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An open resource for accurately benchmarking small variant and reference calls. | LitMetric

AI Article Synopsis

  • Benchmark small variant calls are essential for improving sequencing and bioinformatics methods, and the Genome in a Bottle (GIAB) Consortium is utilizing a cloud-based pipeline for this purpose.
  • The team has generated new benchmark calls for one GIAB sample and six genomes from the Personal Genome Project, increasing the dataset availability for various applications with 17% more single nucleotide variations and significantly more indels compared to previous benchmarks.
  • They also found that these benchmarks can effectively identify errors in existing datasets, though there are challenges in evaluating performance due to incomplete or imperfect benchmarks, and they analyzed the strengths and weaknesses of different callsets based on variant type and genome context.

Article Abstract

Benchmark small variant calls are required for developing, optimizing and assessing the performance of sequencing and bioinformatics methods. Here, as part of the Genome in a Bottle (GIAB) Consortium, we apply a reproducible, cloud-based pipeline to integrate multiple short- and linked-read sequencing datasets and provide benchmark calls for human genomes. We generate benchmark calls for one previously analyzed GIAB sample, as well as six genomes from the Personal Genome Project. These new genomes have broad, open consent, making this a 'first of its kind' resource that is available to the community for multiple downstream applications. We produce 17% more benchmark single nucleotide variations, 176% more indels and 12% larger benchmark regions than previously published GIAB benchmarks. We demonstrate that this benchmark reliably identifies errors in existing callsets and highlight challenges in interpreting performance metrics when using benchmarks that are not perfect or comprehensive. Finally, we identify strengths and weaknesses of callsets by stratifying performance according to variant type and genome context.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6500473PMC
http://dx.doi.org/10.1038/s41587-019-0074-6DOI Listing

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