Bridging the TB data gap : in silico extraction of 1 rifampicin-resistant tuberculosis diagnostic test 2 results from whole genome sequence data 3 4 5

38 Background. Mycobacterium tuberculosis rapid diagnostic tests (RDTs) are widely employed 39 in routine laboratories and national surveys for detection of rifampicin-resistant (RR)-TB. 40 However, as next generation sequencing technologies have become more commonplace in 41 research and surveillance programs, RDTs are being increasingly complemented by whole 42 genome sequencing (WGS). While comparison between RDTs is difficult, all RDT results can be 43 derived from WGS data. This can facilitate longitudinal analysis of RR-TB burden regardless of 44 the data generation technology employed. By converting WGS to RDT results, we enable 45 comparison of data with different formats and sources particularly for countries that employ 46 different diagnostic algorithms for drug resistance surveys. This allows national TB control 47 programs (NTPs) and epidemiologists to exploit all available data for improved RR-TB 48 surveillance. 49 Methods. We developed the Python-based TB Genome to Test (TBGT) tool that transforms 50 WGS-derived data into laboratory-validated results of the primary RDTs – Xpert MTB/RIF, 51 XpertMTB/RIF Ultra, Genotype MDRTBplus v2.0, and Genoscholar NTM + MDRTB II. The 52 tool was validated through RDT results of RR-TB strains with diverse resistance patterns and 53 geographic origins and applied on routine-derived WGS data. 54 Results. The TBGT tool correctly transformed the SNP data into the RDT results and generated 55 tabulated frequencies of the RDT probes as well as rifampicin susceptible cases. The tool 56 supplemented the RDT probe reactions output with the RR-conferring mutation based on 57 identified SNPs. 58 Conclusion. Overall, the TBGT tool allows the NTP to assess whether currently implemented 59 RDTs adequately detect RR-TB in their setting. With its feature to transform WGS to RDT 60 results and enable longitudinal RR-TB data analysis, the TBGT tool may bridge the gap between 61 and among data from periodic surveys, continuous surveillance, research, and routine tests, and 62 may be integrated within the existing national connectivity platform for use by the NTP and 63 epidemiologists to improve setting-specific RR-TB control. The TBGT source code and 64 accompanying documentation is available at https://github.com/KamelaNg/TBGT. 65