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BMCBI
2002

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

8 years 4 months ago
A memory-efficient dynamic programming algorithm for optimal alignment of a sequence to an RNA secondary structure
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. Background There are a growing number of RNA gene families and RNA motifs [1,2]. Many (though not all) RNAs conserve a base-paired RNA secondary structure. Computational analyses of RNA sequence families are more powerful if they take into account both primary sequence and s...
Sean R. Eddy
Added 17 Dec 2010
Updated 17 Dec 2010
Type Journal
Year 2002
Where BMCBI
Authors Sean R. Eddy
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