Structural variant analysis of a cancer reference cell line sample using multiple sequencing technologies

Genome Biology

Research

Li Tai Fang¹  Eric Stahlberg²  Ben Kellman³  Alex R. Hastie³  Andy Wing Chun Pang³  Karl Hong³  Michael Colgan⁴  Wenming Xiao⁴  Tiantain Liu⁵  Zhong Chen⁵  Charles Wang⁵  Wanqiu Chen⁵  Daoud Meerzaman⁶  Andrew Carroll⁷  Zhaowei Yang⁸  Jing Li⁸  Christopher E. Mason⁹  Veronnica Mankinen^1,10  Ali Moshrefi^1,11  Aparna Natarajan^1,11  Anastasiya Granat^1,11  Robin Bombardi^1,11  Tiffany Truong^1,11  Erich Jaeger^1,11  Rebecca Kusko^1,12  Limin Wang^1,13  Chunlin Xiao^1,14  Zhipan Li^1,15  Xiongfong Chen^1,16  Tsai-wei Shen^1,16  Keyur Talsania^1,16  Yongmei Zhao^1,16  Sulbha Choudhari^1,16  Jack Collins^1,16  Bao Tran^1,17  Yuliya Kriga^1,17  Tatyana Smirnova^1,17  Jyoti Shetty^1,17  Oksana German^1,17 

[1] Bioinformatics Research & Early Development, Roche Sequencing Solutions Inc, 1301 Shoreway Road, 94002, Belmont, CA, USA;Bioinformatics and Computational Science Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA;Bionano Genomics, CA92121, San Diego, USA;Center for Drug Evaluation and Research, FDA, Silver Spring, MD, USA;Center for Genomics, Loma Linda University School of Medicine, Loma Linda, CA, USA;Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology (CBIIT), National Cancer Institute, Rockville, MD, USA;DNAnexus, Mountain View, CA, USA;Department of Allergy and Clinical Immunology, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China;Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA;Dovetail Genomics, Scotts Valley, CA, USA;Illumina Inc, Foster City, CA, USA;Immuneering Corp, Cambridge, MA, USA;Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA;National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA;Sentieon Inc, Mountain View, CA, USA;Sequencing Facility Bioinformatics Group, Advanced Biomedical and Computational Science, Frederick National Laboratory for Cancer Research, Frederick, MD, USA;Bioinformatics and Computational Science Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA;Sequencing Facility, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA;

关键词: Structural variation;    Reference call set;    Cancer;    Multiple platforms;    Structural variant calling algorithm;    Next-generation sequencing technology;   

DOI  :  10.1186/s13059-022-02816-6

 received in 2021-10-17, accepted in 2022-11-17,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundThe cancer genome is commonly altered with thousands of structural rearrangements including insertions, deletions, translocation, inversions, duplications, and copy number variations. Thus, structural variant (SV) characterization plays a paramount role in cancer target identification, oncology diagnostics, and personalized medicine. As part of the SEQC2 Consortium effort, the present study established and evaluated a consensus SV call set using a breast cancer reference cell line and matched normal control derived from the same donor, which were used in our companion benchmarking studies as reference samples.ResultsWe systematically investigated somatic SVs in the reference cancer cell line by comparing to a matched normal cell line using multiple NGS platforms including Illumina short-read, 10X Genomics linked reads, PacBio long reads, Oxford Nanopore long reads, and high-throughput chromosome conformation capture (Hi-C). We established a consensus SV call set of a total of 1788 SVs including 717 deletions, 230 duplications, 551 insertions, 133 inversions, 146 translocations, and 11 breakends for the reference cancer cell line. To independently evaluate and cross-validate the accuracy of our consensus SV call set, we used orthogonal methods including PCR-based validation, Affymetrix arrays, Bionano optical mapping, and identification of fusion genes detected from RNA-seq. We evaluated the strengths and weaknesses of each NGS technology for SV determination, and our findings provide an actionable guide to improve cancer genome SV detection sensitivity and accuracy.ConclusionsA high-confidence consensus SV call set was established for the reference cancer cell line. A large subset of the variants identified was validated by multiple orthogonal methods.

【 授权许可】

CC BY   
© The Author(s) 2022

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