Reduced space hidden Markov model training.

Abstract

Complete forward-backward (Baum-Welch) hidden Markov model training cannot take advantage of the linear space, divide-and-conquer sequence alignment algorithms because of the examination of all possible paths rather than the single best path. This paper discusses the implementation and performance of checkpoint-based reduced space sequence alignment in the SAM hidden Markov modeling package. Implementation of the checkpoint algorithm reduced memory usage from O(mn) to O (m square root n) with only a 10% slowdown for small m and n, and vast speed-up for the larger values, such as m = n = 2000, that cause excessive paging on a 96 Mbyte workstation. The results are applicable to other types of dynamic programming. A World-Wide Web server, as well as information on obtaining the Sequence Alignment and Modeling (SAM) software suite, can be found at http://www.cse.ucsc. edu/research/compbio/sam.html. rph@cse.ucsc.edu