Interpreting Disease Relevance of Amino Acid Substitutions

Abstract

High‐throughput sequencing methods can generate large amounts of information about genetic variations; however, interpretation of this data has become a severe bottleneck for efficient use of genomic data, for example, in diagnostics. Identification of variations responsible for phenotypes is laborious and many times difficult task. Amino acid substitutions are among most common disease‐causing variants. Human genome codes for, on an average, approximately 11 000 such variants. Computational tools are needed to filter and rank raw variation datasets for further studies. Amino acid substitutions can have numerous effects, and mechanisms behind them are diverse. Therefore, different kinds of methods have been developed. Tolerance predictors aim at finding out likely harmful variants. Mechanism‐ and effect‐specific tools are dedicated for specific outcomes of variants.

Key Concepts:

  • Amino acid substitution is a change in protein sequence where a single residue is changed.

  • Benchmark dataset contains cases with known effect. It serves as the gold standard, for example, for method performance assessment and training machine learning‐based methods.

  • Human variome project (HVP) is an international organisation coordinating research and standards for variation research.

  • Next generation sequencing methods are fast nucleotide sequencing methods taking benefit of multiplexing and able of sequencing complete genomes very fast.

  • Performance measures are used to indicate performance of prediction methods. For full picture of performance, a number of measures should be reported.

  • Tolerance predictors are methods to predict whether amino acid substitutions are tolerated or not in a sequence.

  • Variation is a change in nucleotide or amino acid sequence in comparison with the reference sequence.

Keywords: variants; pathogenicity; amino acid substitution; tolerance predictor; cancer; locus specific databases; protein localisation; machine learning; next generation sequencing; protein stability predictor

Figure 1.

Schema for pathogenicity assessment of variants with experimental and prediction methods.

Figure 2.

Flowchart for bioinformatics analysis of variant effects.

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Further Reading

Stefl S, Nishi H, Petukh M et al. (2013) Molecular mechanisms of disease‐causing missense mutations. Journal of Molecular Biology 425: 3919–3936.

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How to Cite close
Vihinen, Mauno(Mar 2014) Interpreting Disease Relevance of Amino Acid Substitutions. In: eLS. John Wiley & Sons Ltd, Chichester. http://www.els.net [doi: 10.1002/9780470015902.a0025177]