Dataset Information

Computational assessment of feature combinations for pathogenic variant prediction.

ABSTRACT:

Background

Although several methods have been proposed for predicting the effects of genetic variants and their role in disease, it is still a challenge to identify and prioritize pathogenic variants within sequencing studies.

Methods

Here, we compare different variant and gene-specific features as well as existing methods and investigate their best combination to explore potential performance gains.

Results

We found that combining the number of "biological process" Gene Ontology annotations of a gene with the methods PON-P2, and PROVEAN significantly improves prediction of pathogenic variants, outperforming all individual methods. A comprehensive analysis of the Gene Ontology feature suggests that it is not a variant-dependent annotation bias but reflects the multifunctional nature of disease genes. Furthermore, we identified a set of difficult variants where different prediction methods fail.

Conclusion

Existing pathogenicity prediction methods can be further improved.

SUBMITTER: Konig E

PROVIDER: S-EPMC4947862 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Publications

Computational assessment of feature combinations for pathogenic variant prediction.

König Eva E Rainer Johannes J Domingues Francisco S FS

Molecular genetics & genomic medicine 20160314 4

<h4>Background</h4>Although several methods have been proposed for predicting the effects of genetic variants and their role in disease, it is still a challenge to identify and prioritize pathogenic variants within sequencing studies.<h4>Methods</h4>Here, we compare different variant and gene-specific features as well as existing methods and investigate their best combination to explore potential performance gains.<h4>Results</h4>We found that combining the number of "biological process" Gene On ...[more]

PMID: 27468419

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Project description:Deoxyribonucleic acid replication is one of the most crucial tasks taking place in the cell, and it has to be precisely regulated. This process is initiated in the replication origins (ORIs), and thus it is essential to identify such sites for a deeper understanding of the cellular processes and functions related to the regulation of gene expression. Considering the important tasks performed by ORIs, several experimental and computational approaches have been developed in the prediction of such sites. However, existing computational predictors for ORIs have certain curbs, such as building only single-feature encoding models, limited systematic feature engineering efforts and failure to validate model robustness. Hence, we developed a novel species-specific yeast predictor called yORIpred that accurately identify ORIs in the yeast genomes. To develop yORIpred, we first constructed optimal 40 baseline models by exploring eight different sequence-based encodings and five different machine learning classifiers. Subsequently, the predicted probability of 40 models was considered as the novel feature vector and carried out iterative feature learning approach independently using five different classifiers. Our systematic analysis revealed that the feature representation learned by the support vector machine algorithm (yORIpred) could well discriminate the distribution characteristics between ORIs and non-ORIs when compared with the other four algorithms. Comprehensive benchmarking experiments showed that yORIpred achieved superior and stable performance when compared with the existing predictors on the same training datasets. Furthermore, independent evaluation showcased the best and accurate performance of yORIpred thus underscoring the significance of iterative feature representation. To facilitate the users in obtaining their desired results without undergoing any mathematical, statistical or computational hassles, we developed a web server for the yORIpred predictor, which is available at: http://thegleelab.org/yORIpred.

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Dataset Information

Computational assessment of feature combinations for pathogenic variant prediction.

Background

Methods

Results

Conclusion

Publications

Computational assessment of feature combinations for pathogenic variant prediction.

Similar Datasets

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