Efficient alignment of RNAs with pseudoknots using sequence alignment constraints

Authors:
Byung-Jun Yoon
Affiliations:
Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX
Venue:
EURASIP Journal on Bioinformatics and Systems Biology - Special issue on applications of signal procesing techniques to bioinformatics, genomics, and proteomics
Year:
2009

Citing 7
Cited 0

A tutorial on hidden Markov models and selected applications in speech recognition

Readings in speech recognition
Exploiting conserved structure for faster annotation of non-coding RNAs without loss of accuracy

Bioinformatics
Pairwise local structural alignment of RNA sequences with sequence similarity less than 40%

Bioinformatics
Predicting a set of minimal free energy RNA secondary structures common to two sequences

Bioinformatics
Pair stochastic tree adjoining grammars for aligning and predicting pseudoknot RNA structures

Bioinformatics
Sequence-based heuristics for faster annotation of non-coding RNA families

Bioinformatics
Context-Sensitive Hidden Markov Models for Modeling Long-Range Dependencies in Symbol Sequences

IEEE Transactions on Signal Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

When aligning RNAs, it is important to consider both the secondary structure similarity and primary sequence similarity to find an accurate alignment. However, algorithms that can handle RNA secondary structures typically have high computational complexity that limits their utility. For this reason, there have been a number of attempts to find useful alignment constraints that can reduce the computations without sacrificing the alignment accuracy. In this paper, we propose a new method for finding effective alignment constraints for fast and accurate structural alignment of RNAs, including pseudoknots. In the proposed method, we use a profile-HMM to identify the "seed" regions that can be aligned with high confidence. We also estimate the position range of the aligned bases that are located outside the seed regions. The location of the seed regions and the estimated range of the alignment positions are then used to establish the sequence alignment constraints. We incorporated the proposed constraints into the profile context-sensitive HMM (profile-csHMM) based RNA structural alignment algorithm. Experiments indicate that the proposed method can make the alignment speed up to 11 times faster without degrading the accuracy of the RNA alignment.