Harnessing genomics and bioinformatics to understand the microbial world — driving solutions in antimicrobial resistance, sustainable agriculture, and global food security.
Our research spans five interconnected areas, leveraging microbiome data, genomics, and machine learning to address critical global challenges.
Understanding how AMR develops and spreads across human, animal, and environmental microbiomes using computational and phylogenetic approaches.
Developing AI solutions and computational approaches to transform agri-food systems for sustainability and resilience.
Exploring bacteriophage applications and methanogen biology to address environmental challenges and climate change.
Reconstructing evolutionary histories and inferring relationships across the tree of life using large-scale genomic datasets and novel computational approaches.
Creating novel bioinformatics tools, pipelines, and multi-omics integration approaches for microbiome and genomics research.
An interdisciplinary team of computational biologists, bioinformaticians, and microbiologists working across health, agriculture, and the environment.
Professor of Computational Biology at the Institute for Global Food Security, Queen's University Belfast. His main interests are identifying the genomic factors influencing phenotypic changes in organisms from Bacteria to Eukaryotes with a focus on animal microbiomes. He received his Ph.D. in 2002 from the National University of Ireland for his work in the area of phylogenetics and comparative genomics, and has since held positions at NUI Maynooth, EMBL Heidelberg, Teagasc Ireland, and Aberystwyth University.
We develop and maintain open-source bioinformatics tools used by researchers worldwide.
A computational pipeline for identifying novel Antimicrobial Peptides (AMPs) from any form of digital biological data, for synthesis and screening against multi-drug resistant bacteria and fungi.
A tool for easy implementation of j48 decision trees from WEKA on novel datasets, developed to streamline machine learning workflows in bioinformatics.
Automated Quality Improvement for multiple sequence alignments. Automatically identifies the most reliable alignment for a given protein family using MUSCLE, MAFFT, RASCAL, and NORMD.
A tool for concatenating multiple FASTA alignments for supermatrix phylogenetic analyses, part of a general phylogenomics software suite.
Construction of supertrees and exploration of phylogenomic information from partially overlapping datasets. Implements optimal phylogenetic supertree methods.
Analyses single-copy phylogenetic trees to assess compatibility with user-defined groupings (clans) in unrooted trees — ideal for large-scale phylogenomic analyses.
This paper addresses a fundamental challenge in multilocus phylogenetics — how to handle cases where different genes cover different subsets of taxa, leading to ineffective overlap. Published in Systematic Biology.
A genomic study of pathogenic Enterobacteriaceae isolated from leafy vegetables, examining antibiotic resistance profiles and the public health implications of resistance gene transfer through the food supply. Published in Environment International.
Lab member John-Paul Wilkins contributed to this study demonstrating that combining individual clinical and wastewater whole genome sequencing significantly improves SARS-CoV-2 surveillance. Published in Water Research.
We welcome applications from motivated researchers at all career stages — from PhD students to postdoctoral fellows. Get in touch to discuss current opportunities.