Protein Function Prediction with Bioinformatics Approaches

The DNA sequence of the human genome is now known, yet we are only starting to understand the functional networks of the proteins encoded in it. The group is addressing this issue with a number of computational approaches, including hidden Markov models, Bayesian networks, clustering algorithms, evolutionary models, and discriminant statistical methods. The goal is to predict function both in terms of biochemical activity and role in a pathway. To predict pathway interactions we are developing integrative computational methods and databases with a focus on identifying new disease genes. The resulting networks are available at http://FunCoup.sbc.su.se/

Group members

Mateusz Garbulowski, Postdoc

Davide Buzzao, PhD student

Thomas Hillerton, PhD student

Emma Persson, PhD student

Selected Publications

• "Global networks of functional coupling in eukaryotes from comprehensive data integration"
  Andrey Alexeyenko and Erik L.L. Sonnhammer
  Genome Research, 19:1107-1116 (2009)

• "Network-based identification of novel cancer genes"
  Gabriel Östlund, Mats Lindskog, and Erik L.L. Sonnhammer
  Molecular and Cellular Proteomics, 9:648-655 (2010)

Funding Sources

Swedish Research Council