Title

Improving the prediction of RNA secondary structure by detecting and assessing conserved stems

Authors

Xiaoyong Fang^{1, *}, Zhigang Luo¹, Bo Yuan², Jinhua Wang¹

Affiliation

¹School of Computer Science, National University of Defense Technology, 410073 Changsha, China; ²Department of Biomedical Informatics, College of Medicine and Public Health, Ohio State University, 43210-1239 Columbus Ohio, USA

Email

xyfang@nudt.edu.cn; * Corresponding author

Article Type

Hypothesis

Date

received December 14, 2007; accepted December 28, 2007; published online December 30, 2007

The prediction of RNA secondary structure can be facilitated by incorporating with comparative analysis of homologous sequences. However, most of existing comparative methods are vulnerable to alignment errors and thus are of low accuracy in practical application. Here we improve the prediction of RNA secondary structure by detecting and assessing conserved stems shared by all sequences in the alignment. Our method can be summarized by: 1) we detect possible stems in single RNA sequence using the so-called position matrix with which some possibly paired positions can be uncovered; 2) we detect conserved stems across multiple RNA sequences by multiplying the position matrices; 3) we assess the conserved stems using the Signal-to-Noise; 4) we compute the optimized secondary structure by incorporating the so-called reliable conserved stems with predictions by RNAalifold program. We tested our method on data sets of RNA alignments with known secondary structures. The accuracy, measured as sensitivity and specificity, of our method is greater than predictions by RNAalifold.

RNA; secondary structure; conserved stem; homologous sequence; Signal-to-Noise

Citation

Fang et al., Bioinformation 2(5): 222-229 (2007)

Edited by

J. C. Tong, T. W. Tan & S. Ranganathan

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.