Author

R. J. Leigh

National University of Ireland, Maynooth

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Microbiology· 4

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Organism:E. coli· 2 Assay:metagenomics· 2 Research Focus:variant calling· 2 Assay:whole-genome sequencing· 2 Research Focus:batch correction / integration· 1 Research Focus:benchmarking· 1 Approach:comparative / phylogenetic analysis· 1

Frequent Co-Authors

F. Walsh· 3 shared R. A. Murphy· 2 shared B. Lynch· 1 shared C. M. Burgess· 1 shared C. McKenna· 1 shared Ciara TyrrelliD· 1 shared D. Münzenmaier· 1 shared David DrissneriD· 1 shared

Activity by Year

2021-2025 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 19 mutual co-author links

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3 shared papersMicrobiology

F. Walsh

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Author profile →

4 papers

Recent·Most cited

Genomic analysis of antimicrobial resistant Escherichia coli isolated from manure and manured agricultural grasslands

Ciara Tyrrell, C. M. Burgess, Fiona Brennan, D. Münzenmaier, David Drissner, R. J. Leigh, Fiona Walsh

This study identifies AMR E. coli in agricultural grasslands and manure, revealing genomic adaptations and resistance mechanisms.

npj Antimicrobials and Resistance · Feb 3, 2025Peer-reviewedMicrobiology

Comparative genomics and pangenomics of vancomycin resistant and susceptible Enterococcus faecium from Irish hospitals across 20 years

R. J. Leigh, C. McKenna, R. McWade, B. Lynch, F. Walsh

This study compares genomics and pangenomics of vancomycin-resistant and susceptible Enterococcus faecium isolates from Irish hospitals over 20 years, revealing evolutionary trends and resistance mechanisms.

bioRxiv · Nov 22, 2021 · PreprintMicrobiology

statSuma: automated selection and performance of statistical comparisons for microbiome studies

R. J. Leigh, R. A. Murphy, F. Walsh

statSuma automates statistical test selection for microbiome studies by analyzing data structures and recommending appropriate tests for pairwise and multicategorical comparisons.

bioRxiv · Jun 15, 2021 · PreprintMicrobiology

uniForest: an unsupervised machine learning technique to detect outliers and restrict variance in microbiome studies

R. J. Leigh, R. A. Murphy, F. Walsh

uniForest applies Isolation Forests to microbiome data to detect outliers and reduce variance, achieving significant variance reduction in community structure and alpha diversity.

bioRxiv · May 17, 2021 · PreprintMicrobiology