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.
(Less)
- author
- Niroula, Abhishek
LU
and Vihinen, Mauno
LU
- organization
- publishing date
- 2016-06-01
- 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 Inc.
- external identifiers
-
- scopus:84963537695
- pmid:26987456
- wos:000375157300013
- 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
- 2024-02-02 15:16:04
@article{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}},
keywords = {{Computational tools; Mutation effect prediction; Prediction methods; Variation effect; Variation interpretation; Variation prediction}},
language = {{eng}},
month = {{06}},
number = {{6}},
pages = {{579--597}},
publisher = {{John Wiley & Sons Inc.}},
series = {{Human Mutation}},
title = {{Variation Interpretation Predictors : Principles, Types, Performance, and Choice}},
url = {{http://dx.doi.org/10.1002/humu.22987}},
doi = {{10.1002/humu.22987}},
volume = {{37}},
year = {{2016}},
}