A synthetic-diploid benchmark for accurate variant calling evaluation

Constructed from the consensus of short-read variant callers, existing benchmark datasets for evaluating variant calling accuracy are biased toward easy regions accessible by known algorithms. We derived a new benchmark dataset from the de novo PacBio assemblies of two human cell lines that are homo...

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Autores principales: Li, Heng, Bloom, Jonathan M, Farjoun, Yossi, Fleharty, Mark, Gauthier, Laura, Neale, Benjamin, MacArthur, Daniel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2018
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6341484/
https://www.ncbi.nlm.nih.gov/pubmed/30013044
http://dx.doi.org/10.1038/s41592-018-0054-7
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author Li, Heng
Bloom, Jonathan M
Farjoun, Yossi
Fleharty, Mark
Gauthier, Laura
Neale, Benjamin
MacArthur, Daniel
author_facet Li, Heng
Bloom, Jonathan M
Farjoun, Yossi
Fleharty, Mark
Gauthier, Laura
Neale, Benjamin
MacArthur, Daniel
author_sort Li, Heng
collection PubMed
description Constructed from the consensus of short-read variant callers, existing benchmark datasets for evaluating variant calling accuracy are biased toward easy regions accessible by known algorithms. We derived a new benchmark dataset from the de novo PacBio assemblies of two human cell lines that are homozygous across the whole genome. It provides a more accurate and less biased estimate of the error rate of small variant calls in a realistic context.
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spelling pubmed-63414842019-01-22 A synthetic-diploid benchmark for accurate variant calling evaluation Li, Heng Bloom, Jonathan M Farjoun, Yossi Fleharty, Mark Gauthier, Laura Neale, Benjamin MacArthur, Daniel Nat Methods Article Constructed from the consensus of short-read variant callers, existing benchmark datasets for evaluating variant calling accuracy are biased toward easy regions accessible by known algorithms. We derived a new benchmark dataset from the de novo PacBio assemblies of two human cell lines that are homozygous across the whole genome. It provides a more accurate and less biased estimate of the error rate of small variant calls in a realistic context. 2018-07-16 2018-08 /pmc/articles/PMC6341484/ /pubmed/30013044 http://dx.doi.org/10.1038/s41592-018-0054-7 Text en Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms
spellingShingle Article
Li, Heng
Bloom, Jonathan M
Farjoun, Yossi
Fleharty, Mark
Gauthier, Laura
Neale, Benjamin
MacArthur, Daniel
A synthetic-diploid benchmark for accurate variant calling evaluation
title A synthetic-diploid benchmark for accurate variant calling evaluation
title_full A synthetic-diploid benchmark for accurate variant calling evaluation
title_fullStr A synthetic-diploid benchmark for accurate variant calling evaluation
title_full_unstemmed A synthetic-diploid benchmark for accurate variant calling evaluation
title_short A synthetic-diploid benchmark for accurate variant calling evaluation
title_sort synthetic-diploid benchmark for accurate variant calling evaluation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6341484/
https://www.ncbi.nlm.nih.gov/pubmed/30013044
http://dx.doi.org/10.1038/s41592-018-0054-7
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