Research article

A memory-efficient dynamic programming algorithm for optimal alignment of a sequence to an RNA secondary structure

Sean R Eddy

Howard Hughes Medical Institute & Department of Genetics, Washington University School of Medicine, Saint Louis, Missouri 63110 USA

author email corresponding author email

BMC Bioinformatics 2002, 3:18doi:10.1186/1471-2105-3-18

Published:	2 July 2002

Abstract

Background

Covariance models (CMs) are probabilistic models of RNA secondary structure, analogous to profile hidden Markov models of linear sequence. The dynamic programming algorithm for aligning a CM to an RNA sequence of length N is O(N³) in memory. This is only practical for small RNAs.

Results

I describe a divide and conquer variant of the alignment algorithm that is analogous to memory-efficient Myers/Miller dynamic programming algorithms for linear sequence alignment. The new algorithm has an O(N² log N) memory complexity, at the expense of a small constant factor in time.

Conclusions

Optimal ribosomal RNA structural alignments that previously required up to 150 GB of memory now require less than 270 MB.