Measuring covariation in RNA alignments: physical realism improves information measures

Bioinformatics. 2006 Dec 15;22(24):2988-95. doi: 10.1093/bioinformatics/btl514. Epub 2006 Oct 12.


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

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Conserved Sequence / genetics
  • Evolution, Molecular*
  • Genetic Variation / genetics
  • Information Theory
  • Nucleic Acid Conformation
  • RNA / chemistry*
  • RNA / genetics*
  • Sequence Alignment / methods*
  • Sequence Analysis, RNA / methods*
  • Sequence Homology, Nucleic Acid
  • Structure-Activity Relationship


  • RNA