Alison Mather

@alisonmather.bsky.social

Epidemiology, microbial (meta)genomics, antimicrobial resistance, One Health, Professor of Microbial Genomic Epidemiology at Quadram Institute Bioscience & University of East Anglia.

6/ We applied the tool to public datasets on fluoroquinolone, β-lactam, and rifamycin resistance, showing how mutation-level profiling resolves discrepancies between phenotypes and gene-only predictions.

5/ In benchmarks using simulated reads (100–5000 bp), MetaPointFinder outperformed existing mutation mappers and showed strong recovery of mutation-positive reads, particularly from long-read metagenomes.

4/ The outputs enable calculation of both relative abundance of mutation-driven AMR and the proportion of resistant reads among all reads mapping to a locus (WT + R). Metrics can be summarised at read, gene, or antibiotic-class level, extending metagenomic resistome profiling beyond gene presence.

3/ MetaPointFinder performs read-level protein and DNA alignment (DIAMOND + KMA) against curated AMRFinder mutation references. A custom scoring engine evaluates each substitution, enabling accurate detection across both long- and short-read datasets.

2/ In metagenomic data, most AMR tools detect only acquired genes, while point mutations in gyrA, parC, folA, rpoB, 23S rRNA and others are major drivers of resistance. These variants are rarely quantified because they require precise alignment and position-specific evaluation.

PhD available with me at NTU. Metabolomics and cell-line work (... maybe some microbiota ...). Open to UK and international students. Host–microbiota interactions in pancreatic cancer: determining whether they exist, and their influence on disease Please RT. #MicrobiomeSky

Host–microbiota interactions in pancreatic cancer: determining whether they exist, and their influ...

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