RNA secondary structure prediction from sequence alignments using a network of k-nearest neighbor classifiers.
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.
Basic Research Program, SAIC-Frederick, Inc, National Cancer Institute-Frederick, MD 21702, USA.
Eckart
E
Bindewald
Bruce A
BA
Shapiro
D000465:Algorithms; D001105:Archaea / Q000201:enzymology / Q000235:genetics; D001185:Artificial Intelligence; D001483:Base Sequence; D008969:Molecular Sequence Data; D009690:Nucleic Acid Conformation; D012313:RNA / Q000737:chemistry*; D019642:RNA, Archaeal / Q000737:chemistry / Q000235:genetics; D043262:Ribonuclease P / Q000235:genetics; D016415:Sequence Alignment / Q000379:methods* / Q000706:statistics & numerical data; D012984:Software; D013816:Thermodynamics
D019642:RNA, Archaeal; D012313:RNA; D043262:Ribonuclease P