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PON-P: Integrated Predictor for Pathogenicity of Missense Variants

Olatubosun, Ayodeji; Valiaho, Jouni; Harkonen, Jani; Thusberg, Janita and Vihinen, Mauno LU (2012) In Human Mutation 33(8). p.1166-1174
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... (More)
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. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
variation tolerance prediction, methods integration, consensus, prediction, classification with reject option
in
Human Mutation
volume
33
issue
8
pages
1166 - 1174
publisher
John Wiley & Sons
external identifiers
  • wos:000306375800006
  • scopus:84863875423
ISSN
1059-7794
DOI
10.1002/humu.22102
language
English
LU publication?
yes
id
76e475dc-5824-4990-b6df-ad8347c318ef (old id 2991563)
date added to LUP
2012-09-03 07:16:48
date last changed
2017-10-01 03:04:56
@article{76e475dc-5824-4990-b6df-ad8347c318ef,
  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.},
  author       = {Olatubosun, Ayodeji and Valiaho, Jouni and Harkonen, Jani and Thusberg, Janita and Vihinen, Mauno},
  issn         = {1059-7794},
  keyword      = {variation tolerance prediction,methods integration,consensus,prediction,classification with reject option},
  language     = {eng},
  number       = {8},
  pages        = {1166--1174},
  publisher    = {John Wiley & Sons},
  series       = {Human Mutation},
  title        = {PON-P: Integrated Predictor for Pathogenicity of Missense Variants},
  url          = {http://dx.doi.org/10.1002/humu.22102},
  volume       = {33},
  year         = {2012},
}