Peter Minary

Professor Peter Minary

Associate Professor of Computer Science

Wolfson Building, Parks Road, Oxford OX1 3QD

Themes:

• Computational Biology and Health Informatics

Interests

We develop new computational methods for challenging problems in molecular biology and genomics, with a particular emphasis on approaches that scale to genome-wide settings. Our research combines AI/ML, applied statistics, scientific computing, algorithmic development, and increasingly language-model-based approaches, motivated by questions in CRISPR, epigenetics, and structural biology, and informed by close collaboration with both computational and experimental laboratories.

A central theme of the group is problem-driven method development: important scientific and real-world questions motivate new models and algorithms, and these methods in turn enable new forms of prediction, interpretation, and design. We draw on demanding problems in genome editing, molecular biology, and structural modelling to drive methodological innovation.

Current research areas include:

• AI x CRISPR: physically informed, probabilistic deep learning for CRISPR-based genome editing modalities
• Epigenetics x CRISPR: understanding how epigenetic state and chromatin structure influence CRISPR on-target and off-target activity
• Method development: simulation-assisted machine learning, uncertainty quantification, multimodal deep learning, language-model-based methods, and advanced optimisation and sampling techniques (e.g. hierarchical Monte Carlo)
• 3D structural modelling: RNA (junctions, trees, pseudoknots), DNA (nucleosome architecture), and proteins (including TCR–pMHC complexes)
• Software development: open computational tools including MOSAICS 

We welcome DPhil students, postdoctoral researchers, and collaborators interested in rigorous method development alongside impactful real-world applications.

Selected Publications

View AllManage publications

• Learning to utilize internal protein 3D nanoenvironment descriptors in predicting CRISPR–Cas9 off−target activity

  Jeffrey Kelvin Mak‚ Artemi Bendandi‚ José Augusto Salim‚ Ivan Mazoni‚ Fabio Rogerio de Moraes‚ Luiz Borro‚ Florian Störtz‚ Walter Rocchia‚ Goran Neshich and Peter Minary

  In NAR Genomics and Bioinformatics. Vol. 7. No. 2. Pages lqaf054. 2025.

  Details about Learning to utilize internal protein 3D nanoenvironment descriptors in predicting CRISPR–Cas9 off−target activity | BibTeX data for Learning to utilize internal protein 3D nanoenvironment descriptors in predicting CRISPR–Cas9 off−target activity

• Learning to quantify uncertainty in off−target activity for CRISPR guide RNAs

  Furkan Ozden and Peter Minary

  In Nucleic Acids Research. Vol. 52. No. 18. Pages e87. October, 2024.

  Details about Learning to quantify uncertainty in off−target activity for CRISPR guide RNAs | BibTeX data for Learning to quantify uncertainty in off−target activity for CRISPR guide RNAs | DOI (10.1093/nar/gkae759) | Link to Learning to quantify uncertainty in off−target activity for CRISPR guide RNAs

• piCRISPR: physically informed deep learning models for CRISPR/Cas9 off−target cleavage prediction

  Florian Störtz‚ Jeffrey K Mak and Peter Minary

  In Artificial Intelligence in the Life Sciences. Vol. 3. Pages 100075. 2023.

  Details about piCRISPR: physically informed deep learning models for CRISPR/Cas9 off−target cleavage prediction | BibTeX data for piCRISPR: physically informed deep learning models for CRISPR/Cas9 off−target cleavage prediction

Team

Past Students