ArticleBMC bioinformatics2006
Impact of RNA structure on the prediction of donor and acceptor splice sites.
Article in BMC bioinformatics, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Improved recognition of splice sites in3 Biotech · 2021Article
- A high-performance approach for predicting donor splice sites based on short window size and imbalanced large samples.Biology direct · 2019Article
- Knockdown and replacement therapy mediated by artificial mirtrons in spinocerebellar ataxia 7.Nucleic acids research · 2017Article
- Parallel computation of genome-scale RNA secondary structure to detect structural constraints on human genome.BMC bioinformatics · 2016Article
- RNA secondary structure mediates alternative 3'ss selection in Saccharomyces cerevisiae.RNA (New York, N.Y.) · 2012Article
- Fast splice site detection using information content and feature reduction.BMC bioinformatics · 2008Article
- Genomic mid-range inhomogeneity correlates with an abundance of RNA secondary structures.BMC genomics · 2008Article
- Splice site identification using probabilistic parameters and SVM classification.BMC bioinformatics · 2006Article
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Authors and funding
4 authors.
Funding
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Abstract
backgroundgene identification in genomic DNA sequences by computational methods has become an important task in bioinformatics and computational gene prediction tools are now essential components of every genome sequencing project. Prediction of splice sites is a key step of all gene structural prediction algorithms.
resultswe sought the role of mRNA secondary structures and their information contents for five vertebrate and plant splice site datasets. We selected 900-nucleotide sequences centered at each (real or decoy) donor and acceptor sites, and predicted their corresponding RNA structures by Vienna software. Then, based on whether the nucleotide is in a stem or not, the conventional four-letter nucleotide alphabet was translated into an eight-letter alphabet. Zero-, first- and second-order Markov models were selected as the signal detection methods. It is shown that applying the eight-letter alphabet compared to the four-letter alphabet considerably increases the accuracy of both donor and acceptor site predictions in case of higher order Markov models.
conclusionOur results imply that RNA structure contains important data and future gene prediction programs can take advantage of such information.
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