New preprint: Including minor alleles improves fluoroquinolone resistance prediction Philip Fowler, 10th November 202217th November 2022 Fluoroquinolones are used to treat both normal and drug resistant tuberculosis and therefore being able to work out if an infection is resistant or not to fluoroquinolones is very important. Sequencing the genome of an infection is increasingly used to rapidly return which antibiotics could be used to treat a patient with tuberculosis. Genetics-based approaches usually assume that any infection is homogenous which allows the variant caller to assume that any evidence of a minor alleles are due to sequencing error, allowing these to be filtered out. The WHO catalogue of mutations conferring resistance to M. tuberculosis was published in 2021 and includes several mutations in the gyrA gene that confer resistance to both moxifloxacin and levofloxacin. Despite the molecular mechanism being thought to be understood the sensitivity of genetics-based resistance prediction was lower for the fluoroquinolones than rifampicin and isoniazid. In this preprint Alice Brankin uses the large CRyPTIC dataset of M. tuberculosis to show that if two or more reads at a genome position support the existence of a known resistance-conferring mutation in gyrA, then calling that sample resistant improves the sensitivity of moxifloxacin resistance prediction from 85.4% to 94.0%, bringing it into line with rifamipcin and isoniazid. 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 clinical microbiology publication research tuberculosis
publication New Publication: State-Dependent Network Connectivity Determines Gating in a K+ Channel 27th June 2014 In an earlier paper we showed that the closed state of Kir1.1, a important potassium… 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 AMyGDA now available from GitHub 27th January 202027th January 2020 AMyGDA is a python module that analyses photographs of 96-well plates and, by examining each… 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