New publication: Assessing Drug Susceptibility in Tuberculosis Philip Fowler, 28th September 201829th September 2018 A paper was published in the New England Journal of Medicine earlier this week by the CRyPTIC project, of which I am part, with help from the 100,000 genomes project. It demonstrates how whole genome sequencing can be used to accurately predict drug susceptibility for the four first-line anti-tubercular drugs (isoniazid, rifampicin, pyrazinamide and ethambutol) and, crucially, how the accuracy only degrades slowly as the local prevalence of resistance increases. All the 10,000 samples had qualitative drug susceptibility profiles gathered using the MGIT system which simply reports whether a sample is resistant or sensitive to a drug. CRyPTIC is currently collecting >30,000 samples using a 96-well plate containing 14 different drugs and therefore will be reporting quantitative data (i.e. minimum inhibitory concentrations). So, whilst this is a good first step, I expect the project to produce a wide range of even more exciting studies in the next few years. Since our ability to draw inferences from correlations between the genetic and phenotypic data depend critically on minimising the errors in the data, I have been heavily involved developing methods to allow us to quality control the measurements made in the different consortium laboratories. These are AMyGDA. This is software that automatically analyses photographs of the 96 well plates CRyPTIC is using. You can read more here. A paper is accepted and I’ll update this post when I have a link. BashTheBug. This is a Citizen Science project hosted on the Zooniverse where anyone can classify a few (or a thousand) images taken from the 96 well plates. These independent sets of measurements are then combined with those collected in the laboratories and a consensus is reached, hopefully minimising errors, improving signal to noise and reducing therefore the number of samples we need before we can start e.g. identifying new genes and genetic variants that can confer resistance to anti-TB drugs. In short, watch this space! Share this: Click to share on X (Opens in new window) X Click to share on Bluesky (Opens in new window) Bluesky Click to email a link to a friend (Opens in new window) Email Click to share on LinkedIn (Opens in new window) LinkedIn Click to share on Mastodon (Opens in new window) Mastodon Related antimicrobial resistance citizen science clinical microbiology publication tuberculosis
antimicrobial resistance BioExcel Alchemical Free Energy workshop 17th June 2019 Last month I was invited to give a talk on using alchemical free energy methods… Share this: Click to share on X (Opens in new window) X Click to share on Bluesky (Opens in new window) Bluesky Click to email a link to a friend (Opens in new window) Email Click to share on LinkedIn (Opens in new window) LinkedIn Click to share on Mastodon (Opens in new window) Mastodon Read More
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: Click to share on X (Opens in new window) X Click to share on Bluesky (Opens in new window) Bluesky Click to email a link to a friend (Opens in new window) Email Click to share on LinkedIn (Opens in new window) LinkedIn Click to share on Mastodon (Opens in new window) Mastodon Read More
publication New publication: Gating Topology of the Proton-Coupled Oligopeptide Symporters 3rd February 2015