Publications

Discordance between different bioinformatic methods for identifying resistance genes from short-read genomic data, with a focus on Escherichia coli.

Davies TJ. et al, (2023), Microbial genomics, 9

Reply: Epidemiological cut-off values for a 96-well broth microdilution plate for high-throughput research antibiotic susceptibility testing ofM. tuberculosis

(2023), European Respiratory Journal, 61, 2300426 - 2300426

Inclusion of minor alleles improves catalogue-based prediction of fluoroquinolone resistance in Mycobacterium tuberculosis.

Brankin AE. and Fowler PW., (2023), JAC Antimicrob Resist, 5

Compensatory mutations are associated with increasedin vitrogrowth in resistant clinical samples ofMycobacterium tuberculosis

Brunner V. and Fowler P., (2023)

Epidemiological cut-off values for a 96-well broth microdilution plate for high-throughput research antibiotic susceptibility testing ofM. tuberculosis

(2022), European Respiratory Journal, 60, 2200239 - 2200239

Predicting antibiotic resistance in complex protein targets using alchemical free energy methods

Brankin AE. and Fowler PW., (2022), Journal of Computational Chemistry

Genome-wide association studies of global Mycobacterium tuberculosis resistance to 13 antimicrobials in 10,228 genomes identify new resistance mechanisms

(2022), PLOS Biology, 20, e3001755 - e3001755

A data compendium associating the genomes of 12,289 Mycobacterium tuberculosis isolates with quantitative resistance phenotypes to 13 antibiotics.

The CRyPTIC Consortium None., (2022), PLoS biology, 20

ReadItAndKeep: rapid decontamination of SARS-CoV-2 sequencing reads

Hunt M. et al, (2022), Bioinformatics

An Observational Cohort Study on the Incidence of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection and B.1.1.7 Variant Infection in Healthcare Workers by Antibody and Vaccination Status

Lumley SF. et al, (2022), Clinical Infectious Diseases, 74, 1208 - 1219

The 2021 WHO catalogue of Mycobacterium tuberculosis complex mutations associated with drug resistance: a genotypic analysis

Walker TM. et al, (2022), The Lancet Microbe, 3, e265 - e273

ReadItAndKeep: rapid decontamination of SARS-CoV-2 sequencing reads

Hunt M. et al, (2022)

Rapid turnaround multiplex sequencing of SARS-CoV-2: comparing tiling amplicon protocol performance

Constantinides B. et al, (2022)

Discordance between different bioinformatic methods for identifying resistance genes from short-read genomic data, with a focus onEscherichia coli

Davies TJ. et al, (2021)

The Duration, Dynamics, and Determinants of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Antibody Responses in Individual Healthcare Workers

Lumley SF. et al, (2021), Clinical Infectious Diseases, 73, e699 - e709

BashTheBug: a crowd of volunteers reproducibly and accurately measure the minimum inhibitory concentrations of 13 antitubercular drugs from photographs of 96-well broth microdilution plates

Fowler PW. et al, (2021)

Deciphering Bedaquiline and Clofazimine Resistance in Tuberculosis: An Evolutionary Medicine Approach

Sonnenkalb L. et al, (2021)

An observational cohort study on the incidence of SARS-CoV-2 infection and B.1.1.7 variant infection in healthcare workers by antibody and vaccination status

Lumley SF. et al, (2021)

Antibody Status and Incidence of SARS-CoV-2 Infection in Health Care Workers

Lumley SF. et al, (2021), New England Journal of Medicine, 384, 533 - 540

Differential occupational risks to healthcare workers from SARS-CoV-2: A prospective observational study

Eyre DW. et al, (2020)

Antibiotic resistance prediction for Mycobacterium tuberculosis from genome sequence data with Mykrobe

Hunt M. et al, (2019), Wellcome Open Research, 4, 191 - 191

Prediction of Pyrazinamide Resistance in <i>Mycobacterium Tuberculosis</i> Using Structure-Based Machine Learning Approaches

Carter JJ. et al, (2019)

Prediction of pyrazinamide resistance inMycobacterium tuberculosisusing structure-based machine learning approaches

Carter JJ. et al, (2019)

How Membrane Curvature Drives the Up-Concentration of N-Ras Proteins to Ordered Lipid Domains : Correlation of In Vivo and In Vitro Experiments with Mean Field Theory Calculations and Coarse Grain Simulations

Hatzakis NS. et al, (2014), BIOPHYSICAL JOURNAL, 106, 713A - 713A

A Structural Model of a Kir Channel in the Open State Derived from Mutagenic Scanning of the Pore Gating Energetics

Bollepalli MK. et al, (2010), Biophysical Journal, 98, 697a - 697a

Gating Sensitive Residues In The Pore Of An Inwardly Rectifying Potassium (Kir) Channel

Bollepalli MK. et al, (2009), BIOPHYSICAL JOURNAL, 96, 462A - 462A

STIMD: A grid-based method for accelerating the calculation of differences in binding free energies and its application to the binding of peptides to Src SH2 protein domains

Fowler PW. et al, (2006), ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 231

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Publications