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A memory-efficient dynamic programming algorithm for optimal alignment of a sequence to an RNA secondary structure
2002
BMC Bioinformatics
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. 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)
pmid:12095421
pmcid:PMC119854
fatcat:j4zsregp5fgtzn4xbfyjcdya3a