Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

Dimitrios V. Vavoulis

BSc, MRes, MSc, PhD

Computational Genomics, Statistical Machine Learning

Short Biography

I am a computational biologist at the Oxford Molecular Diagnostics Centre (OMDC, Department of Oncology) and at the Wellcome Centre for Human Genetics (WHG), University of Oxford. I hold a BSc and an MRes with distinction in Biology from the University of Patras (Greece), as well as an MSc in Evolutionary and Adaptive Systems with Distinction, and a DPhil in Computational Neurobiology from the University of Sussex. Before moving to Oxford, I held research positions at the Universities of Warwick and Bristol. 

The unifying thread underlying my career has always been a keen interest in applying computational methodologies for data modelling and analysis in the Life Sciences. In particular, I find fascinating the applications of probabilistic machine learning for the analysis of big genomic data, typically generated by next-generation sequencing technologies, especially in the context of Cancer Genomics. Recent work includes the development of statistical methodologies for tracking clonal dynamics in liquid cancers (i.e. leukaemias; Vavoulis et al., Bioinformatics 2020), for discovering the genomic and transcriptomic correlates of Richter’s syndrome (a complication of Chronic Lymphocytic Leukaemia with dismal prognosis; Klintman et al., Blood 2020), for the non-invasive prenatal diagnosis of sickle-cell disease (Cutts, Vavoulis, et al., Blood 2019), and for the joint analysis of genotypic and gene expression data for the discovery of eQTLs (Vavoulis et al., Bioinformatics 2017). An exciting avenue of research currently pursued in collaboration with experimental and clinical scientists at the OMDC, WHG, and elsewhere focuses on the discovery of cell-free DNA signals in the blood (as revealed by whole genome and targeted sequencing assays via appropriate data analysis), which could be used as biomarkers for early cancer detection and for monitoring minimal residual disease (Vavoulis et al., 2023).

You can find me online on Twitter and/or Github.

Book chapter

  1. Vavoulis DV. "Exploring Bayesian approaches to eQTL mapping through probabilistic programming", in Methods in Molecular Biology (vol. 2082), Springer, 2019


For a full list of publications, check pubmed 

*equal contribution, co-first or co-senior authors

  1. Vavoulis DV, Cutts A, ..., Schuh A. "Multimodal cell-free DNA whole-genome analysis combined with TET-Assisted Pyridine Borane Sequencing is sensitive and reveals specific cancer signals", medRxiv, 2023
  2. Robbe P, Ridout KE, Vavoulis DV, ..., Schuh A. "Whole-genome sequencing of CLL identifies subgroups with distinct biological and clinical features", Nature Genetics, 2022 
  3. Vavoulis DV, Cutts A, Taylor JC, Schuh A. "A statistical approach for tracking clonal dynamics in cancer using longitudinal next-generation sequencing data", Bioinformatics, 2020
  4. Klintman J, Appleby N, ..., Taylor JC*, Vavoulis DV*, Schuh A*. "Differential genomic and transcriptomic events associated with high-grade transformation of Chronic Lymphocytic Leukemia", Blood2020
  5. Cutts A*, Vavoulis DV*, Petrou M, Smith F, Clark B, Henderson S, Schuh A. "A method for non-invasive prenatal diagnosis of monogenic autosomal recessive disorders", Blood, 2019
  6. Vavoulis DV, Pagnamenta AT, Knight SJL, Pentony MM, ..., Taylor JC. "Whole genome sequencing identifies putative associations between genomic polymorphisms and clinical response to the anti-epileptic drug levetiracetam", medRxiv, 2019
  7. Vavoulis DV, Taylor J & Schuh A. "Hierarchical probabilistic models for multiple gene-variant associations based on next-generation sequencing data”, Bioinformatics2017
  8. Vavoulis DV, Frascescatto M, Heutink P & Gough J. “DGEclust: Differential expression analysis of clustered count data”, Genome Biology2015
  9. Oates M, Stahlhacke J, Vavoulis DV, …, Gough J. The SUPERFAMILY 1.75 database in 2014: a doubling of data”, Nucleic Acids Research2014
  10. Vavoulis DV, Straub VA, Aston JAD & Feng JF. “A self-organising state-space-model approach for parameter estimation in Hodgkin-Huxley-type models of single neurones”, PLoS Computational Biology2012
  11. Vavoulis DV, Nikitin ES, Kemenes I, Feng JF, Benjamin PR & Kemenes G. “Balanced plasticity and stability of the electrical properties of a molluscan modulatory interneuron after conditioning: a computational study”, Front Behav Neurosci, 2010
  12. Ashmole I, Vavoulis DV, Stansfeld PJ, Mehta PR, Feng JF, Sutcliffe MJ & Stanfield PR. “The response of the tandem pore potassium channel TASK-3 (K2P9.1) to voltage: gating at the cytoplasmic mouth”, J Physiol, 587(20):4769-4783, 2009
  13. Nikitin ES, Vavoulis DV, Kemenes I, Marra V, Pirger Z, Michel M, Feng JF, O’Shea M, Benjamin PR & Kemenes G. “Persistent sodium current is a non-synaptic substrate for long-term associative memory”, Curr Biol 18(16): 1221-1226, 2008
  14. Vavoulis DV, Straub VA, Kemenes I, Kemenes G, Feng JF & Benjamin PR. “Dynamic control of a Central Pattern Generator circuit: a computational model of the snail feeding network”, Eur J Neurosci, 2007