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Automatic detection of exonic splicing enhancers (ESEs) using SVMs.

Mersch B, Gepperth A, Suhai S, Hotz-Wagenblatt A - BMC Bioinformatics (2008)

Bottom Line: Using SVMs with the combined oligo kernel yields a high accuracy of about 90 percent and well interpretable parameters.The motif-oriented data-extraction method seems to produce consistent training and test data leading to good classification rates and thus allows verification of potential ESE motifs.The best results were obtained using an SVM with the combined oligo kernel, while oligo kernels with oligomers of a certain length could be used to extract relevant features.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Molecular Biophysics, German Cancer Research Center DKFZ, Im Neuenheimer Feld 580, Heidelberg, Germany. b.mersch@dkfz.de

ABSTRACT

Background: Exonic splicing enhancers (ESEs) activate nearby splice sites and promote the inclusion (vs. exclusion) of exons in which they reside, while being a binding site for SR proteins. To study the impact of ESEs on alternative splicing it would be useful to have a possibility to detect them in exons. Identifying SR protein-binding sites in human DNA sequences by machine learning techniques is a formidable task, since the exon sequences are also constrained by their functional role in coding for proteins.

Results: The choice of training examples needed for machine learning approaches is difficult since there are only few exact locations of human ESEs described in the literature which could be considered as positive examples. Additionally, it is unclear which sequences are suitable as negative examples. Therefore, we developed a motif-oriented data-extraction method that extracts exon sequences around experimentally or theoretically determined ESE patterns. Positive examples are restricted by heuristics based on known properties of ESEs, e.g. location in the vicinity of a splice site, whereas negative examples are taken in the same way from the middle of long exons. We show that a suitably chosen SVM using optimized sequence kernels (e.g., combined oligo kernel) can extract meaningful properties from these training examples. Once the classifier is trained, every potential ESE sequence can be passed to the SVM for verification. Using SVMs with the combined oligo kernel yields a high accuracy of about 90 percent and well interpretable parameters.

Conclusion: The motif-oriented data-extraction method seems to produce consistent training and test data leading to good classification rates and thus allows verification of potential ESE motifs. The best results were obtained using an SVM with the combined oligo kernel, while oligo kernels with oligomers of a certain length could be used to extract relevant features.

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Receiver operating characteristics (ROC) for the classifiers. Median ROC curves of the classifiers based on 50 trails are shown.
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Figure 4: Receiver operating characteristics (ROC) for the classifiers. Median ROC curves of the classifiers based on 50 trails are shown.

Mentions: In Figure 4, the receiver operating characteristics (ROCs) of the classifiers are shown. For the SVMs, the curves were obtained by simply varying the threshold parameter b [26]. For the Markov chain model, a threshold parameter b was introduced and adjusted, that is, a sequence was classified based on the sign of ln (s) - ln (s) + b. Each curve in Figure 4 corresponds to the median of the 50 trials (similar to the attainment surfaces described in [27]). The superior performance of the SVM with combined oligo kernel was also supported by the receiver operating characteristics in Figure 4, while the Markov chain model showed the worst performance. The SVM with 6-mer oligo kernel performed only slightly worse than the SVM with combined oligo kernel indicating that the hexamers are important for this classification problem.


Automatic detection of exonic splicing enhancers (ESEs) using SVMs.

Mersch B, Gepperth A, Suhai S, Hotz-Wagenblatt A - BMC Bioinformatics (2008)

Receiver operating characteristics (ROC) for the classifiers. Median ROC curves of the classifiers based on 50 trails are shown.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2567995&req=5

Figure 4: Receiver operating characteristics (ROC) for the classifiers. Median ROC curves of the classifiers based on 50 trails are shown.
Mentions: In Figure 4, the receiver operating characteristics (ROCs) of the classifiers are shown. For the SVMs, the curves were obtained by simply varying the threshold parameter b [26]. For the Markov chain model, a threshold parameter b was introduced and adjusted, that is, a sequence was classified based on the sign of ln (s) - ln (s) + b. Each curve in Figure 4 corresponds to the median of the 50 trials (similar to the attainment surfaces described in [27]). The superior performance of the SVM with combined oligo kernel was also supported by the receiver operating characteristics in Figure 4, while the Markov chain model showed the worst performance. The SVM with 6-mer oligo kernel performed only slightly worse than the SVM with combined oligo kernel indicating that the hexamers are important for this classification problem.

Bottom Line: Using SVMs with the combined oligo kernel yields a high accuracy of about 90 percent and well interpretable parameters.The motif-oriented data-extraction method seems to produce consistent training and test data leading to good classification rates and thus allows verification of potential ESE motifs.The best results were obtained using an SVM with the combined oligo kernel, while oligo kernels with oligomers of a certain length could be used to extract relevant features.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Molecular Biophysics, German Cancer Research Center DKFZ, Im Neuenheimer Feld 580, Heidelberg, Germany. b.mersch@dkfz.de

ABSTRACT

Background: Exonic splicing enhancers (ESEs) activate nearby splice sites and promote the inclusion (vs. exclusion) of exons in which they reside, while being a binding site for SR proteins. To study the impact of ESEs on alternative splicing it would be useful to have a possibility to detect them in exons. Identifying SR protein-binding sites in human DNA sequences by machine learning techniques is a formidable task, since the exon sequences are also constrained by their functional role in coding for proteins.

Results: The choice of training examples needed for machine learning approaches is difficult since there are only few exact locations of human ESEs described in the literature which could be considered as positive examples. Additionally, it is unclear which sequences are suitable as negative examples. Therefore, we developed a motif-oriented data-extraction method that extracts exon sequences around experimentally or theoretically determined ESE patterns. Positive examples are restricted by heuristics based on known properties of ESEs, e.g. location in the vicinity of a splice site, whereas negative examples are taken in the same way from the middle of long exons. We show that a suitably chosen SVM using optimized sequence kernels (e.g., combined oligo kernel) can extract meaningful properties from these training examples. Once the classifier is trained, every potential ESE sequence can be passed to the SVM for verification. Using SVMs with the combined oligo kernel yields a high accuracy of about 90 percent and well interpretable parameters.

Conclusion: The motif-oriented data-extraction method seems to produce consistent training and test data leading to good classification rates and thus allows verification of potential ESE motifs. The best results were obtained using an SVM with the combined oligo kernel, while oligo kernels with oligomers of a certain length could be used to extract relevant features.

Show MeSH