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Variation Interpretation Predictors : Principles, Types, Performance, and Choice

Niroula, Abhishek LU and Vihinen, Mauno LU (2016) In Human Mutation 37(6). p.579-597
Abstract

Next-generation sequencing methods have revolutionized the speed of generating variation information. Sequence data have a plethora of applications and will increasingly be used for disease diagnosis. Interpretation of the identified variants is usually not possible with experimental methods. This has caused a bottleneck that many computational methods aim at addressing. Fast and efficient methods for explaining the significance and mechanisms of detected variants are required for efficient precision/personalized medicine. Computational prediction methods have been developed in three areas to address the issue. There are generic tolerance (pathogenicity) predictors for filtering harmful variants. Gene/protein/disease-specific tools are... (More)

Next-generation sequencing methods have revolutionized the speed of generating variation information. Sequence data have a plethora of applications and will increasingly be used for disease diagnosis. Interpretation of the identified variants is usually not possible with experimental methods. This has caused a bottleneck that many computational methods aim at addressing. Fast and efficient methods for explaining the significance and mechanisms of detected variants are required for efficient precision/personalized medicine. Computational prediction methods have been developed in three areas to address the issue. There are generic tolerance (pathogenicity) predictors for filtering harmful variants. Gene/protein/disease-specific tools are available for some applications. Mechanism and effect-specific computer programs aim at explaining the consequences of variations. Here, we discuss the different types of predictors and their applications. We review available variation databases and prediction methods useful for variation interpretation. We discuss how the performance of methods is assessed and summarize existing assessment studies. A brief introduction is provided to the principles of the methods developed for variation interpretation as well as guidelines for how to choose the optimal tools and where the field is heading in the future.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Computational tools, Mutation effect prediction, Prediction methods, Variation effect, Variation interpretation, Variation prediction
in
Human Mutation
volume
37
issue
6
pages
19 pages
publisher
John Wiley & Sons
external identifiers
  • Scopus:84963537695
ISSN
1059-7794
DOI
10.1002/humu.22987
language
English
LU publication?
yes
id
b65599a4-be5d-4340-8ea9-9b7b8ddffd5f
date added to LUP
2016-05-19 14:50:57
date last changed
2016-10-13 05:09:03
@misc{b65599a4-be5d-4340-8ea9-9b7b8ddffd5f,
  abstract     = {<p>Next-generation sequencing methods have revolutionized the speed of generating variation information. Sequence data have a plethora of applications and will increasingly be used for disease diagnosis. Interpretation of the identified variants is usually not possible with experimental methods. This has caused a bottleneck that many computational methods aim at addressing. Fast and efficient methods for explaining the significance and mechanisms of detected variants are required for efficient precision/personalized medicine. Computational prediction methods have been developed in three areas to address the issue. There are generic tolerance (pathogenicity) predictors for filtering harmful variants. Gene/protein/disease-specific tools are available for some applications. Mechanism and effect-specific computer programs aim at explaining the consequences of variations. Here, we discuss the different types of predictors and their applications. We review available variation databases and prediction methods useful for variation interpretation. We discuss how the performance of methods is assessed and summarize existing assessment studies. A brief introduction is provided to the principles of the methods developed for variation interpretation as well as guidelines for how to choose the optimal tools and where the field is heading in the future.</p>},
  author       = {Niroula, Abhishek and Vihinen, Mauno},
  issn         = {1059-7794},
  keyword      = {Computational tools,Mutation effect prediction,Prediction methods,Variation effect,Variation interpretation,Variation prediction},
  language     = {eng},
  month        = {06},
  number       = {6},
  pages        = {579--597},
  publisher    = {ARRAY(0x637c6f0)},
  series       = {Human Mutation},
  title        = {Variation Interpretation Predictors : Principles, Types, Performance, and Choice},
  url          = {http://dx.doi.org/10.1002/humu.22987},
  volume       = {37},
  year         = {2016},
}