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Variant effect predictors: a systematic review and practical guide.

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Selecting the right tools for genetic variant annotation is crucial for understanding whole-genome sequence data. A practical guide reveals that combining just three tools can predict over 60% of functional impacts, aiding researchers in their analyses.

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Area of Science:

  • Genomics and Bioinformatics
  • Computational Biology
  • Genetic Variant Analysis

Background:

  • Large-scale whole-genome sequencing studies generate vast amounts of data, but interpreting the functional consequences of identified genetic variants remains a significant challenge.
  • Numerous computational tools exist for predicting variant functional impacts, yet practical guidance on selecting the most appropriate ones is lacking.

Purpose of the Study:

  • To provide a practical guide for selecting variant annotation tools for large-scale association analyses.
  • To categorize available tools based on variant types and predicted functional impacts for standardized assessment.

Main Methods:

  • Conducted a MEDLINE search up to November 10, 2023, focusing on tools applicable to broad phenotypes, usable locally, and recently updated.
  • Categorized 118 identified databases and software packages based on 36 variant types and 161 functional impacts using Sequence Ontology terms.
  • Evaluated tool combinations and unique impact predictions, including tools relevant to ACMG/AMP guidelines for clinical pathogenicity.

Main Results:

  • Identified 118 tools predicting 161 distinct functional impacts across 36 variant types.
  • A combination of three tools (SnpEff, FAVOR, SparkINFERNO) predicts 99 (61%) functional impacts.
  • Thirty-seven tools offer unique impact predictions, and 75 tools predict pathogenicity for clinical use.

Conclusions:

  • Over 100 tools are available for predicting numerous functional impacts, with a core set of three tools covering a majority of predictions.
  • Recent tools do not necessarily offer broader functional impact prediction capabilities compared to older ones.
  • Future development should focus on predicting impacts for currently unsupported variant types, such as gene fusions.