{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T14:35:32Z","timestamp":1782398132798,"version":"3.54.5"},"reference-count":80,"publisher":"Microbiology Society","issue":"8","license":[{"start":{"date-parts":[[2023,8,8]],"date-time":"2023-08-08T00:00:00Z","timestamp":1691452800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Australian Government Medical Research Future Fund","award":["2020\/MRF1200856"],"award-info":[{"award-number":["2020\/MRF1200856"]}]},{"name":"Australian Government Medical Research Future Fund","award":["2020\/MRF1200856"],"award-info":[{"award-number":["2020\/MRF1200856"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,8,8]]},"abstract":"<jats:p>\n                    Tuberculosis is a global pandemic disease with a rising burden of antimicrobial resistance. As a result, the World Health Organization (WHO) has a goal of enabling universal access to drug susceptibility testing (DST). Given the slowness of and infrastructure requirements for phenotypic DST, whole-genome sequencing, followed by genotype-based prediction of DST, now provides a route to achieving this. Since a central component of genotypic DST is to detect the presence of any known resistance-causing mutations, a natural approach is to use a reference graph that allows encoding of known variation. We have developed DrPRG (Drug resistance Prediction with Reference Graphs) using the bacterial reference graph method Pandora. First, we outline the construction of a\n                    <jats:italic>\n                      <jats:named-content content-type=\"species\">\n                        <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/doi.org\/10.1601\/nm.10886\" xlink:type=\"simple\">Mycobacterium tuberculosis<\/jats:ext-link>\n                      <\/jats:named-content>\n                    <\/jats:italic>\n                    drug resistance reference graph. The graph is built from a global dataset of isolates with varying drug susceptibility profiles, thus capturing common and rare resistance- and susceptible-associated haplotypes. We benchmark DrPRG against the existing graph-based tool Mykrobe and the haplotype-based approach of TBProfiler using 44\u200a709 and 138 publicly available Illumina and Nanopore samples with associated phenotypes. We find that DrPRG has significantly improved sensitivity and specificity for some drugs compared to these tools, with no significant decreases. It uses significantly less computational memory than both tools, and provides significantly faster runtimes, except when runtime is compared to Mykrobe with Nanopore data. We discover and discuss novel insights into resistance-conferring variation for\n                    <jats:italic>\n                      <jats:named-content content-type=\"species\">\n                        <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/doi.org\/10.1601\/nm.10886\" xlink:type=\"simple\">M. tuberculosis<\/jats:ext-link>\n                      <\/jats:named-content>\n                    <\/jats:italic>\n                    \u2013 including deletion of genes\n                    <jats:italic>katG<\/jats:italic>\n                    and\n                    <jats:italic>pncA<\/jats:italic>\n                    \u2013 and suggest mutations that may warrant reclassification as associated with resistance.\n                  <\/jats:p>","DOI":"10.1099\/mgen.0.001081","type":"journal-article","created":{"date-parts":[[2023,8,8]],"date-time":"2023-08-08T12:16:22Z","timestamp":1691496982000},"source":"Crossref","is-referenced-by-count":5,"title":["Drug resistance prediction for Mycobacterium tuberculosis with reference graphs"],"prefix":"10.1099","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3683-6208","authenticated-orcid":false,"given":"Michael B.","family":"Hall","sequence":"first","affiliation":[{"name":"Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, Australia"},{"name":"European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridgeshire, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8976-2762","authenticated-orcid":false,"given":"Leandro","family":"Lima","sequence":"additional","affiliation":[{"name":"European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridgeshire, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4300-455X","authenticated-orcid":false,"given":"Lachlan J. 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