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Considerations in the identification of functional RNA structural elements in genomic alignments
Tomas Babak1, Benjamin J Blencowe, Timothy R Hughes
1Banting and Best Department of Medical Research, Donnelly Centre for Cellular and Biomolecular Research, 160 College St, Toronto, ON M5S 3E1 Canada. tomas.babak@utoronto.ca <tomas.babak@utoronto.ca>
BMC Bioinformatics
|February 1, 2007
Summary
Developing accurate methods for identifying noncoding RNA (ncRNA) is challenging. A new shuffling algorithm revealed high false-positive rates for current tools, but still found significant ncRNA signals in 3' UTRs and selection against them in coding regions.
Area of Science:
- Genomics
- Bioinformatics
- Molecular Biology
Background:
- Identifying functional noncoding RNA (ncRNA) in genome sequences is complex.
- Current ncRNA prediction tools rely on thermodynamic stability, conservation, or covariance, but their precision is not well-assessed.
- High false-positive rates in genomic-scale analyses can lead to numerous inaccurate discoveries.
Purpose of the Study:
- To develop and apply a novel shuffling algorithm for assessing the precision and recall of ncRNA search tools.
- To evaluate the performance of six different ncRNA prediction tools using a rigorous negative-control strategy.
- To identify global trends in ncRNA prediction scores across different genomic regions.
Main Methods:
- Developed the shuffle-pair.pl algorithm to preserve dinucleotide frequency, gaps, and local conservation in sequence alignments.
- Assessed six ncRNA search tools (MSARI, QRNA, ddbRNA, RNAz, Evofold, thermodynamic stability) on 3046 known ncRNA alignments.
- Compared prediction scores of real sequences against shuffled sequences in UTRs, introns, intergenic, and coding regions.
Main Results:
- Preserving dinucleotide content in shuffling significantly increased estimated false-positive rates for ncRNA elements compared to mononucleotide shuffling.
- Covariance-based tools did not outperform simple thermodynamic scoring on pairwise alignments.
- ncRNA prediction scores were significantly higher in real sequences than shuffled sequences for UTRs, introns, and intergenic regions, but lower for coding sequences.
Conclusions:
- Accurate prediction of novel RNA structural elements remains a significant challenge, particularly developing negative controls for multiple alignments.
- Observed trends in predicted ncRNA distributions across genomic features are biologically meaningful.
- Evidence supports the presence of secondary structural elements in many 3' UTRs and evolutionary selection against them in coding regions.
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