Abstract
BackgroundMultidrug-resistant Mycobacterium tuberculosis (Mtb) is a significant global public health threat. Genotypic resistance prediction from Mtb DNA sequences offers an alternative to laboratory-based drug-susceptibility testing. User-friendly and accurate resistance prediction tools are needed to enable public health and clinical practitioners to rapidly diagnose resistance and inform treatment regimens.ResultsWe present Translational Genomics platform for Tuberculosis (GenTB), a free and open web-based application to predict antibiotic resistance from next-generation sequence data. The user can choose between two potential predictors, a Random Forest (RF) classifier and a Wide and Deep Neural Network (WDNN) to predict phenotypic resistance to 13 and 10 anti-tuberculosis drugs, respectively. We benchmark GenTB’s predictive performance along with leading TB resistance prediction tools (Mykrobe and TB-Profiler) using a ground truth dataset of 20,408 isolates with laboratory-based drug susceptibility data. All four tools reliably predicted resistance to first-line tuberculosis drugs but had varying performance for second-line drugs. The mean sensitivities for GenTB-RF and GenTB-WDNN across the nine shared drugs were 77.6% (95% CI 76.6–78.5%) and 75.4% (95% CI 74.5–76.4%), respectively, and marginally higher than the sensitivities of TB-Profiler at 74.4% (95% CI 73.4–75.3%) and Mykrobe at 71.9% (95% CI 70.9–72.9%). The higher sensitivities were at an expense of ≤ 1.5% lower specificity: Mykrobe 97.6% (95% CI 97.5–97.7%), TB-Profiler 96.9% (95% CI 96.7 to 97.0%), GenTB-WDNN 96.2% (95% CI 96.0 to 96.4%), and GenTB-RF 96.1% (95% CI 96.0 to 96.3%). Averaged across the four tools, genotypic resistance sensitivity was 11% and 9% lower for isoniazid and rifampicin respectively, on isolates sequenced at low depth (< 10× across 95% of the genome) emphasizing the need to quality control input sequence data before prediction. We discuss differences between tools in reporting results to the user including variants underlying the resistance calls and any novel or indeterminate variantsConclusionsGenTB is an easy-to-use online tool to rapidly and accurately predict resistance to anti-tuberculosis drugs. GenTB can be accessed online at https://gentb.hms.harvard.edu, and the source code is available at https://github.com/farhat-lab/gentb-site.
Highlights
Multidrug-resistant Mycobacterium tuberculosis (Mtb) is a significant global public health threat
We evaluated 1,000 probability thresholds per drug to call resistance or susceptibility for Genomics platform for Tuberculosis (GenTB)-Random Forest (RF) while using the GenTB-Wide and Deep Neural Network (WDNN) thresholds described in Chen et al [17] (Additional file 2: Fig. S2 and Additional file 2: Fig. S3)
A user-friendly application to analyze M. tuberculosis sequencing data GenTB was developed as a free and benchmarked online application to help public health and clinical practitioners deconvolute the complexity of M. tuberculosis whole-genome sequencing (WGS) data
Summary
Multidrug-resistant Mycobacterium tuberculosis (Mtb) is a significant global public health threat. Genotypic resistance prediction from Mtb DNA sequences offers an alternative to laboratory-based drugsusceptibility testing. User-friendly and accurate resistance prediction tools are needed to enable public health and clinical practitioners to rapidly diagnose resistance and inform treatment regimens. Antimicrobial resistance is conventionally determined by in vitro drug susceptibility tests (DST) on solid or liquid antibiotic-containing culture, which uses drug-specific testing breakpoints (“critical concentration”) to classify the infecting strain into drugsusceptible or drug-resistant [2]. Molecular methods have emerged as rapid resistance prediction alternatives to complement and speed up traditional DST, leveraging known and reliable genotype-phenotype relationships between variants in the M. tuberculosis genome and in vitro drug resistance [5]
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