| 1. |
- Ali, Heidi, et al.
(author)
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Classification of mismatch repair gene missense variants with PON-MMR
- 2012
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In: Human Mutation. - : Hindawi Limited. - 1059-7794. ; 33:4, s. 642-650
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Journal article (peer-reviewed)abstract
- Numerous mismatch repair (MMR) gene variants have been identified in Lynch syndrome and other cancer patients, but knowledge about their pathogenicity is frequently missing. The diagnosis and treatment of patients would benefit from knowing which variants are disease related. Bioinformatic approaches are well suited to the problem and can handle large numbers of cases. Functional effects were revealed based on literature for 168 MMR missense variants. Performance of numerous prediction methods was tested with this dataset. Among the tested tools, only the results of tolerance prediction methods correlated to functional information, however, with poor performance. Therefore, a novel consensus-based predictor was developed. The novel prediction method, pathogenic-or-not mismatch repair (PON-MMR), achieved accuracy of 0.87 and Matthews correlation coefficient of 0.77 on the experimentally verified variants. When applied to 616 MMR cases with unknown effects, 81 missense variants were predicted to be pathogenic and 167 neutral. With PON-MMR, the number of MMR missense variants with unknown effect was reduced by classifying a large number of cases as likely pathogenic or benign. The results can be used, for example, to prioritize cases for experimental studies and assist in the classification of cases. Hum Mutat 33:642650, 2012. (c) 2012 Wiley Periodicals, Inc.
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| 2. |
- Olatubosun, Ayodeji, et al.
(author)
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PON-P: Integrated Predictor for Pathogenicity of Missense Variants
- 2012
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In: Human Mutation. - : Hindawi Limited. - 1059-7794. ; 33:8, s. 1166-1174
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Journal article (peer-reviewed)abstract
- High-throughput sequencing data generation demands the development of methods for interpreting the effects of genomic variants. Numerous computational methods have been developed to assess the impact of variations because experimental methods are unable to cope with both the speed and volume of data generation. To harness the strength of currently available predictors, the Pathogenic-or-Not-Pipeline (PON-P) integrates five predictors to predict the probability that nonsynonymous variations affect protein function and may consequently be disease related. Random forest methodology-based PON-P shows consistently improved performance in cross-validation tests and on independent test sets, providing ternary classification and statistical reliability estimate of results. Applied to missense variants in a melanoma cancer cell line, PON-P predicts variants in 17 genes to affect protein function. Previous studies implicate nine of these genes in the pathogenesis of various forms of cancer. PON-P may thus be used as a first step in screening and prioritizing variants to determine deleterious ones for further experimentation. Hum Mutat 33:1166-1174, 2012. (c) 2012 Wiley Periodicals, Inc.
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