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

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Eddy, Sean R·2002·BMC Bioinformatics

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.

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Metadata

Authors
Eddy, S. R.
Venue
BMC Bioinformatics
Published
2002-07-02
Volume
3
Issue
1
Pages
18-18
ISSN
1471-2105 (ISSN-L)
OpenAlex
W2064345118
Cited by
244
References
4
FWCI
3.89
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: A memory-efficient dynamic programming algorithm for optimal alignment of a sequence to an RNA secondary structure. https://app.cochat.ai/papers/10.1186/1471-2105-3-18
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Eddy, S. R. (2002). A memory-efficient dynamic programming algorithm for optimal alignment of a sequence to an RNA secondary structure. BMC Bioinformatics. https://doi.org/10.1186/1471-2105-3-18

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