RNA secondary structure prediction from sequence alignments using a network of k-nearest neighbor classifiers
Abstract
We present a machine learning method (a hierarchical network of k-nearest neighbor classifiers) that uses an RNA sequence alignment in order to predict a consensus RNA secondary structure. The input to the network is the mutual information, the fraction of complementary nucleotides, and a novel consensus RNAfold secondary structure prediction of a pair of alignment columns and its nearest neighbors. Given this input, the network computes a prediction as to whether a particular pair of alignment columns corresponds to a base pair. By using a comprehensive test set of 49 RFAM alignments, the program KNetFold achieves an average Matthews correlation coefficient of 0.81. This is a significant improvement compared with the secondary structure prediction methods PFOLD and RNAalifold. By using the example of archaeal RNase P, we show that the program can also predict pseudoknot interactions.
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Footnotes
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Article and publication are at http://www.rnajournal.org/cgi/doi/10.1261/rna.2164906.
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- Accepted November 23, 2005.
- Received July 19, 2005.
- Copyright 2006 by RNA Society