George William Kagugube

Multidisciplinary researcher in bioinformatics and computational biology with extensive experience in analysing high-throughput omics data and developing reproducible workflows. Skilled in bulk and Single-cell RNA-seq, genome assembly and annotation, functional enrichment and imaging data integration. Experienced in applying machine learning techniques for biological data interpretation and predictive modelling. Strong record of cross-disciplinary collaboration, mentoring and delivering bioinformatics support to diverse research groups. Currently completing a PhD in Metal Neurotoxicity at UCL, submission expected on 28th October 2025.

• National Citizen Services, Senior Mentor, London, UK, 2017-06, 2018-10
• International Citizen Services, Team Leader, Tamil Nadu, India, 2013-04, 2013-08

• Issit, T. et al. The hepcidin-ferroportin axis influences mitochondrial function, proliferation, and migration in pulmonary artery endothelial and smooth muscle cells. doi: https://doi.org/10.1101/2023.10.09.561497
• Cui, J. (Katie) et al. Impact of Pathological Vascular Remodelling on Right Ventricular Mechanics. Computational Physiology: Simula Summer School 2022 – Student Reports (ed. McCabe, K. J.) 91–109 (Springer Nature Switzerland, 2023). doi:10.1007/978-3-031-25374-4_7.
• Atkinson, L., Basharat, A., Benson, A. P., Colman, M. A., Holden, A. V, Kagugube, G. W., & Pervolaraki, E. 92016). Computational Modelling of Cardiac Electrophysiological Changes in Malarial Fever, 43, 3–6. https://doi.org/10.22489/CinC.2016.315-27 (Conference publication)

• PhD Researcher – Division of Biosciences (Cell and Developmental Biology)
• University College London Oct 2021 – Present
• Designed RNA-seq workflows using Snakemake and R for transcriptome-wide analysis
• Conducted functional enrichment, GO/KEGG pathway annotation, and visualisation
• Integrated imaging and physiological readouts with gene expression analysis
• Mentored junior lab members on RNA-seq and bioinformatics best practices
• Kings College London, LiDO DTP Rotation project Feb 2022 – July 2022
• Title: A cohort of virtual human atria for in-silico testing of ablation and pharmacology therapies
• MRI image data analysis for in silico reconstruction of human atria for clinical applications
• And to study treatment and understand the pathological mechanisms of atrial fibrillation.
• Imperial College London
• Project 1: Supervised Machine learning guided diagnosis and characterisation of Atrial Fibrillation
• Analysis of contact electrograms from atrial cardiomyocyte monolayers
• In the presence and absence of fibrosis exposed to a gap junction uncoupler
• And train different classifiers to predict the propensity of fibrillatory activity and potential cause.