A computational procedure for assessing the significance of RNA secondary structure

Chen, Jih-Hsiang; Le, Shu-Yun; Shapiro, Bruce A.; Currey, Kathleen M.; Maizel, Jacob V.

doi:10.1093/bioinformatics/6.1.7

Cited by 39 publications

(32 citation statements)

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“…The problem of finding novel ncRNAs also can rely on predicted folding free energy change. It was hypothesized early that ncRNA sequences have lower folding free energy changes than random sequences (Le et al 1988;Chen et al 1990). This hypothesis proved controversial and one reason for the controversy is whether the correct controls for testing this hypothesis are random sequences with the same nucleotide content or dinucleotide content; this is because the stacking nearest neighbor parameters depend on dinucleotides (Seffens and Digby 1999;Workman and Krogh 1999).…”

Section: Finding Functional Rnamentioning

confidence: 99%

Folding and Finding RNA Secondary Structure

Mathews¹,

Moss²,

Turner³

2010

Cold Spring Harbor Perspectives in Biology

141

119

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SUMMARYOptimal exploitation of the expanding database of sequences requires rapid finding and folding of RNAs. Methods are reviewed that automate folding and discovery of RNAs with algorithms that couple thermodynamics with chemical mapping, NMR, and/or sequence comparison. New functional noncoding RNAs in genome sequences can be found by combining sequence comparison with the assumption that functional noncoding RNAs will have more favorable folding free energies than other RNAs. When a new RNA is discovered, experiments and sequence comparison can restrict folding space so that secondary structure can be rapidly determined with the help of predicted free energies. In turn, secondary structure restricts folding in three dimensions, which allows modeling of three-dimensional structure. An example from a domain of a retrotransposon is described. Discovery of new RNAs and their structures will provide insights into evolution, biology, and design of therapeutics. Applications to studies of evolution are also reviewed.

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Section: Finding Functional Rnamentioning

confidence: 99%

Folding and Finding RNA Secondary Structure

Mathews¹,

Moss²,

Turner³

2010

Cold Spring Harbor Perspectives in Biology

141

119

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“…In analogy to previous work 11- 13 we use a Monte Carlo approach to estimate statistical significance of a MFE. For each alignment we generate 100 random alignments (see next section).…”

Section: Estimation Of Statistical Significancementioning

confidence: 99%

“…[11][12][13] However, efforts to build a general RNA gene finder based on secondary structure prediction failed. Rivas & Eddy had to conclude in an in-depth study on the subject that secondary structure alone is generally not significant enough for the detection of non-coding RNAs.…”

Section: Introductionmentioning

confidence: 99%

Consensus Folding of Aligned Sequences as a New Measure for the Detection of Functional RNAs by Comparative Genomics

Washietl

Hofacker

2004

Journal of Molecular Biology

143

128

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Facing the ever-growing list of newly discovered classes of functional RNAs, it can be expected that further types of functional RNAs are still hidden in recently completed genomes. The computational identification of such RNA genes is, therefore, of major importance. While most known functional RNAs have characteristic secondary structures, their free energies are generally not statistically significant enough to distinguish RNA genes from the genomic background. Additional information is required. Considering the wide availability of new genomic data of closely related species, comparative studies seem to be the most promising approach. Here we show that prediction of consensus structures of aligned sequences can be a significant measure to detect functional RNAs. We report a new method how to test multiple sequence alignments for the existence of an unusually structured and conserved fold. We show for alignments of six types of well known functional RNA that an energy score consisting of free energy and a covariation term significantly improves sensitivity compared to single sequence predictions. We further test our method on a number of non coding RNAs from C. elegans/C. briggsae and seven Saccharomyces species. Most RNAs can be detected with high significance. We provide a Perl implementation which can be readily used to score single alignments and discuss how the methods described here can be extended to allow for efficient genome-wide screens.

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“…This has led to various attempts to predict functional RNAs using predicted secondary structures. It was first suggested by Maizel and co-workers that functional RNA elements should have a more stable secondary structure than expected by chance [231,57]. However, Rivas and Eddy had to conclude in an in-depth study on the subject that thermodynamic stability alone is generally not statistically significant enough for reliable ncRNA detection [342].…”

Section: Novel Ncrnas and Rna Motifsmentioning

confidence: 99%

Evolutionary patterns of non-coding RNAs

Bompfünewerer

Flamm

Fried

et al. 2005

Theory in Biosciences

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A plethora of new functions of non-coding RNAs have been discovered in past few years. In fact, RNA is emerging as the central player in cellular regulation, taking on active roles in multiple regulatory layers from transcription, RNA maturation, and Manuscript January 2005RNA modification to translational regulation. Nevertheless, very little is known about the evolution of this "Modern RNA World" and its components. In this contribution we attempt to provide at least a cursory overview of the diversity of non-coding RNAs and functional RNA motifs in non-translated regions of regular messenger RNAs (mRNAs) with an emphasis on evolutionary questions. This survey is complemented by an in-depth analysis of examples from different classes of RNAs focusing mostly on their evolution in the vertebrate lineage. We present a survey of Y RNA genes in vertebrates, studies of the molecular evolution of the U7 snRNA, the snoRNAs E1/U17, E2, and E3, the Y RNA family, the let-7 microRNA family, and the mRNA-like evf-1 gene. We furthermore discuss the statistical distribution of microRNAs in metazoans, which suggests an explosive increase in the microRNA repertoire in vertebrates. The analysis of the transcription of non-coding RNAs (ncRNAs) suggests that small RNAs in general are genetically mobile in the sense that their association with a hostgene (e.g. when transcribed from introns of a mRNA) can change on evolutionary time scales. The let-7 family demonstrates, that even the mode of transcription (as intron or as exon) can change among paralogous ncRNA.

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A computational procedure for assessing the significance of RNA secondary structure

Cited by 39 publications

References 0 publications

Folding and Finding RNA Secondary Structure

Folding and Finding RNA Secondary Structure

Consensus Folding of Aligned Sequences as a New Measure for the Detection of Functional RNAs by Comparative Genomics

Evolutionary patterns of non-coding RNAs

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