Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine.
BACKGROUND
MicroRNAs (miRNAs) are a group of short (approximately 22 nt) non-coding RNAs that play important regulatory roles. MiRNA precursors (pre-miRNAs) are characterized by their hairpin structures. However, a large amount of similar hairpins can be folded in many genomes. Almost all current methods for computational prediction of miRNAs use comparative genomic approaches to identify putative pre-miRNAs from candidate hairpins. Ab initio method for distinguishing pre-miRNAs from sequence segments with pre-miRNA-like hairpin structures is lacking. Being able to classify real vs. pseudo pre-miRNAs is important both for understanding of the nature of miRNAs and for developing ab initio prediction methods that can discovery new miRNAs without known homology.


RESULTS
A set of novel features of local contiguous structure-sequence information is proposed for distinguishing the hairpins of real pre-miRNAs and pseudo pre-miRNAs. Support vector machine (SVM) is applied on these features to classify real vs. pseudo pre-miRNAs, achieving about 90% accuracy on human data. Remarkably, the SVM classifier built on human data can correctly identify up to 90% of the pre-miRNAs from other species, including plants and virus, without utilizing any comparative genomics information.


CONCLUSIONS
The local structure-sequence features reflect discriminative and conserved characteristics of miRNAs, and the successful ab initio classification of real and pseudo pre-miRNAs opens a new approach for discovering new miRNAs.
Xue
Chenghai
C
Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China. chenghai.xue@mail.ia.ac.cn
Li
Fei
F
He
Tao
T
Liu
Guo-Ping
GP
Li
Yanda
Y
Zhang
Xuegong
X
D000818:Animals; D001483:Base Sequence; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D005189:False Positive Reactions; D016678:Genome; D023281:Genomics; D006801:Humans; D035683:MicroRNAs / Q000145:classification* / Q000235:genetics*; D015233:Models, Statistical; D008969:Molecular Sequence Data; D009690:Nucleic Acid Conformation; D012333:RNA, Messenger / Q000378:metabolism; D012984:Software; D013045:Species Specificity
D035683:MicroRNAs; D012333:RNA, Messenger