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PubMed · 12095421

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

Sean R Eddy
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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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AlgorithmsComputational BiologyConsensus SequenceEmpirical ResearchMarkov ChainsModels, StatisticalNucleic Acid ConformationProgramming, LinearRNARNA, RibosomalRNA, Small CytoplasmicRNA, TransferSequence AlignmentSignal Recognition ParticleSoftwareSoftware DesignTime FactorsComparative StudyJournal ArticleResearch Support, Non-U.S. Gov'tResearch Support, U.S. Gov't, P.H.S.

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Sean R Eddy. 2002-07-02. A memory-efficient dynamic programming algorithm for optimal alignment of a sequence to an RNA secondary structure.. https://doi.org/10.1186/1471-2105-3-18

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