Predicting cancer-associated germline variations in proteins.
Pier Luigi Martelli1, Piero Fariselli, Eva Balzani
1Biocomputing Group, *CIRI-Health Science and Technology/Department of Biology, via San Giacomo 9/2, Bologna, Italy.
BMC Genomics
|July 5, 2012
Summary
A new computational method effectively distinguishes cancer-associated genetic variations from those linked to other diseases. Incorporating protein function via Gene Ontology terms significantly improves this classification accuracy.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Existing computational methods classify disease-associated protein variations.
- Technological advancements enable phenotype-specific annotation of variations.
- Distinguishing cancer-related variations from other genetic disorders is a key challenge.
Purpose of the Study:
- To develop and validate a computational method for discriminating cancer-associated genetic variations from those of other genetic disorders.
- To leverage protein function, described by Gene Ontology terms, for improved variant annotation.
Main Methods:
- Implementation of a Support Vector Machine (SVM) based computational method.
- Input features include protein variants and their associated Gene Ontology (GO) terms.
- The method was trained and tested on a dataset of germline variations.
Main Results:
- The SVM method achieved 90% accuracy and a 0.61 Matthews correlation coefficient on 6478 germline variations.
- Sensitivity and specificity for the cancer class were 69% and 66%, respectively.
- The method successfully excluded 96% of somatic cancer-associated variations not in the training set.
Conclusions:
- It is feasible to discriminate cancer-associated germline protein variations from those of other genetic disorders.
- Protein function, as defined by Gene Ontology terms, is a crucial feature for accurate variant annotation.
- This work represents a significant advancement in the annotation of protein variations.
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