Professor of Computational Biology
Overview
Computational epigenomics of human cell identity, ageing, disease, and cell-free DNA.
Our group develops computational, statistical, and AI-based methods for interpreting DNA methylation, hydroxymethylation, and other epigenomic signals across human tissues, cell types, ageing, and cancer. We integrate 5mC and 5hmC technologies with multi-omic data - including genetics, chromatin, gene expression, single-cell and allele-specific profiles, fragmentomics, and liquid biopsy cell-free DNA - to understand gene regulation, cellular identity, and disease-associated epigenetic variation. A central goal is to advance multimodal liquid biopsy: combining methylation, hydroxymethylation, nucleosome positioning, fragmentation patterns, and genetic information to infer tissue-of-origin, tumour biology, ageing-related changes, and otherwise inaccessible physiological processes from blood.
Key Publications
- A DNA methylation atlas of normal human cell types. Nature (2023). 613(7943):355-364. PMID: 36599988
- Atlas of imprinted and allele-specific DNA methylation in the human body. Nat Commun (2025). 16(1):2141. PMID: 40069157
- The genetic basis for DNA methylation variation across tissues and development. Nat Commun (2026). Online ahead of print. PMID: 41980926
- Systematic errors in enzymatic conversion limit cell-free DNA methylation specificity. bioRxiv (2026). DOI: 10.64898/2026.03.24.713040
- Accurate age prediction from blood using a small set of DNA methylation sites and a cohort-based machine learning algorithm. Cell Rep Methods (2023). 3(9):100567. PMID: 37751697
- Time is encoded by methylation changes at clustered CpG sites. Cell Rep (2025). 44(7):115958. PMID: 40664208
- Cell-to-cell variability and gain of methylation at polycomb CpG islands as a hallmark of aging. Nat Commun (2026) Jun 9. PMID: 42265112