Constructing structural alignment of RNA sequences by detecting and assessing conserved stems

Authors:
Xiaoyong Fang;Zhigang Luo;Bo Yuan;Zhenghua Wang;Fan Ding
Affiliations:
School of Computer Science, National University of Defense Technology, Changsha, China;School of Computer Science, National University of Defense Technology, Changsha, China;Department of Biomedical Informatics, College of Medicine and Public Health, Ohio State University, Columbus, Ohio;School of Computer Science, National University of Defense Technology, Changsha, China;School of Computer Science, National University of Defense Technology, Changsha, China
Venue:
LSMS'07 Proceedings of the 2007 international conference on Life System Modeling and Simulation
Year:
2007

Citing 5
Cited 0

An Iterated loop matching approach to the prediction of RNA secondary structures with pseudoknots

Bioinformatics
Alignment of RNA base pairing probability matrices

Bioinformatics
A graph theoretical approach for predicting common RNA secondary structure motifs including pseudoknots in unaligned sequences

Bioinformatics
SCARNA: fast and accurate structural alignment of RNA sequences by matching fixed-length stem fragments

Bioinformatics
The detection and assessment of possible RNA secondary structure using multiple sequence alignment

Proceedings of the 2007 ACM symposium on Applied computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

The comparative methods for predicting RNA secondary structure can be facilitated by taking structural alignments of homologous sequences as input. However, it is very difficult to construct a well structural alignment of RNA sequences without knowing the secondary structures. In this paper, we present a stem-based method for constructing structural alignment of RNA sequences with unknown structures. The method can be summarized by: 1) we detect possible stems in the RNA sequence using the so-called position matrix with which some possibly paired positions are uncovered; 2) we detect conserved stems across multiple sequences by multiplying the position matrices; 3) we assess the conserved stems using the Signal-to-Noise and the new SCFG model; 4) we construct structural alignment of RNA sequences by incorporating conserved stems with Clustal W which is a popular program for multiple sequence alignment. We tested our method on data sets composed of known structural alignments which are downloaded from the Rfam database. The accuracy, measured as sensitivity and true positive rate, of our method is much greater than alignments by Clustal W.