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Using profiles based on nucleotide hydrophobicity to define essential regions for splicing.

ABSTRACT: The splice-site sequences of U2-type introns are highly degenerate, so many different sequences can function as U2-type splice sites. Using our new profiles based on hydrophobicity properties we pointed out specific properties for regions surrounding splice sites. We built a set T of flanking regions of genes with 1-3 introns from 21st and 22nd chromosomes extracted from GenBank to define positions having conserved properties, namely hydrophobicity, that are potentially essential for recognition by spliceosome. GT-AG introns exist in U2 and U12-types. Therefore, intron type cannot be simply determined by the dinucleotide termini. We attempted to distinguish U2 and U12-types introns with help of hydrophobicity profiles on sets of spice sites for U2 or U12-type introns extracted from SpliceRack database. The positions given by our method, which may be important for recognition by spliceosome, were compared to the nucleotide consensus provided by a classical method, Pictogram. We showed that there is a similarity of hydrophobicity profiles inside intron types. On one hand, GT-AG and GC-AG introns belonging to U2-type have resembling hydrophobicity profiles as well as AT-AC and GT-AG introns belonging to U12-type. On the other hand, hydrophobicity profiles of U2 and U12-types GT-AG introns are completely different. We suggest that hydrophobicity profiles facilitate definition of intron type, distinguishing U2 and U12 intron types and can be used to build programs to search splice site and to evaluate their strength. Therefore, our study proves that hydrophobicity profiles are informative method providing insights into mechanisms of splice sites recognition.

SUBMITTER: Boldina G

PROVIDER: S-EPMC2597731 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature

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Using profiles based on nucleotide hydrophobicity to define essential regions for splicing.

Boldina Galina G Ivashchenko Anatoly A Régnier Mireille M

International journal of biological sciences 20081203 1

The splice-site sequences of U2-type introns are highly degenerate, so many different sequences can function as U2-type splice sites. Using our new profiles based on hydrophobicity properties we pointed out specific properties for regions surrounding splice sites. We built a set T of flanking regions of genes with 1-3 introns from 21st and 22nd chromosomes extracted from GenBank to define positions having conserved properties, namely hydrophobicity, that are potentially essential for recognition ...[more]

PMID: 19119309

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Project description:BackgroundMotivated by the increased amount of data on protein-RNA interactions and the availability of complete genome sequences of several organisms, many computational methods have been proposed to predict binding sites in protein-RNA interactions. However, most computational methods are limited to finding RNA-binding sites in proteins instead of protein-binding sites in RNAs. Predicting protein-binding sites in RNA is more challenging than predicting RNA-binding sites in proteins. Recent computational methods for finding protein-binding sites in RNAs have several drawbacks for practical use.ResultsWe developed a new support vector machine (SVM) model for predicting protein-binding regions in mRNA sequences. The model uses sequence profiles constructed from log-odds scores of mono- and di-nucleotides and nucleotide compositions. The model was evaluated by standard 10-fold cross validation, leave-one-protein-out (LOPO) cross validation and independent testing. Since actual mRNA sequences have more non-binding regions than protein-binding regions, we tested the model on several datasets with different ratios of protein-binding regions to non-binding regions. The best performance of the model was obtained in a balanced dataset of positive and negative instances. 10-fold cross validation with a balanced dataset achieved a sensitivity of 91.6%, a specificity of 92.4%, an accuracy of 92.0%, a positive predictive value (PPV) of 91.7%, a negative predictive value (NPV) of 92.3% and a Matthews correlation coefficient (MCC) of 0.840. LOPO cross validation showed a lower performance than the 10-fold cross validation, but the performance remains high (87.6% accuracy and 0.752 MCC). In testing the model on independent datasets, it achieved an accuracy of 82.2% and an MCC of 0.656. Testing of our model and other state-of-the-art methods on a same dataset showed that our model is better than the others.ConclusionsSequence profiles of log-odds scores of mono- and di-nucleotides were much more powerful features than nucleotide compositions in finding protein-binding regions in RNA sequences. But, a slight performance gain was obtained when using the sequence profiles along with nucleotide compositions. These are preliminary results of ongoing research, but demonstrate the potential of our approach as a powerful predictor of protein-binding regions in RNA. The program and supporting data are available at http://bclab.inha.ac.kr/RBPbinding .

Dataset Information

Using profiles based on nucleotide hydrophobicity to define essential regions for splicing.

Publications

Using profiles based on nucleotide hydrophobicity to define essential regions for splicing.

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