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Performance of mutation pathogenicity prediction methods on missense variants.

Thusberg, Janita; Olatubosun, Ayodeji and Vihinen, Mauno LU (2011) In Human Mutation 32(4). p.358-368
Abstract
Single nucleotide polymorphisms (SNPs) are the most common form of genetic variation in humans. The number of SNPs identified in the human genome is growing rapidly, but attaining experimental knowledge about the possible disease association of variants is laborious and time-consuming. Several computational methods have been developed for the classification of SNPs according to their predicted pathogenicity. In this study, we have evaluated the performance of nine widely used pathogenicity prediction methods available on the Internet. The evaluated methods were MutPred, nsSNPAnalyzer, Panther, PhD-SNP, PolyPhen, PolyPhen2, SIFT, SNAP, and SNPs&GO. The methods were tested with a set of over 40,000 pathogenic and neutral variants. We... (More)
Single nucleotide polymorphisms (SNPs) are the most common form of genetic variation in humans. The number of SNPs identified in the human genome is growing rapidly, but attaining experimental knowledge about the possible disease association of variants is laborious and time-consuming. Several computational methods have been developed for the classification of SNPs according to their predicted pathogenicity. In this study, we have evaluated the performance of nine widely used pathogenicity prediction methods available on the Internet. The evaluated methods were MutPred, nsSNPAnalyzer, Panther, PhD-SNP, PolyPhen, PolyPhen2, SIFT, SNAP, and SNPs&GO. The methods were tested with a set of over 40,000 pathogenic and neutral variants. We also assessed whether the type of original or substituting amino acid residue, the structural class of the protein, or the structural environment of the amino acid substitution, had an effect on the prediction performance. The performances of the programs ranged from poor (MCC 0.19) to reasonably good (MCC 0.65), and the results from the programs correlated poorly. The overall best performing methods in this study were SNPs&GO and MutPred, with accuracies reaching 0.82 and 0.81, respectively. (Less)
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author
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Computational Biology: methods
in
Human Mutation
volume
32
issue
4
pages
358 - 368
publisher
John Wiley & Sons
external identifiers
  • pmid:21412949
  • scopus:79952764520
ISSN
1059-7794
DOI
10.1002/humu.21445
language
English
LU publication?
no
id
7bc810f1-8c65-4100-96c1-820c350ae374 (old id 3634660)
alternative location
http://www.ncbi.nlm.nih.gov/pubmed/21412949?dopt=Abstract
date added to LUP
2013-06-12 20:45:55
date last changed
2017-10-22 04:53:07
@article{7bc810f1-8c65-4100-96c1-820c350ae374,
  abstract     = {Single nucleotide polymorphisms (SNPs) are the most common form of genetic variation in humans. The number of SNPs identified in the human genome is growing rapidly, but attaining experimental knowledge about the possible disease association of variants is laborious and time-consuming. Several computational methods have been developed for the classification of SNPs according to their predicted pathogenicity. In this study, we have evaluated the performance of nine widely used pathogenicity prediction methods available on the Internet. The evaluated methods were MutPred, nsSNPAnalyzer, Panther, PhD-SNP, PolyPhen, PolyPhen2, SIFT, SNAP, and SNPs&GO. The methods were tested with a set of over 40,000 pathogenic and neutral variants. We also assessed whether the type of original or substituting amino acid residue, the structural class of the protein, or the structural environment of the amino acid substitution, had an effect on the prediction performance. The performances of the programs ranged from poor (MCC 0.19) to reasonably good (MCC 0.65), and the results from the programs correlated poorly. The overall best performing methods in this study were SNPs&GO and MutPred, with accuracies reaching 0.82 and 0.81, respectively.},
  author       = {Thusberg, Janita and Olatubosun, Ayodeji and Vihinen, Mauno},
  issn         = {1059-7794},
  keyword      = {Computational Biology: methods},
  language     = {eng},
  number       = {4},
  pages        = {358--368},
  publisher    = {John Wiley & Sons},
  series       = {Human Mutation},
  title        = {Performance of mutation pathogenicity prediction methods on missense variants.},
  url          = {http://dx.doi.org/10.1002/humu.21445},
  volume       = {32},
  year         = {2011},
}