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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.

Show MeSH
Oligomer ranking. The ten most important oligomers for discrimination based on trimers, tetramers, pentamers and hexamers are shown. The heights of the bars correlate to the average norm of the corresponding K-mer weight function and was scaled to yield an unit maximum.
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Figure 2: Oligomer ranking. The ten most important oligomers for discrimination based on trimers, tetramers, pentamers and hexamers are shown. The heights of the bars correlate to the average norm of the corresponding K-mer weight function and was scaled to yield an unit maximum.

Mentions: In order to extract the most important oligomers for the kernel-based ESE prediction, the oligomer-specific weight functions of the discriminant were calculated. The ten most important K-mers, K = {3,4,5,6}, were identified and displayed in a bar graph in Figure 2. The height of each bar correlates to the average norm of the corresponding K-mer weight function and was scaled to yield an unit maximum. For the oligomers shown in Figure 2, one can identify a group of motifs which is most prominent. These are the motifs which occur in the purine-rich enhancers, as for example GAGGAG or GAAGAA. These motifs are represented by several of the important oligomers shown in Figure 2.


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

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

Oligomer ranking. The ten most important oligomers for discrimination based on trimers, tetramers, pentamers and hexamers are shown. The heights of the bars correlate to the average norm of the corresponding K-mer weight function and was scaled to yield an unit maximum.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Oligomer ranking. The ten most important oligomers for discrimination based on trimers, tetramers, pentamers and hexamers are shown. The heights of the bars correlate to the average norm of the corresponding K-mer weight function and was scaled to yield an unit maximum.
Mentions: In order to extract the most important oligomers for the kernel-based ESE prediction, the oligomer-specific weight functions of the discriminant were calculated. The ten most important K-mers, K = {3,4,5,6}, were identified and displayed in a bar graph in Figure 2. The height of each bar correlates to the average norm of the corresponding K-mer weight function and was scaled to yield an unit maximum. For the oligomers shown in Figure 2, one can identify a group of motifs which is most prominent. These are the motifs which occur in the purine-rich enhancers, as for example GAGGAG or GAAGAA. These motifs are represented by several of the important oligomers shown in Figure 2.

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