Computational Biology
for a Healthier World
Harnessing genomics and bioinformatics to understand the microbial world — driving solutions in antimicrobial resistance, sustainable agriculture, and global food security.
Research Themes
Our research spans five interconnected areas, leveraging microbiome data, genomics, and machine learning to address critical global challenges.
Antimicrobial Resistance & One Health
Understanding how AMR develops and spreads across human, animal, and environmental microbiomes using computational and phylogenetic approaches.
Sustainable Food Systems & Agriculture
Developing AI solutions and computational approaches to transform agri-food systems for sustainability and resilience.
Environmental Microbiology & Biotechnology
Exploring bacteriophage applications and methanogen biology to address environmental challenges and climate change.
Phylogenomics
Reconstructing evolutionary histories and inferring relationships across the tree of life using large-scale genomic datasets and novel computational approaches.
Methodological Development
Creating novel bioinformatics tools, pipelines, and multi-omics integration approaches for microbiome and genomics research.
Our Team
An interdisciplinary team of computational biologists, bioinformaticians, and microbiologists working across health, agriculture, and the environment.
Prof. Chris Creevey
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.
Software Tools
We develop and maintain open-source bioinformatics tools used by researchers worldwide.
Amply
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.
Apply Decision Tree
A tool for easy implementation of j48 decision trees from WEKA on novel datasets, developed to streamline machine learning workflows in bioinformatics.
AQUA
Automated Quality Improvement for multiple sequence alignments. Automatically identifies the most reliable alignment for a given protein family using MUSCLE, MAFFT, RASCAL, and NORMD.
CatSequences
A tool for concatenating multiple FASTA alignments for supermatrix phylogenetic analyses, part of a general phylogenomics software suite.
Clann
Construction of supertrees and exploration of phylogenomic information from partially overlapping datasets. Implements optimal phylogenetic supertree methods.
Clan Check
Analyses single-copy phylogenetic trees to assess compatibility with user-defined groupings (clans) in unrooted trees — ideal for large-scale phylogenomic analyses.
Join the Lab
We welcome applications from motivated researchers at all career stages — from PhD students to postdoctoral fellows. Get in touch to discuss current opportunities.