Members of the PSI Lab study machine learning, genome biology and vision. We develop statistical inference and probabilistic reasoning (graphical modeling) methods for learning about complex patterns in data. In our genome biology research, we are interested in understanding the regulatory processes that enable a limited number of genes to generate a much more massive and diverse set of genetic messages. In our vision research, we focus on developing probabilistic generative models for scene and object analysis. The group is led by Brendan J. Frey in the Department of Electrical and Computer Engineering, with cross appointments in Computer Science, Banting and Best Department of Medical Research and Donnelly Centre for Cellular and Biomolecular Research. If you are interested in joining the group, click here.
Current Research Highlight
The Evolutionary Landscape of Alternative Splicing
Alternative splicing generates cellular diversity in many species and many human diseases are caused by mutations in the genetic code governing splicing. The Frey lab uses large-scale datasets and statistical inference methods to build regulatory models of splicing and decipher the genetic code governing splicing.
New results:
In collaboration with the Blencowe Laboratory, we recently used a regulatory model of splicing that we inferred using mouse data (Nature 2010) to understand how the frequency and regulation of alternative splicing has changed over 350 million years of evolution (Science, December 2012). We found that there is a significant difference in the amount of alternative splicing that occurs in different species and that primate brain tissues employ an unusually large amount of alternative splicing. (See the first figure, which plots the relative proportions of exons undergoing alternative splicing in diverse tissues, relative to the tissue with lowest alternative splicing frequency.) In general, the frequency of alternative splicing decreases with evolutionary distance from primates. Interestingly, we found that the regulatory model inferred using mouse data was able to predict tissue-specific alternative splicing with similar accuracy across diverse species, including human (79.8%), mouse (87.8%), chicken (82.1%) and frog (86.5%). These results demonstrate that a primary source of diversity across 350 million years of evolution can be found in the regulatory code embedded in DNA. The second figure shows elements in an ancestral tissue-specific splicing code that is shared across all four species. Splicing factors associated with code features are in square brackets. Significantly enriched features are indicated by hollow arrows; features both significantly enriched and predictive are indicated by bold arrows. Arrows are colored according to the organ with which the features are associated (refer to key).
Reference
The evolutionary landscape of alternative splicing in vertebrate species (jrnl) (PubMed)
Nuno L. Barbosa-Morais, Manuel Irimia, Qun Pan, Hui Y. Xiong, Serge Gueroussov, Leo J. Lee, Valentina Slobodeniuc, Claudia Kutter, Stephen Watt, Recep Çolak, TaeHyung Kim, Christine M. Misquitta-Ali, Michael D. Wilson, Philip M. Kim, Duncan T. Odom, Brendan J. Frey, and Benjamin J. Blencowe
Science 338(6114): 1587-1593, December 2012.
Deciphering the splicing code (jrnl) (pdf)
Yoseph Barash, John A. Calarco, Weijun Gao, Qun Pan, Xinchen Wang, Ofer Shai, Benjamin J. Blencowe & Brendan J. Frey
Nature, 465:7294, pp53-59, May 2010.