Reduced space hidden Markov model training

Abstract

Motivation: 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. Results: 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 0(mn) to 0(m√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. Availability: 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/compbiol/sam.html. Contact: rph@@@cse.ucsc.edu.