A Machine Learning Approach for Accurate Annotation of Noncoding RNAs

Searching genomes to locate noncoding RNA genes with known secondary structure is an important problem in bioinformatics. In general, the secondary structure of a searched noncoding RNA is defined with a structure model constructed from the structural alignment of a set of sequences from its family. Computing the optimal alignment between a sequence and a structure model is the core part of an algorithm that can search genomes for noncoding RNAs. In practice, a single structure model may not be sufficient to capture all crucial features important for a noncoding RNA family. In this paper, we develop a novel machine learning approach that can efficiently search genomes for noncoding RNAs with high accuracy. During the search procedure, a sequence segment in the searched genome sequence is processed and a feature vector is extracted to represent it. Based on the feature vector, a classifier is used to determine whether the sequence segment is the searched ncRNA or not. Our testing results show that this approach is able to efficiently capture crucial features of a noncoding RNA family. Compared with existing search tools, it significantly improves the accuracy of genome annotation.