ArticleNucleic acids research1996
Logitlinear models for the prediction of splice sites in plant pre-mRNA sequences.
Article in Nucleic acids research, 1996. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Linkage mapping of putative regulator genes of barley grain development characterized by expression profiling.BMC plant biology · 2009Article
- Method of predicting splice sites based on signal interactions.Biology direct · 2006Article
- Structural and functional analysis of rice genome.Journal of genetics · 2004Review
- GénoPlante-Info (GPI): a collection of databases and bioinformatics resources for plant genomics.Nucleic acids research · 2003Article
- Current methods of gene prediction, their strengths and weaknesses.Nucleic acids research · 2002Review
- Molecular characterization of a mutable pigmentation phenotype and isolation of the first active transposable element from Sorghum bicolor.Proceedings of the National Academy of Sciences of the United States of America · 1999Article
- Analysis of donor splice sites in different eukaryotic organisms.Journal of molecular evolution · 1997Article
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Authors and funding
5 authors.
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Abstract
Pre-mRNA splicing in plants, while generally similar to the processes in vertebrates and yeast, is thought to involve plant specific cis-acting elements. Both monocot and dicot introns are typically strongly enriched in U nucleotides, and AU- or U-rich segments are thought to be involved in intron recognition, splice site selection, and splicing efficiency. We have applied logitlinear models to find optimal combinations of splice site variables for the purpose of separating true splice sites from a large excess of potential sites. It is shown that plant splice site prediction from sequence inspection is greatly improved when compositional contrast between exons and introns is considered in addition to degree of matching to the splice site consensus (signal quality). The best model involves subclassification of splice sites according to the identity of the base immediately upstream of the GU and AG signals and gives substantial performance gains compared with conventional profile methods.
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Registered trials
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