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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.

SUBMITTER: Washietl S

PROVIDER: S-EPMC548974 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundComputational prediction of noncoding RNAs (ncRNAs) is an important task in the post-genomic era. One common approach is to utilize the profile information contained in alignment data rather than single sequences. However, this strategy involves the possibility that the quality of input alignments can influence the performance of prediction methods. Therefore, the evaluation of the robustness against alignment errors is necessary as well as the development of accurate prediction methods.ResultsWe describe a new method, called Profile BPLA kernel, which predicts ncRNAs from alignment data in combination with support vector machines (SVMs). Profile BPLA kernel is an extension of base-pairing profile local alignment (BPLA) kernel which we previously developed for the prediction from single sequences. By utilizing the profile information of alignment data, the proposed kernel can achieve better accuracy than the original BPLA kernel. We show that Profile BPLA kernel outperforms the existing prediction methods which also utilize the profile information using the high-quality structural alignment dataset. In addition to these standard benchmark tests, we extensively evaluate the robustness of Profile BPLA kernel against errors in input alignments. We consider two different types of error: first, that all sequences in an alignment are actually ncRNAs but are aligned ignoring their secondary structures; second, that an alignment contains unrelated sequences which are not ncRNAs but still aligned. In both cases, the effects on the performance of Profile BPLA kernel are surprisingly small. Especially for the latter case, we demonstrate that Profile BPLA kernel is more robust compared to the existing prediction methods.ConclusionsProfile BPLA kernel provides a promising way for identifying ncRNAs from alignment data. It is more accurate than the existing prediction methods, and can keep its performance under the practical situations in which the quality of input alignments is not necessarily high.

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Fast and reliable prediction of noncoding RNAs.

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