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Nature Biotechnology
Article . 2021 . Peer-reviewed
License: Springer TDM
Data sources: Crossref
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Research.fi
Article . 2021 . Peer-reviewed
Data sources: Research.fi
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Establishing community reference samples, data and call sets for benchmarking cancer mutation detection using whole-genome sequencing

Authors: Li Tai Fang; Bin Zhu; Yongmei Zhao; Wanqiu Chen; Zhaowei Yang; Liz Kerrigan; Kurt Langenbach; +179 Authors

Establishing community reference samples, data and call sets for benchmarking cancer mutation detection using whole-genome sequencing

Abstract

The lack of samples for generating standardized DNA datasets for setting up a sequencing pipeline or benchmarking the performance of different algorithms limits the implementation and uptake of cancer genomics. Here, we describe reference call sets obtained from paired tumor-normal genomic DNA (gDNA) samples derived from a breast cancer cell line-which is highly heterogeneous, with an aneuploid genome, and enriched in somatic alterations-and a matched lymphoblastoid cell line. We partially validated both somatic mutations and germline variants in these call sets via whole-exome sequencing (WES) with different sequencing platforms and targeted sequencing with >2,000-fold coverage, spanning 82% of genomic regions with high confidence. Although the gDNA reference samples are not representative of primary cancer cells from a clinical sample, when setting up a sequencing pipeline, they not only minimize potential biases from technologies, assays and informatics but also provide a unique resource for benchmarking 'tumor-only' or 'matched tumor-normal' analyses.

Keywords

Whole Genome Sequencing, DNA Mutational Analysis, Datasets as Topic, High-Throughput Nucleotide Sequencing, Reproducibility of Results, Breast Neoplasms, Reference Standards, Benchmarking, Germ Cells, Cell Line, Tumor, Mutation, Humans

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    90
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 1%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 1%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
90
Top 1%
Top 10%
Top 1%
bronze