Considerations in the identification of functional RNA structural elements in genomic alignments

BackgroundAccurate identification of novel, functional noncoding (nc) RNA features in genome sequence has proven more difficult than for exons. Current algorithms identify and score potential RNA secondary structures on the basis of thermodynamic stability, conservation, and/or covariance in sequence alignments. Neither the algorithms nor the information gained from the individual inputs have been independently assessed. Furthermore, due to issues in modelling background signal, it has been difficult to gauge the precision of these algorithms on a genomic scale, in which even a seemingly small false-positive rate can result in a vast excess of false discoveries.ResultsWe developed a shuffling algorithm, shuffle-pair.pl, that simultaneously preserves dinucleotide frequency, gaps, and local conservation in pairwise sequence alignments. We used shuffle-pair.pl to assess precision and recall of six ncRNA search tools (MSARI, QRNA, ddbRNA, RNAz, Evofold, and several variants of simple thermodynamic stability on a test set of 3046 alignments of known ncRNAs. Relative to mononucleotide shuffling, preservation of dinucleotide content in shuffling the alignments resulted in a drastic increase in estimated false-positive detection rates for ncRNA elements, precluding evaluation of higher order alignments, which cannot not be adequately shuffled maintaining both dinucleotides and alignment structure. On pairwise alignments, none of the covariance-based tools performed markedly better than thermodynamic scoring alone. Although the high false-positive rates call into question the veracity of any individual predicted secondary structural element in our analysis, we nevertheless identified intriguing global trends in human genome alignments. The distribution of ncRNA prediction scores in 75-base windows overlapping UTRs, introns, and intergenic regions analyzed using both thermodynamic stability and EvoFold (which has no thermodynamic component) was significantly higher for real than shuffled sequence, while the distribution for coding sequences was lower than that of corresponding shuffles.ConclusionAccurate prediction of novel RNA structural elements in genome sequence remains a difficult problem, and development of an appropriate negative-control strategy for multiple alignments is an important practical challenge. Nonetheless, the general trends we observed for the distributions of predicted ncRNAs across genomic features are biologically meaningful, supporting the presence of secondary structural elements in many 3' UTRs, and providing evidence for evolutionary selection against secondary structures in coding regions.

[1]  Michael Zuker,et al.  DINAMelt web server for nucleic acid melting prediction , 2005, Nucleic Acids Res..

[2]  Jens Stoye,et al.  Benchmarking tools for the alignment of functional noncoding DNA , 2004, BMC Bioinformatics.

[3]  David Haussler,et al.  Identification and Classification of Conserved RNA Secondary Structures in the Human Genome , 2006, PLoS Comput. Biol..

[4]  D. Haussler,et al.  Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. , 2005, Genome research.

[5]  Peter F Stadler,et al.  Fast and reliable prediction of noncoding RNAs , 2005, Proc. Natl. Acad. Sci. USA.

[6]  W. J. Kent,et al.  BLAT--the BLAST-like alignment tool. , 2002, Genome research.

[7]  N. Sonenberg,et al.  The involvement of mRNA secondary structure in protein synthesis. , 1987, Biochemistry and cell biology = Biochimie et biologie cellulaire.

[8]  A. Krogh,et al.  No evidence that mRNAs have lower folding free energies than random sequences with the same dinucleotide distribution. , 1999, Nucleic acids research.

[9]  Sergey Steinberg,et al.  Compilation of tRNA sequences and sequences of tRNA genes , 2004, Nucleic Acids Res..

[10]  B. Berger,et al.  MSARI: multiple sequence alignments for statistical detection of RNA secondary structure. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[11]  Walter Fontana,et al.  Fast folding and comparison of RNA secondary structures , 1994 .

[12]  N. Sonenberg,et al.  Insertion mutagenesis to increase secondary structure within the 5′ noncoding region of a eukaryotic mRNA reduces translational efficiency , 1985, Cell.

[13]  Colin N. Dewey,et al.  Initial sequencing and comparative analysis of the mouse genome. , 2002 .

[14]  H. Margalit,et al.  Novel small RNA-encoding genes in the intergenic regions of Escherichia coli , 2001, Current Biology.

[15]  Diego di Bernardo,et al.  ddbRNA: detection of conserved secondary structures in multiple alignments , 2003, Bioinform..

[16]  Tatiana A. Tatusova,et al.  NCBI Reference Sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins , 2004, Nucleic Acids Res..

[17]  Mouse Genome Sequencing Consortium.  Initial sequencing and comparative analysis of the mouse genome , 2002, Nature.

[18]  I. Hofacker,et al.  Consensus folding of aligned sequences as a new measure for the detection of functional RNAs by comparative genomics. , 2004, Journal of molecular biology.

[19]  International Human Genome Sequencing Consortium.  Initial sequencing and analysis of the human genome , 2001, Nature.

[20]  Sean R. Eddy,et al.  Rfam: annotating non-coding RNAs in complete genomes , 2004, Nucleic Acids Res..

[21]  P. Stadler,et al.  Mapping of conserved RNA secondary structures predicts thousands of functional noncoding RNAs in the human genome , 2005, Nature Biotechnology.

[22]  I-Min A. Dubchak,et al.  A computational approach to identify genes for functional RNAs in genomic sequences. , 2001, Nucleic acids research.

[23]  C. Burge,et al.  Widespread selection for local RNA secondary structure in coding regions of bacterial genes. , 2003, Genome research.

[24]  S. Eddy,et al.  Computational identification of noncoding RNAs in E. coli by comparative genomics , 2001, Current Biology.

[25]  J. Gorodkin,et al.  Thousands of corresponding human and mouse genomic regions unalignable in primary sequence contain common RNA structure. , 2006, Genome research.

[26]  S. Altschul,et al.  Significance of nucleotide sequence alignments: a method for random sequence permutation that preserves dinucleotide and codon usage. , 1985, Molecular biology and evolution.

[27]  Ivo L. Hofacker,et al.  Vienna RNA secondary structure server , 2003, Nucleic Acids Res..

[28]  S. Eddy.  Computational Genomics of Noncoding RNA Genes , 2002, Cell.

[29]  Elena Rivas,et al.  Noncoding RNA gene detection using comparative sequence analysis , 2001, BMC Bioinformatics.

[30]  Terrence S. Furey,et al.  The UCSC Genome Browser Database: update 2006 , 2005, Nucleic Acids Res..

[31]  David H. Mathews,et al.  Detection of non-coding RNAs on the basis of predicted secondary structure formation free energy change , 2006, BMC Bioinformatics.

[32]  P. Clote,et al.  Structural RNA has lower folding energy than random RNA of the same dinucleotide frequency. , 2005, RNA.

[33]  G. Storz,et al.  Identification of novel small RNAs using comparative genomics and microarrays. , 2001, Genes & development.

[34]  Gary D. Stormo,et al.  Pairwise local structural alignment of RNA sequences with sequence similarity less than 40% , 2005, Bioinform..

[35]  Yi Zhao,et al.  NONCODE: an integrated knowledge database of non-coding RNAs , 2004, Nucleic Acids Res..