Philip William Fowler

This study validates an optimized Oxford Nanopore sequencing workflow for mycobacteria from primary MGIT cultures, showing high concordance with Illumina for species identification and SNP detection.

This study compares long-read and short-read sequencing for tuberculosis drug resistance and outbreak analysis, finding they are comparable with minimal differences in key clinical outputs.

This study uses a graph convolutional network to predict pyrazinamide resistance in Mycobacterium tuberculosis by integrating protein structure and residue-level features, achieving high accuracy.

The paper reprocesses SARS-CoV-2 genomic data to correct systematic errors in phylogenies using a novel assembly tool, resulting in a cleaner global tree of 4.47 million samples.

This paper evaluates the performance of the gnomonicus tool for predicting antibiotic resistance in Mycobacterium tuberculosis using a diverse testset of 2,663 samples and compares it to TB-Profiler.

This paper develops an automated method to build a comprehensive catalogue of resistance-associated variants for M. tuberculosis, achieving comparable performance to existing catalogues without expert rules.

This study improves rifampicin resistance prediction in M. tuberculosis by accounting for resistant subpopulations and compensatory mutations, increasing sensitivity without sacrificing specificity.

The paper develops an automated method to predict bedaquiline resistance in M. tuberculosis using a reproducible algorithmic approach, achieving high sensitivity and specificity in variant classification.

This study trains machine learning models to predict rifampicin resistance in M. tuberculosis based on structural and chemical features of rpoB mutations, achieving moderate accuracy.

TMAS uses deep learning to accurately detect M. tuberculosis growth in microtiter plates, improving antimicrobial susceptibility testing.

This study quantifies antibiotic resistance in tuberculosis using genomic data and MIC measurements to identify genetic determinants of resistance and hypersensitivity across multiple drugs.

The paper identifies compensatory mutations in Mycobacterium tuberculosis associated with increased in vitro growth in resistant strains, highlighting their role in resistance spread.

ReadItAndKeep removes human contamination from SARS-CoV-2 sequencing data efficiently on low-specification laptops.

GenomegaMap estimates genome-wide dN/dS ratios across 10,209 tuberculosis genomes to identify selection pressures shaping the pathogen's evolution.

This paper predicts pyrazinamide resistance in Mycobacterium tuberculosis using machine learning models trained on pncA mutations and structural features, achieving 80.2% sensitivity and 76.9% specificity.