Measuring covariation in RNA alignments: Physical realism improves information measures

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Standard

Measuring covariation in RNA alignments: Physical realism improves information measures. / Lindgreen, Stinus; Gardner, Paul Phillip; Krogh, Anders.

In: Bioinformatics, Vol. 22, No. 24, 2006, p. 2988-2995.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Lindgreen, S, Gardner, PP & Krogh, A 2006, 'Measuring covariation in RNA alignments: Physical realism improves information measures', Bioinformatics, vol. 22, no. 24, pp. 2988-2995. https://doi.org/10.1093/bioinformatics/btl514

APA

Lindgreen, S., Gardner, P. P., & Krogh, A. (2006). Measuring covariation in RNA alignments: Physical realism improves information measures. Bioinformatics, 22(24), 2988-2995. https://doi.org/10.1093/bioinformatics/btl514

Vancouver

Lindgreen S, Gardner PP, Krogh A. Measuring covariation in RNA alignments: Physical realism improves information measures. Bioinformatics. 2006;22(24):2988-2995. https://doi.org/10.1093/bioinformatics/btl514

Author

Lindgreen, Stinus ; Gardner, Paul Phillip ; Krogh, Anders. / Measuring covariation in RNA alignments: Physical realism improves information measures. In: Bioinformatics. 2006 ; Vol. 22, No. 24. pp. 2988-2995.

Bibtex

@article{c1d2ca207e5111dcbee902004c4f4f50,
title = "Measuring covariation in RNA alignments: Physical realism improves information measures",
abstract = "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 https://www.binf.ku.dk/Stinus_covariation Contact: stinus@binf.ku.dk Supplementary information: Supplementary data are available at Bioinformatics online. ",
author = "Stinus Lindgreen and Gardner, {Paul Phillip} and Anders Krogh",
year = "2006",
doi = "10.1093/bioinformatics/btl514",
language = "English",
volume = "22",
pages = "2988--2995",
journal = "Computer Applications in the Biosciences",
issn = "1471-2105",
publisher = "Oxford University Press",
number = "24",

}

RIS

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 https://www.binf.ku.dk/Stinus_covariation Contact: stinus@binf.ku.dk 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 https://www.binf.ku.dk/Stinus_covariation Contact: stinus@binf.ku.dk Supplementary information: Supplementary data are available at Bioinformatics online.

U2 - 10.1093/bioinformatics/btl514

DO - 10.1093/bioinformatics/btl514

M3 - Journal article

C2 - 17038338

VL - 22

SP - 2988

EP - 2995

JO - Computer Applications in the Biosciences

JF - Computer Applications in the Biosciences

SN - 1471-2105

IS - 24

ER -

ID: 1337708