In the bioinformatics group at BRIC we are investigating how cells can activate and regulate genes using computers, statistical methods and machine learning.
An important aspect of understanding gene expression is to understand the underlying regulatory mechanisms. There are several layers of such mechanisms working on partially different scales, including:
- Epigenetic and/or chromosomal events that make DNA accessible
- Binding of proteins to cis-regulatory elements which can be positive or negative regulators
- RNA-based silencing of genes, especially by miRNAs
- Assembly and initiation of the transcriptional machinery at transcription start sites
In the promoter and miRNA bioinformatics groups we are developing models for these processes, in order to both predict the associated events and, even more importantly, to understand the processes. To this end we use both advanced machine learning methods and statistical analyses on novel high-throughput genomics data. This also involves the construction of computational methods that can be applied to new data sets.
A unique strength in the groups is that we cover the whole area from theoretical modelling to hands-on analysis and interpretation of large sets of genomics data. We also have long-standing collaborations with high-throughput genomics labs – in particular the Genome Science Center of RIKEN Yokohama Institute.