Computational Approaches to Structure/Function Genomics
We develop computational methods for the analysis and integration of molecular sequence, structure, and expression
data. Our aim is to understand organismal biology by interpreting the information encoded in complete genomes. This
work is presently focused on the areas of structural and functional genomics.
Structural genomics projects attempt to provide an experimental structure or a good theoretical model for every tractable
protein in all completed genomes. Our work involves organizing proteins into families according to homology; classifying
proteins and RNA according to structure; predicting structure from homology and constructing atomic coordinate
models; providing information resources for structural genomics; developing methods for selection of proteins for
experimental characterization; and analyzing solved structures to detect homology and functional information.
We study computational functional genomics by creating algorithms using molecular sequence, structure, phylogeny, and
expression information to infer the functions of genes. This work includes the use of gene genealogies to trace gene
histories and functional divergences; reverse-genomics comparison of multiple complete genomes to locate genes
associated with characterized cellular or biochemical functions; creation of databases of genomic information; and
continued refinement of sequence comparison methods. We also combine sequence comparison with expression and
other experimental data to improve molecular and cellular functional characterization.
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