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

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

Li Tai Fang, Bin Zhu, Yongmei Zhao, Wanqiu Chen, Zhaowei Yang, Liz Kerrigan, Kurt Langenbach, Maryellen de Mars, Charles Lu, Kenneth Idler, …
Nature biotechnology, Vol.39(9), pp.1151-1160
2021
PMCID: PMC8532138
PMID: 34504347

Abstract

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

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Collaboration types
Industry collaboration
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International collaboration
Citation topics
1 Clinical & Life Sciences
1.189 Genome Studies
1.189.310 Population Genetics
Web Of Science research areas
Biotechnology & Applied Microbiology
ESI research areas
Biology & Biochemistry
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