2 p.m. Thursday February 12, 2009

Title: LOCST: a Low Complexity Sequence Search Tool
By: Stephen M. Mount, University of Maryland
Venue: Biomolecular Science Building Room 3118
Abstract: Alignment-based tools such as blast are in widespread use for identifying similar proteins. Low-complexity regions are typically not included in such alignments even though they are often important for function. Examples include argnine-serine-rich proteins involved in splicing and proline-rich, glutamine-rich and acidic transcription activation domains. An approach for identifying and evaluating similar low-complexity regions within proteins based on shared repeated dipeptides will be presented, as will its implementation in the program LOCST (Low Complexity Sequence Search Tool). This is work was performed with Nicolas Tilmans and Stephen Fiorelli.

2 p.m. Thursday February 26, 2009

Title: Protein Annotation Prediction By Clustering Within Interaction Networks
By: Carl Kingsford, University of Maryland
Venue: Biomolecular Science Building Room 3118
Abstract: Determining protein function is a fundamental biological challenge, and protein-protein interaction networks are an increasingly useful data source from which to computationally predict protein annotations. One approach to automated detection of protein complexes and prediction of biological processes is to divide an interaction network into biologically meaningful modules or clusters. I will present several graph clustering techniques and illustrate their usefulness for predicting protein annotations. I will describe a novel method to decompose a hierarchical tree decomposition into a collection of clusters that optimally match a set of known annotations. We find that our approach generally outperforms commonly used heuristics for identifying protein complexes from hierarchical clusterings. The technique is general and may be of use in other applications where hierarchical clustering is used. I will also show how a graph compression technique called graph summarization leads to more biologically meaningful modules that other graph clustering algorithms. Time permitting, I will also describe how protein interaction networks can be used to transfer functional annotations between species.