Modern biology increasingly relies on high-throughput techniques. This trend challenges computational biologists to quickly extract as much useful information from the data as possible. In the genomic sense, this primarily implies correlating phenotypic differences with observed nucleotide sequence variations. On the protein side the challenge generally is to annotate protein function at reasonable accuracy levels. The whole organism level, then incorporates all types of evidence to annotate evolutionary history, current health conditions, and prognosed phenotypic changes.

We believe that nucleic and amino acid sequences contain a large portion of the information necessary to address both of these directions. However, we are always willing to supplement this data with other sources available for computational access. The main interest of this lab is in developing fast, accurate, and meaningful ways of analyzing the growing deluge of biological data and in bringing these developments bench- (or patient-) side. To make our predictions we rely on a number of sequence-based features (including evolutionary information, predicted structural features, and available annotations) and utilize a variety of computational methodologies (including artificial learning, network analysis and statistical methods).

Active projects and services in the lab can be explored here.

We are always looking for interested/qualified individuals to join our team!