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We are a computational biology research and training group with particular interest in analysing genomic and functional genomic data to study transcriptional regulation and disease genetics.

Our group is interested in using computational and statistical approaches to integrate genomic and functional data to study mechanisms of transcriptional regulation, and the dysregulation of transcription in disease. We collaborate widely across the fields of immunology, neuroscience and oncology on approaches ranging from single cell transcriptomics, to chromatin conformation and whole genome sequencing.

Our philosophy is to build robust, reusable code and reproducible workflows. We have developed a large, open source and publically available code base of computational genomics scripts and pipelines, and we have worked hard to produce build a reproducible software environment, using Conda, to enable others to easily use and adapt out pipelines.

We also lead the Oxford Biomedical Data Science Training Programme. This is a ten week secondment programme designed to train biomedical scientists in data science and computational biology skills and to apply them to their own data and research questions. We provide training in Linux, programming in Python and R, software engineer best practices (version control, testing), writing reproducible data analysis workflows, data visualisation, statistics and machine learning. For more information, please see the training page.

Our team

Selected publications

Collaborators

Scott Waddell (DPAG)

Gero Miesenboeck (DPAG)

Alison Simmons (MRC WIMM)

Richard Cornall (MRC WIMM/WTCHG)

Colin Ackerman (Pharmacology)

Lab Alumni

Nick Ilott

Steve Sansom

Helen Knight

Ian Sudbery

Jethro Johnson

Antonio Berlanga

Mike Morgan

Tom Smith

Katy Brown

Charlotte George

Kath Fawcett Hania Pavlou 
Martin Dienstbier