Simultaneous alignment and structure prediction of RNAs are three input sequences better than two?

Abstract

Comparative RNA sequence analyses have contributed remarkably accurate predictions. The recent determination of the 30S and 50S ribosomal subunits brought more supporting evidence. Several inference tools are combining free-energy minimisation and comparative analysis to improve the quality of secondary structure predictions. Using many input sequences should improve the accuracy, reduce the likelihood that bad predictions are made, but also lower the sensitivity. To investigate these claims, we have extended the software system Dynalign to use three input sequences, rather than two, and tested our algorithm with 10 tRNAs and 13 5S rRNAs. The following hypotheses were tested: 1) the use of three input sequences improves the average accuracy compared to predictions based on two input sequences. Also, it should be less likely that all three input sequences simultaneously fold into a bad free-energy minimum compared to predictions based on two sequences, consequently, 2) the worse prediction (minimum accuracy) for any sequence should be more accurate when three input sequences are used rather than two. Finally, the consensus structure of three sequences is probably less representative of the individual sequences. 3) Therefore, the average coverage should be less. © Springer-Verlag Berlin Heidelberg 2005.