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Bridging the TB data gap: in silico extraction of rifampicin-resistant tuberculosis diagnostic test results from whole genome sequence dataNg, K.C.S., Ngabonziza, J.C.S., Lempens, P., de Jong, B.C., van Leth, F., Meehan, Conor J. 05 November 2019 (has links)
Yes / Background: Mycobacterium tuberculosis rapid diagnostic tests (RDTs) are widely
employed in routine laboratories and national surveys for detection of rifampicinresistant (RR)-TB. However, as next-generation sequencing technologies have
become more commonplace in research and surveillance programs, RDTs are being
increasingly complemented by whole genome sequencing (WGS). While comparison
between RDTs is difficult, all RDT results can be derived from WGS data. This
can facilitate continuous analysis of RR-TB burden regardless of the data generation
technology employed. By converting WGS to RDT results, we enable comparison of
data with different formats and sources particularly for low- and middle-income
high TB-burden countries that employ different diagnostic algorithms for drug
resistance surveys. This allows national TB control programs (NTPs) and
epidemiologists to utilize all available data in the setting for improved RR-TB
surveillance.
Methods: We developed the Python-based MycTB Genome to Test (MTBGT) tool
that transforms WGS-derived data into laboratory-validated results of the primary
RDTs—Xpert MTB/RIF, XpertMTB/RIF Ultra, GenoType MDRTBplus v2.0, and
GenoscholarNTM+MDRTB II. The tool was validated through RDT results of
RR-TB strains with diverse resistance patterns and geographic origins and applied on
routine-derived WGS data.
Results: The MTBGT tool correctly transformed the single nucleotide polymorphism
(SNP) data into the RDT results and generated tabulated frequencies of the RDT
probes as well as rifampicin-susceptible cases. The tool supplemented the RDT
probe reactions output with the RR-conferring mutation based on identified SNPs.
The MTBGT tool facilitated continuous analysis of RR-TB and Xpert probe reactions
from different platforms and collection periods in Rwanda.
Conclusion: Overall, the MTBGT tool allows low- and middle-income countries to
make sense of the increasingly generated WGS in light of the readily available RDT. / Erasmus Mundus Joint Doctorate Fellowship grant 2016- 1346.
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