbinsight/tblineage) makes the CBN model available for TB control and research efforts.

Online knowledge-based support vector machines

Prior knowledge, in the form of simple advice rules, can greatly speed up convergence in learning algorithms. Online learning methods predict the label of the current point and then receive the correct label (and learn from that information). The goal of this work is to update the hypothesis taking into account not just the label feedback, but also the prior knowledge, in the form of soft polyhedral advice, so as to make increasingly accurate predictions on subsequent examples. Advice helps speed up and bias learning so that generalization can be obtained with less data. Our passive-aggressive approach updates the hypothesis using a hybrid loss that takes into account the margins of both the hypothesis and the advice on the current point. Encouraging computational results and loss bounds are provided.

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.

Determination of Major Lineages of Mycobacterium tuberculosis Complex Using Mycobacterial Interspersed Repetitive Units

Abstract—We present a novel Bayesian network (BN) to classify strains of Mycobacterium tuberculosis Complex (MTBC) into six major genetic lineages using mycobacterial interspersed repetitive units (MIRUs), a high-throughput biomarker. MTBC is the causative agent of tuberculosis (TB), which remains one of the leading causes of disease and morbidity world-wide. DNA fingerprinting methods such as MIRU are key components of modern TB control and tracking. The BN achieves high accuracy on four large MTBC genotype collections consisting of over 4700 distinct 12-loci MIRU genotypes. The BN captures distinct MIRU signatures associated with each lineage, explaining the excellent performance of the BN. The errors in the BN support the need for additional biomarkers such as the expanded 24-loci MIRU used in CDC genotyping labs since May 2009. The conditional independence assumption of each locus given the lineage makes the BN easily extensible to additional MIRU loci and other biomarkers.

Predicting Mycobacterium tuberculosis Complex Clades Using Knowledge-Based Bayesian Networks

We develop a novel approach for incorporating expert rules into Bayesian networks for classification of Mycobacterium tuberculosis complex (MTBC) clades. The proposed knowledge-based Bayesian network (KBBN) treats sets of expert rules as prior distributions on the classes. Unlike prior knowledge-based support vector machine approaches which require rules expressed as polyhedral sets, KBBN directly incorporates the rules without any modification. KBBN uses data to refine rule-based classifiers when the rule set is incomplete or ambiguous. We develop a predictive KBBN model for 69 MTBC clades found in the SITVIT international collection. We validate the approach using two testbeds that model knowledge of the MTBC obtained from two different experts and large DNA fingerprint databases to predict MTBC genetic clades and sublineages. These models represent strains of MTBC using high-throughput biomarkers called spacer oligonucleotide types (spoligotypes), since these are routinely gathered from MTBC isolates of tuberculosis (TB) patients. Results show that incorporating rules into problems can drastically increase classification accuracy if data alone are insufficient. The SITVIT KBBN is publicly available for use on the World Wide Web.

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.