MASTR: multiple alignment and structure prediction of non-coding RNAs using simulated annealing.
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MASTR: multiple alignment and structure prediction of non-coding RNAs using simulated annealing. / Lindgreen, Stinus; Gardner, Paul P; Krogh, Anders.
In: Bioinformatics, Vol. 23, No. 24, 2007, p. 3304-11.Research output: Contribution to journal › Journal article › Research › peer-review
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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
VL - 23
SP - 3304
EP - 3311
JO - Computer Applications in the Biosciences
JF - Computer Applications in the Biosciences
SN - 1471-2105
IS - 24
ER -
ID: 2736980