Mathematics

New data gathering technologies such as ChIP-seq and BS-seq are allowing scientists to probe the epigenetic regulation of gene expression at unprecedented resolution. Yet the analysis of such data brings several non-trivial computational challenges. Here, I focus on how we can exploit the spatial correlation information to develop more powerful statistical models. I will briefly describe how we developed non-parametric statistical testing techniques for ChIP and BS-seq data, and discuss how spatial information can be used to extract more informative features from such data.

Short bio: Guido Sanguinetti is Chair of Computational Bioinformatics in the School of Informatics at the University of Edinburgh. His interests focus on machine learning methodologies and applications in biology, particularly in dynamical systems and high-throughput data modelling. He has published over 80 papers in international journals including Science, PNAS and Nature journals. He held an ERC Starting Grant 2012-2017 and was the recipient of the 2012 PNAS Cozzarelli Prize in Applied Science and Engineering.