{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T13:36:55Z","timestamp":1786541815487,"version":"build-2736575974"},"reference-count":68,"publisher":"American Society for Microbiology","issue":"8","license":[{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.asm.org\/non-commercial-tdm-license"}],"content-domain":{"domain":["journals.asm.org"],"crossmark-restriction":true},"short-container-title":["J Clin Microbiol"],"published-print":{"date-parts":[[2026,8,12]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title\/>\n                    <jats:p>\n                      Whole-genome sequencing (WGS) accelerates drug-susceptibility testing (DST) in\n                      <jats:italic toggle=\"yes\">Mycobacterium tuberculosis<\/jats:italic>\n                      (Mtb). Open-access software tools have become widely available, but the sources of real-world performance variability remain uncharacterized. We performed a systematic review and meta-analysis of the performance of open-access, independently validated WGS-based DST prediction tools. Bivariate random-effects meta-analysis was performed for six maintained tools (TBProfiler, Mykrobe, PhyResSE, MTBseq, GenTB, and SAM-TB). Bivariate meta-regression identified covariates associated with performance variation. Thirty-nine studies comprising 144,623 genomes were included. For the two most extensively validated tools, TBProfiler and Mykrobe, pooled rifampicin sensitivity was 95.4% (95% CI: 93.5\u201396.7) and 93.7% (92.0\u201395.1), with a specificity of 97.3% (95.7\u201398.3) and 97.0% (94.8\u201398.3), respectively. For isoniazid, the sensitivity was 92.0% (90.4\u201393.3) and 88.2% (85.5\u201390.4) and specificity 97.3% (96.0\u201398.2) and 97.5% (95.8\u201398.5). For ethambutol, the specificity was heterogeneous across tools (86.5%\u201395.4%); for pyrazinamide, the sensitivity varied widely (49.9%\u201380.6%). For fluoroquinolones, both sensitivity and specificity approached 90%, with heterogeneity. For newer agents, data scarcity precluded meaningful assessment. Meta-regression identified rifampicin resistance prevalence as the dominant predictor of decreased specificity across first-line drugs (\u03b2 \u22121.5 to \u22123.6 on logit scale, false discovery rate [FDR] q &lt; 0.05), while lineage composition effects were small and confounded. Current open-access WGS prediction tools achieve clinically useful accuracy as rule-out tests for rifampicin, isoniazid, and fluoroquinolone resistance. Predictive performance for second-line drugs is limited by data scarcity. Methodological limitations, including lineage bias, data leakage, and selective sampling, may undermine the tools\u2019 generalizability across diverse global tuberculosis populations.\n                    <\/jats:p>\n                    <jats:sec>\n                      <jats:title>IMPORTANCE<\/jats:title>\n                      <jats:p>\n                        Tuberculosis remains a leading infectious disease killer worldwide. Whole-genome sequencing (WGS) of\n                        <jats:italic toggle=\"yes\">Mycobacterium tuberculosis<\/jats:italic>\n                        offers the potential to rapidly predict drug resistance as a one-stop test, but the accuracy of the software tools used to interpret sequencing results has been inconsistently reported. This meta-analysis leverages the heterogeneity across 39 studies and 144,623 genomes to identify factors that drive inconsistencies in reported performance, providing context-specific guidance for clinical adoption. We show that most tools perform adequately as rule-out tests for resistance to the most important first- and second-line drugs but fall short of specificity targets. Importantly, we identify that the local burden of drug resistance in a study population is the dominant factor driving inconsistencies between reported performance estimates. These findings provide guidance for laboratories considering adopting sequencing-based resistance testing and specify priorities for future tool development and validation.\n                      <\/jats:p>\n                    <\/jats:sec>\n                  <\/jats:sec>","DOI":"10.1128\/jcm.00321-26","type":"journal-article","created":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T14:07:49Z","timestamp":1785161269000},"update-policy":"https:\/\/doi.org\/10.1128\/asmj-crossmark-policy-page","source":"Crossref","is-referenced-by-count":0,"title":["Open-access genomic drug resistance prediction tools for\n                    <i>Mycobacterium tuberculosis<\/i>\n                    : a systematic review and meta-analysis"],"prefix":"10.1128","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7029-2470","authenticated-orcid":true,"given":"Klaas","family":"Dewaele","sequence":"first","affiliation":[{"name":"Unit of Mycobacteriology, Institute of Tropical Medicine","place":["Antwerp, Belgium"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christelle","family":"Jouego","sequence":"additional","affiliation":[{"name":"Unit of HIV and Tuberculosis, Institute of Tropical Medicine","place":["Antwerp, Belgium"]},{"name":"Molecular Diagnostic and Research Group, Universit\u00e9 de Yaound\u00e9","place":["Yaound\u00e9, Cameroon"]},{"id":[{"id":"https:\/\/ror.org\/05f950310","id-type":"ROR","asserted-by":"publisher"}],"name":"Laboratory of Clinical Microbiology, Department of Microbiology, Immunology and Transplantation, KU Leuven","place":["Leuven, Belgium"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adina","family":"Asim","sequence":"additional","affiliation":[{"name":"Department of Biomedical Sciences, Institute of Tropical Medicine","place":["Antwerp, Belgium"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lies","family":"Laenen","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/05f950310","id-type":"ROR","asserted-by":"publisher"}],"name":"Laboratory of Clinical Microbiology, Department of Microbiology, Immunology and Transplantation, KU Leuven","place":["Leuven, Belgium"]},{"name":"Clinical Department of Laboratory Medicine and National Reference Center for Respiratory Pathogens, KU Leuven University Hospitals Leuven","place":["Leuven, Belgium"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Conor","family":"Meehan","sequence":"additional","affiliation":[{"name":"Unit of Mycobacteriology, Institute of Tropical Medicine","place":["Antwerp, Belgium"]},{"id":[{"id":"https:\/\/ror.org\/04xyxjd90","id-type":"ROR","asserted-by":"publisher"}],"name":"Department of Biosciences, Nottingham Trent University","place":["Nottingham, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emmanuel","family":"Andr\u00e9","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/05f950310","id-type":"ROR","asserted-by":"publisher"}],"name":"Laboratory of Clinical Microbiology, Department of Microbiology, Immunology and Transplantation, KU Leuven","place":["Leuven, Belgium"]},{"name":"Clinical Department of Laboratory Medicine and National Reference Center for Respiratory Pathogens, KU Leuven University Hospitals Leuven","place":["Leuven, Belgium"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"235","reference":[{"key":"e_1_3_2_2_2","unstructured":"WHO. 2024. 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