Rapid Development and Distribution of Statistical Tools for High-Throughput Sequencing Data

WP6: Genome, Epigenome and Transcriptome

The overarching goal of this work package is the integration of various -omics datasets, often from different platforms and laboratories, to “provide more biological insight than using one data set alone”. The complicated relationship between genetic variation, epigenetic variation and expression outcomes remains poorly understood; aberrations in these have been linked to the occurrence and severity of many diseases, including cancer, inflammatory diseases and diabetes. Furthermore, there is emerging evidence that genomic and epigenomic influences can directly modulate alternative splicing activity. Unlike genetic changes, epigenetic changes are potentially reversible or malleable; notably, there are now at least three FDA-approved epigenetic pharmaceuticals. Thus, a deeper understanding of epigenetic mechanisms may have far-reaching implications for the early identification, treatment and management of disease.
We will develop the new statistical methodolgy that is required to integrate genomic, epigenomic and transcriptomic data from high throughput technologies and thereby characterise mechanisms of regulation and function. Using integrative computational approaches, we aim to elucidate mechanisms by which genetic and epigenetic factors regulate gene expression in normal cells and become altered in disease.


University of Zurich (Lead Partner)
University of Manchester
University of Sheffield