SGER: Sequence Alignment, Clustering, and Statistical Physics

Florida Atlantic University, Boca Raton FL

Investigators

Abstract

0110903 Yu This is a SGER award to support theoretical research and education in the area of bioinfomatics using methods inspired by statistical mechanics that differ from ones currently used in computational biology. The research centers on sequence alignment and clustering. An important problem in this area is how to assign an accurate statistical significance to a result obtained from these bioinfomatics tools. A novel alignment method and statistical theory recently developed by the PI will be enhanced to create a more complete statistical theory and to address the statistical significance problem. An aim of this work is to develop a better method to extract appropriate alignment parameters where insertions, deletions, and substitutions of biomonomers will be treated on an equal footing. Another focus of this work is to improve clustering methods using variants of the random-cluster model. The PI's new clustering method assumes no a priori knowledge of the nature of the existing data and is expected to be more robust against noisy data. %%% This SGER award supports theoretical research and graduate level education in the area of bioinformatics. The research is interdisciplinary with a view to bring the formidable tools of statistical physics to bear on an important part of the larger biological problem of understanding the meaning of the enormous amount of genome data that has been collected. The PI's research addresses the problem of being able to reliably identify homology among different sequences and to improve clustering tools that can be used to help identify gene expression patterns associated with a certain biological process or disease. ***