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

Författare

Summary, in English

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.

Avdelning/ar

  • Proteinbioinformatik
  • BioCARE: Biomarkers in Cancer Medicine improving Health Care, Education and Innovation

Publiceringsår

2016-06-01

Språk

Engelska

Sidor

579-597

Publikation/Tidskrift/Serie

Human Mutation

Volym

37

Issue

6

Dokumenttyp

Artikel i tidskrift

Förlag

John Wiley & Sons Inc.

Ämne

  • Bioinformatics and Systems Biology

Nyckelord

  • Computational tools
  • Mutation effect prediction
  • Prediction methods
  • Variation effect
  • Variation interpretation
  • Variation prediction

Status

Published

Forskningsgrupp

  • Protein Bioinformatics

ISBN/ISSN/Övrigt

  • ISSN: 1059-7794