MTBGT

MTBGT converts whole-genome sequencing (WGS) data from Mycobacterium tuberculosis into simulated rapid diagnostic test outputs (Xpert MTB/RIF, XpertMTB/RIF Ultra, GenoType MDRTBplus v2.0, and GenoscholarNTM+MDRTB II) to detect and report rifampicin resistance-associated mutations.

Key Features:

• Data transformation: Converts single nucleotide polymorphism (SNP) data derived from WGS into results that mimic established rapid diagnostic tests.
• Supported tests: Produces simulated outputs for Xpert MTB/RIF, XpertMTB/RIF Ultra, GenoType MDRTBplus v2.0, and GenoscholarNTM+MDRTB II.
• Output generation: Generates tabulated frequencies of RDT probe reactions, identifies rifampicin-susceptible cases, and reports specific rifampicin-resistance (RR)-conferring mutations based on identified SNPs.
• Implementation: Implemented as a Python-based application processing WGS-derived SNP data.
• Validation and datasets: Validated on RR-TB strains with diverse resistance patterns and geographic origins and applied to routine-derived WGS datasets.
• Surveillance facilitation: Enables continuous analysis of RR-TB data across different diagnostic platforms and collection periods to support surveillance.

Scientific Applications:

• RR-TB surveillance: Supports continuous monitoring of rifampicin-resistant tuberculosis across diagnostic platforms and time periods.
• Diagnostic evaluation: Allows assessment of the adequacy of Xpert MTB/RIF, XpertMTB/RIF Ultra, GenoType MDRTBplus v2.0, and GenoscholarNTM+MDRTB II in detecting RR-TB.
• Programmatic integration: Integrates WGS-derived mutation data into RDT frameworks for national tuberculosis control programs and epidemiologists.
• Research and routine comparison: Facilitates comparison between periodic surveys, research datasets, and routine diagnostic test results.

Methodology:

Python-based conversion of WGS-derived SNP data into simulated probe reactions for specified RDTs, generation of tabulated probe reaction frequencies, identification of rifampicin-susceptible cases, and annotation of RR-conferring mutations based on identified SNPs.