MASTR: multiple alignment and structure prediction of non-coding RNAs using simulated annealing.

TY - JOUR

T1 - MASTR: multiple alignment and structure prediction of non-coding RNAs using simulated annealing.

AU - Lindgreen, Stinus

AU - Gardner, Paul P

AU - Krogh, Anders

N1 - Keywords: Algorithms; Base Sequence; Molecular Sequence Data; RNA; RNA, Untranslated; Sequence Alignment; Sequence Analysis, RNA; Software

PY - 2007

Y1 - 2007

N2 - MOTIVATION: As more non-coding RNAs are discovered, the importance of methods for RNA analysis increases. Since the structure of ncRNA is intimately tied to the function of the molecule, programs for RNA structure prediction are necessary tools in this growing field of research. Furthermore, it is known that RNA structure is often evolutionarily more conserved than sequence. However, few existing methods are capable of simultaneously considering multiple sequence alignment and structure prediction. RESULT: We present a novel solution to the problem of simultaneous structure prediction and multiple alignment of RNA sequences. Using Markov chain Monte Carlo in a simulated annealing framework, the algorithm MASTR (Multiple Alignment of STructural RNAs) iteratively improves both sequence alignment and structure prediction for a set of RNA sequences. This is done by minimizing a combined cost function that considers sequence conservation, covariation and basepairing probabilities. The results show that the method is very competitive to similar programs available today, both in terms of accuracy and computational efficiency. AVAILABILITY: Source code available from http://mastr.binf.ku.dk/

AB - MOTIVATION: As more non-coding RNAs are discovered, the importance of methods for RNA analysis increases. Since the structure of ncRNA is intimately tied to the function of the molecule, programs for RNA structure prediction are necessary tools in this growing field of research. Furthermore, it is known that RNA structure is often evolutionarily more conserved than sequence. However, few existing methods are capable of simultaneously considering multiple sequence alignment and structure prediction. RESULT: We present a novel solution to the problem of simultaneous structure prediction and multiple alignment of RNA sequences. Using Markov chain Monte Carlo in a simulated annealing framework, the algorithm MASTR (Multiple Alignment of STructural RNAs) iteratively improves both sequence alignment and structure prediction for a set of RNA sequences. This is done by minimizing a combined cost function that considers sequence conservation, covariation and basepairing probabilities. The results show that the method is very competitive to similar programs available today, both in terms of accuracy and computational efficiency. AVAILABILITY: Source code available from http://mastr.binf.ku.dk/

U2 - 10.1093/bioinformatics/btm525

DO - 10.1093/bioinformatics/btm525

M3 - Journal article

C2 - 18006551

SN - 1367-4803

VL - 23

SP - 3304

EP - 3311

JO - Bioinformatics

JF - Bioinformatics

IS - 24

ER -