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Genomic mid-range inhomogeneity correlates with an abundance of RNA secondary structures.

ABSTRACT: BACKGROUND: Genomes possess different levels of non-randomness, in particular, an inhomogeneity in their nucleotide composition. Inhomogeneity is manifest from the short-range where neighboring nucleotides influence the choice of base at a site, to the long-range, commonly known as isochores, where a particular base composition can span millions of nucleotides. A separate genomic issue that has yet to be thoroughly elucidated is the role that RNA secondary structure (SS) plays in gene expression. RESULTS: We present novel data and approaches that show that a mid-range inhomogeneity (~30 to 1000 nt) not only exists in mammalian genomes but is also significantly associated with strong RNA SS. A whole-genome bioinformatics investigation of local SS in a set of 11,315 non-redundant human pre-mRNA sequences has been carried out. Four distinct components of these molecules (5'-UTRs, exons, introns and 3'-UTRs) were considered separately, since they differ in overall nucleotide composition, sequence motifs and periodicities. For each pre-mRNA component, the abundance of strong local SS (< -25 kcal/mol) was a factor of two to ten greater than a random expectation model. The randomization process preserves the short-range inhomogeneity of the corresponding natural sequences, thus, eliminating short-range signals as possible contributors to any observed phenomena. CONCLUSION: We demonstrate that the excess of strong local SS in pre-mRNAs is linked to the little explored phenomenon of genomic mid-range inhomogeneity (MRI). MRI is an interdependence between nucleotide choice and base composition over a distance of 20-1000 nt. Additionally, we have created a public computational resource to support further study of genomic MRI.

SUBMITTER: Bechtel JM

PROVIDER: S-EPMC2442090 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature

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Genomic mid-range inhomogeneity correlates with an abundance of RNA secondary structures.

Bechtel Jason M JM Wittenschlaeger Thomas T Dwyer Trisha T Song Jun J Arunachalam Sasi S Ramakrishnan Sadeesh K SK Shepard Samuel S Fedorov Alexei A

BMC genomics 20080612

<h4>Background</h4>Genomes possess different levels of non-randomness, in particular, an inhomogeneity in their nucleotide composition. Inhomogeneity is manifest from the short-range where neighboring nucleotides influence the choice of base at a site, to the long-range, commonly known as isochores, where a particular base composition can span millions of nucleotides. A separate genomic issue that has yet to be thoroughly elucidated is the role that RNA secondary structure (SS) plays in gene exp ...[more]

PMID: 18549495

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Project description:BackgroundMid-range inhomogeneity or MRI is the significant enrichment of particular nucleotides in genomic sequences extending from 30 up to several thousands of nucleotides. The best-known manifestation of MRI is CpG islands representing CG-rich regions. Recently it was demonstrated that MRI could be observed not only for G+C content but also for all other nucleotide pairings (e.g. A+G and G+T) as well as for individual bases. Various types of MRI regions are 4-20 times enriched in mammalian genomes compared to their occurrences in random models.ResultsThis paper explores how different types of mutations change MRI regions. Human, chimpanzee and Macaca mulatta genomes were aligned to study the projected effects of substitutions and indels on human sequence evolution within both MRI regions and control regions of average nucleotide composition. Over 18.8 million fixed point substitutions, 3.9 million SNPs, and indels spanning 6.9 Mb were procured and evaluated in human. They include 1.8 Mb substitutions and 1.9 Mb indels within MRI regions. Ancestral and mutant (derived) alleles for substitutions have been determined. Substitutions were grouped according to their fixation within human populations: fixed substitutions (from the human-chimp-macaca alignment), major SNPs (> 80% mutant allele frequency within humans), medium SNPs (20% - 80% mutant allele frequency), minor SNPs (3% - 20%), and rare SNPs (<3%). Data on short (< 3 bp) and medium-length (3 - 50 bp) insertions and deletions within MRI regions and appropriate control regions were analyzed for the effect of indels on the expansion or diminution of such regions as well as on changing nucleotide composition.ConclusionMRI regions have comparable levels of de novo mutations to the control genomic sequences with average base composition. De novo substitutions rapidly erode MRI regions, bringing their nucleotide composition toward genome-average levels. However, those substitutions that favor the maintenance of MRI properties have a higher chance to spread through the entire population. Indels have a clear tendency to maintain MRI features yet they have a smaller impact than substitutions. All in all, the observed fixation bias for mutations helps to preserve MRI regions during evolution.

Project description:BackgroundAccurate structure prediction methods play an important role for the understanding of RNA function. Energy-based, pseudoknot-free secondary structure prediction is one of the most widely used and versatile approaches, and improved methods for this task have received much attention over the past five years. Despite the impressive progress that as been achieved in this area, existing evaluations of the prediction accuracy achieved by various algorithms do not provide a comprehensive, statistically sound assessment. Furthermore, while there is increasing evidence that no prediction algorithm consistently outperforms all others, no work has been done to exploit the complementary strengths of multiple approaches.ResultsIn this work, we present two contributions to the area of RNA secondary structure prediction. Firstly, we use state-of-the-art, resampling-based statistical methods together with a previously published and increasingly widely used dataset of high-quality RNA structures to conduct a comprehensive evaluation of existing RNA secondary structure prediction procedures. The results from this evaluation clarify the performance relationship between ten well-known existing energy-based pseudoknot-free RNA secondary structure prediction methods and clearly demonstrate the progress that has been achieved in recent years. Secondly, we introduce AveRNA, a generic and powerful method for combining a set of existing secondary structure prediction procedures into an ensemble-based method that achieves significantly higher prediction accuracies than obtained from any of its component procedures.ConclusionsOur new, ensemble-based method, AveRNA, improves the state of the art for energy-based, pseudoknot-free RNA secondary structure prediction by exploiting the complementary strengths of multiple existing prediction procedures, as demonstrated using a state-of-the-art statistical resampling approach. In addition, AveRNA allows an intuitive and effective control of the trade-off between false negative and false positive base pair predictions. Finally, AveRNA can make use of arbitrary sets of secondary structure prediction procedures and can therefore be used to leverage improvements in prediction accuracy offered by algorithms and energy models developed in the future. Our data, MATLAB software and a web-based version of AveRNA are publicly available at http://www.cs.ubc.ca/labs/beta/Software/AveRNA.

Dataset Information

Genomic mid-range inhomogeneity correlates with an abundance of RNA secondary structures.

Publications

Genomic mid-range inhomogeneity correlates with an abundance of RNA secondary structures.

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