title: A Machine Learning Approach for Accurate Annotation of Noncoding RNAs.
abstract: 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.
authors.forename: Yinglei
authors.initials: Y
authors.lastname: Song
authors.forename: Chunmei
authors.initials: C
authors.lastname: Liu
authors.forename: Zhi
authors.initials: Z
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D016678:Genome / Q000235:genetics; D023281:Genomics / Q000379:methods*; D000069550:Machine Learning; D058977:Molecular Sequence Annotation / Q000379:methods*; D022661:RNA, Untranslated / Q000235:genetics*
chemical_list: D022661:RNA, Untranslated