New publication: WHO catalogue of Mycobacterium tuberculosis resistant mutations Philip Fowler, 28th March 202228th March 2022 The CRyPTIC project collecting over 20,000 clinical samples of TB and for each, sequencing its genome and testing its susceptibility to 13 different antibiotics. A lovely unintended consequence of compiling such a large high-quality dataset is that CRyPTIC was invited to form part of the team that collected data and compiled the first catalogue of resistance-conferring mutations for M. tuberculosis complex published by the World Health Organization in June 2021. This paper, that is just out in Lancet Microbe, describes in more detail the analysis necessary to build the catalogue. 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 paper: a deep learning model that reads MICs from images of 96 well plates 26th May 20251st July 2025 Our paper describing how a convolutional neural network model can determine the minimum inhibitory concentrations… 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
New publication: BashTheBug works! 20th May 202219th July 2022 Yesterday eLife published the first paper from our citizen science project, BashTheBug, which was launched… 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