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Best practices for benchmarking germline small-variant calls in human genomes.

  • Author(s): Krusche, Peter
  • Trigg, Len
  • Boutros, Paul C
  • Mason, Christopher E
  • De La Vega, Francisco M
  • Moore, Benjamin L
  • Gonzalez-Porta, Mar
  • Eberle, Michael A
  • Tezak, Zivana
  • Lababidi, Samir
  • Truty, Rebecca
  • Asimenos, George
  • Funke, Birgit
  • Fleharty, Mark
  • Chapman, Brad A
  • Salit, Marc
  • Zook, Justin M
  • Global Alliance for Genomics and Health Benchmarking Team
  • et al.

Published Web Location

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6699627/
No data is associated with this publication.
Abstract

Standardized benchmarking approaches are required to assess the accuracy of variants called from sequence data. Although variant-calling tools and the metrics used to assess their performance continue to improve, important challenges remain. Here, as part of the Global Alliance for Genomics and Health (GA4GH), we present a benchmarking framework for variant calling. We provide guidance on how to match variant calls with different representations, define standard performance metrics, and stratify performance by variant type and genome context. We describe limitations of high-confidence calls and regions that can be used as truth sets (for example, single-nucleotide variant concordance of two methods is 99.7% inside versus 76.5% outside high-confidence regions). Our web-based app enables comparison of variant calls against truth sets to obtain a standardized performance report. Our approach has been piloted in the PrecisionFDA variant-calling challenges to identify the best-in-class variant-calling methods within high-confidence regions. Finally, we recommend a set of best practices for using our tools and evaluating the results.

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