Desirable features for any antibiotic resistance catalogue Philip Fowler, 31st October 202331st October 2023 In the past few years a growing number of catalogues containing mutations associated with resistance (and susceptibility) to different anti-TB drugs have been published. Some are supplements to papers, some can be found in a version controlled repository and others are a mixture. The publication by the World Health Organisation of their first Catalogue of mutations in Mycobacterium tuberculosis complex and their association with drug resistance in July 2021 has brought the field much needed credibility, however it has also brought to light some problems. I expect most people to agree with the statement that any catalogue must be straightforwardly usable by a public health official with limited bioinformatic training and experience, otherwise it will never be adopted in the countries where it stands to bring the biggest benefit. This naturally leads us to propose that to be unambiguous and easy to understand and deploy a catalogue of mutations should… be with respect to a specified version of a reference genome (currently H37Rv version 3 for M. tuberculosis) be a single object that is readable by both humans and computers contain all the logic necessary to build an antibiogram provide an estimate of the uncertainty underlying each association and, ideally, the evidence supporting the association be versioned to allow for bugs/mistakes to be corrected following publication ideally, be provided in a standard format so that the performance of different catalogues can be directly compared Unfortunately no published catalogue, including the first WHO catalogue (which I played a small part in collating and creating so must take some of the blame), yet meet the majority, let alone all, of these criteria. These criteria first saw the light of day at a talk I gave at the ESM Annual Meeting in Tirana, Albania in June 2023. I post the list here so we can revisit it when future catalogues are published and see how they fare. Share this: Share on X (Opens in new window) X Share on Bluesky (Opens in new window) Bluesky Email a link to a friend (Opens in new window) Email Share on LinkedIn (Opens in new window) LinkedIn Share on Mastodon (Opens in new window) Mastodon Related antimicrobial resistance research tuberculosis
antimicrobial resistance New preprint: Predicting antibiotic resistance in complex protein targets 4th January 20224th January 2022 In this preprint, which Alice has been working on for several years, we show how… Share this: Share on X (Opens in new window) X Share on Bluesky (Opens in new window) Bluesky Email a link to a friend (Opens in new window) Email Share on LinkedIn (Opens in new window) LinkedIn Share on Mastodon (Opens in new window) Mastodon Read More
antimicrobial resistance Successful NIHR grant 29th June 20185th August 2018 Last year I coordinated a bid to the NIHR for capital to improve our research… Share this: Share on X (Opens in new window) X Share on Bluesky (Opens in new window) Bluesky Email a link to a friend (Opens in new window) Email Share on LinkedIn (Opens in new window) LinkedIn Share on Mastodon (Opens in new window) Mastodon Read More
antimicrobial resistance New preprint: Predicting pyrazinamide resistance in M. tuberculosis using a graph convolutional network 29th October 202530th October 2025 In previous work we’ve used “traditional” machine-learning approaches, like XGBoost, to learn and therefore predict… Share this: Share on X (Opens in new window) X Share on Bluesky (Opens in new window) Bluesky Email a link to a friend (Opens in new window) Email Share on LinkedIn (Opens in new window) LinkedIn Share on Mastodon (Opens in new window) Mastodon Read More