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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3634660
- author
- Thusberg, Janita
; Olatubosun, Ayodeji
and Vihinen, Mauno
LU
- publishing date
- 2011
- 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 Inc.
- 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
- 2016-04-04 07:57:38
- date last changed
- 2022-04-23 08:45:19
@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}},
keywords = {{Computational Biology: methods}},
language = {{eng}},
number = {{4}},
pages = {{358--368}},
publisher = {{John Wiley & Sons Inc.}},
series = {{Human Mutation}},
title = {{Performance of mutation pathogenicity prediction methods on missense variants.}},
url = {{http://dx.doi.org/10.1002/humu.21445}},
doi = {{10.1002/humu.21445}},
volume = {{32}},
year = {{2011}},
}