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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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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Dataset Information

Computational assessment of feature combinations for pathogenic variant prediction.

Publications

Computational assessment of feature combinations for pathogenic variant prediction.

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