Predicting functional effect of human missense mutations

Poster (2013)

Authors

B.A. van den Berg Netherlands Bioinformatics Centre, Nijmegen, Kluyver Centre for Genomics of Industrial Fermentation, Pattern Recognition and Bioinformatics
JM Thornton European Bioinformatics Institute, Cambridge
MJT Reinders Pattern Recognition and Bioinformatics, Kluyver Centre for Genomics of Industrial Fermentation, Netherlands Bioinformatics Centre, Nijmegen
Dick De Ridder Netherlands Bioinformatics Centre, Nijmegen, Pattern Recognition and Bioinformatics, Kluyver Centre for Genomics of Industrial Fermentation
TAP Beer European Bioinformatics Institute, Cambridge
Research Group
Pattern Recognition and Bioinformatics
Copyright: © 2013 B.A. van den Berg, JM Thornton, M.J.T. Reinders, D. de Ridder, TAP Beer
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Published Date

2013

Language

English

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Research Group

Pattern Recognition and Bioinformatics

Abstract

Our aim is to prioritize human missense mutations by their probability of being disease causing. Such a computational method could be used to obtain a reduced set of mutations with a relatively large fraction of disease related mutations, thereby aiding in the search for this type of mutation within a large mutation set.

Whereas a range of methods is available for this purpose, only few employ the availability of the 1000G data to obtain a set of neutral mutations. The novelty of our approach is the use of separate classifiers that were trained on a subset of mutations from one amino acid to any other amino acid. The combined performance of these classifiers show an improved performance compared to the often used prediction method PolyPhen2.

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