Manuscript Title: Faster Protein Classification Using Suffix Trees Running Head: Protein Classification Using Suffix Trees Authors:

Motivation: Position-specific scoring matrices have been used extensively to recognize highly conserved protein regions. We present a method for accelerating these searches using a suffix tree data structure computed from the sequences to be searched. Methods: Building on earlier work that allows evaluation of a scoring matrix to be stopped early, the suffix tree-based method excludes many protein segments from consideration at once by pruning entire subtrees. Although suffix trees are usually expensive in space, the fact that scoring matrix evaluation requires an in-order traversal allows nodes to be stored compactly in memory and on the disk without significant loss of speed. Results: Our implementation requires as little as 12 bytes of disk storage per input symbol. Searches are accelerated by a factor of up to eleven under typical conditions. Availability: The package source code is available at: http://sequence.rutgers.edu/sat. Contact: [email protected] Abbreviations: EOS, end of sequence symbol; K, kilo; M, mega; MB, megabyte; PSSM position-specific scoring matrix.