Fast and reliable prediction of noncoding RNAs

Abstract

We report an efficient method for detecting functional RNAs. The approach, which combines comparative sequence analysis and structure prediction, already has yielded excellent results for a small number of aligned sequences and is suitable for large-scale genomic screens. It consists of two basic components: (i) a measure for RNA secondary structure conservation based on computing a consensus secondary structure, and (ii) a measure for thermodynamic stability, which, in the spirit of a z score, is normalized with respect to both sequence length and base composition but can be calculated without sampling from shuffled sequences. Functional RNA secondary structures can be identified in multiple sequence alignments with high sensitivity and high specificity. We demonstrate that this approach is not only much more accurate than previous methods but also significantly faster. The method is implemented in the program rnaz, which can be downloaded from www.tbi.univie.ac.at/~wash/RNAz. We screened all alignments of length n > or = 50 in the Comparative Regulatory Genomics database, which compiles conserved noncoding elements in upstream regions of orthologous genes from human, mouse, rat, Fugu, and zebrafish. We recovered all of the known noncoding RNAs and cis-acting elements with high significance and found compelling evidence for many other conserved RNA secondary structures not described so far to our knowledge.

Fig. 1.

z scores calculated by SVM regression in comparison with z scores determined from 1,000 random samples for each data point. As test sequences we chose 100 sequences from random locations in the human genome and 100 known ncRNAs from the Rfam database (31). (Upper) Correlation of z scores from two independent samplings (mean squared error: 0.00990). (Lower) Correlation of calculated z scores and sampled z scores (mean squared error: 0.00998)

Fig. 2.

Classification based on z scores and SCI by using a SVM. Alignments of tRNAs and 5S rRNAs with two to four sequences per alignment and mean pairwise identities between 60% and 90% are shown. Green circles represent native alignments, and red crosses represent shuffled random controls. The background color ranging from red to green indicates the RNA class probability for different regions of the z–SCI plane.