A novel representation of RNA secondary structure based on element-contact graphs.
BACKGROUND
Depending on their specific structures, noncoding RNAs (ncRNAs) play important roles in many biological processes. Interest in developing new topological indices based on RNA graphs has been revived in recent years, as such indices can be used to compare, identify and classify RNAs. Although the topological indices presented before characterize the main topological features of RNA secondary structures, information on RNA structural details is ignored to some degree. Therefore, it is necessity to identify topological features with low degeneracy based on complete and fine-grained RNA graphical representations.


RESULTS
In this study, we present a complete and fine scheme for RNA graph representation as a new basis for constructing RNA topological indices. We propose a combination of three vertex-weighted element-contact graphs (ECGs) to describe the RNA element details and their adjacent patterns in RNA secondary structure. Both the stem and loop topologies are encoded completely in the ECGs. The relationship among the three typical topological index families defined by their ECGs and RNA secondary structures was investigated from a dataset of 6,305 ncRNAs. The applicability of topological indices is illustrated by three application case studies. Based on the applied small dataset, we find that the topological indices can distinguish true pre-miRNAs from pseudo pre-miRNAs with about 96% accuracy, and can cluster known types of ncRNAs with about 98% accuracy, respectively.


CONCLUSIONS
The results indicate that the topological indices can characterize the details of RNA structures and may have a potential role in identifying and classifying ncRNAs. Moreover, these indices may lead to a new approach for discovering novel ncRNAs. However, further research is needed to fully resolve the challenging problem of predicting and classifying noncoding RNAs.
Shu
Wenjie
W
Beijing Institute of Radiation Medicine, Beijing 100850, China. shuwj@bmi.ac.cn
Bo
Xiaochen
X
Zheng
Zhiqiang
Z
Wang
Shengqi
S
D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003196:Computer Graphics; D003627:Data Interpretation, Statistical; D003663:Decision Trees; D006801:Humans; D008958:Models, Molecular; D016571:Neural Networks, Computer; D009690:Nucleic Acid Conformation; D021281:Quantitative Structure-Activity Relationship; D022661:RNA, Untranslated / Q000145:classification* / Q000648:ultrastructure*
D022661:RNA, Untranslated