TY - JOUR
T1 - Measuring covariation in RNA alignments: Physical realism improves information measures
AU - Lindgreen, Stinus
AU - Gardner, Paul Phillip
AU - Krogh, Anders
PY - 2006
Y1 - 2006
N2 - Motivation: The importance of non-coding RNAs is becoming increasingly evident, and often the function of these molecules depends on the structure. It is common to use alignments of related RNA sequences to deduce the consensus secondary structure by detecting patterns of co-evolution. A central part of such an analysis is to measure covariation between two positions in an alignment. Here, we rank various measures ranging from simple mutual information to more advanced covariation measures. Results: Mutual information is still used for secondary structure prediction, but the results of this study indicate which measures are useful. Incorporating more structural information by considering e.g. indels and stacking improves accuracy, suggesting that physically realistic measures yield improved predictions. This can be used to improve both current and future programs for secondary structure prediction. The best measure tested is the RNAalifold covariation measure modified to include stacking. Availability: Scripts, data and supplementary material can be found at http://www.binf.ku.dk/Stinus_covariation Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.
AB - Motivation: The importance of non-coding RNAs is becoming increasingly evident, and often the function of these molecules depends on the structure. It is common to use alignments of related RNA sequences to deduce the consensus secondary structure by detecting patterns of co-evolution. A central part of such an analysis is to measure covariation between two positions in an alignment. Here, we rank various measures ranging from simple mutual information to more advanced covariation measures. Results: Mutual information is still used for secondary structure prediction, but the results of this study indicate which measures are useful. Incorporating more structural information by considering e.g. indels and stacking improves accuracy, suggesting that physically realistic measures yield improved predictions. This can be used to improve both current and future programs for secondary structure prediction. The best measure tested is the RNAalifold covariation measure modified to include stacking. Availability: Scripts, data and supplementary material can be found at http://www.binf.ku.dk/Stinus_covariation Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.
U2 - 10.1093/bioinformatics/btl514
DO - 10.1093/bioinformatics/btl514
M3 - Journal article
C2 - 17038338
SN - 1367-4803
VL - 22
SP - 2988
EP - 2995
JO - Bioinformatics
JF - Bioinformatics
IS - 24
ER -