Best ECCB 2016 Paper
PhD student Andreas Kapourani won best paper at the prestigious European Conference on Computational Biology (ECCB).
His paper was entitled, ‘Higher order methylation features for clustering and prediction in epigenomic studies.’
The conference was held 3-7 September in The Hague, Netherlands and organised by the Dutch Techcentre for Life Sciences, BioSB research school and ELIXIR in association with the Vrij Universiteit Amsterdam and Delft University of Technology. Delegates included scientists working in the fields of bioinformatics, computational biology, biology, medicine and systems biology.
Andreas’s supervisor, Dr Guido Sanguinetti, says:
“ECCB is one of the largest Computational Biology conferences, attracting over a thousand participants from academia and industry and several hundred papers are submitted, so a best paper award is pretty big news!”
In an abstract published on the ECCB’s website, Andreas says:
DNA methylation is an intensely studied epigenetic mark, yet its functional role is incompletely understood. Attempts to quantitatively associate average methylation to expression yield poor correlations outside of the well-understood methylation-switch at CpG-islands. We propose a probabilistic model to extract higher-order features associated with the methylation profile across a defined region. These features quantitate precisely notions of shape of methylation profiles, capturing spatial correlations in methylation patterns. Using these features, we construct a powerful machine learning predictor of gene expression, significantly improving upon predictive power of average methylation. Furthermore, we use these features to cluster promoter-proximal regions, showing that five major patterns of methylation occur across cell lines, and provide evidence that methylation beyond CGIs may be related to transcriptional regulation. Results support previous reports of a functional role of spatial correlations in methylation patterns, and provide a mean to quantitate such features for downstream analyses.