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Comprehensive, integrated, and phased whole-genome analysis of the primary ENCODE cell line K562

View ORCID ProfileBo Zhou, View ORCID ProfileSteve S. Ho, Stephanie U. Greer, View ORCID ProfileXiaowei Zhu, John M. Bell, View ORCID ProfileJoseph G. Arthur, View ORCID ProfileNoah Spies, View ORCID ProfileXianglong Zhang, View ORCID ProfileSeunggyu Byeon, View ORCID ProfileReenal Pattni, Noa Ben-Efraim, View ORCID ProfileMichael S. Haney, View ORCID ProfileRajini R. Haraksingh, Hanlee P. Ji, View ORCID ProfileGiltae Song, View ORCID ProfileDimitri Perrin, Wing H. Wong, View ORCID ProfileAlexej Abyzov, View ORCID ProfileAlexander E. Urban
doi: https://doi.org/10.1101/192344
Bo Zhou
1Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California 94305, USA
2Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
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Steve S. Ho
1Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California 94305, USA
2Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
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Stephanie U. Greer
3Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, California 94305, USA
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Xiaowei Zhu
1Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California 94305, USA
2Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
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John M. Bell
4Stanford Genome Technology Center, Stanford University, Palo Alto, California 94304, USA
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Joseph G. Arthur
5Department of Statistics, Stanford University, Stanford, California 94305, USA
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Noah Spies
2Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
6Department of Pathology, Stanford University School of Medicine, Stanford, California 94305, USA
7Genome-Scale Measurements Group, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA
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Xianglong Zhang
1Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California 94305, USA
2Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
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Seunggyu Byeon
8School of Computer Science and Engineering, College of Engineering, Pusan National University, Busan 46241, South Korea
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Reenal Pattni
1Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California 94305, USA
2Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
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Noa Ben-Efraim
1Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California 94305, USA
2Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
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Michael S. Haney
1Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California 94305, USA
2Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
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Rajini R. Haraksingh
1Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California 94305, USA
2Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
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Hanlee P. Ji
3Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, California 94305, USA
4Stanford Genome Technology Center, Stanford University, Palo Alto, California 94304, USA
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Giltae Song
8School of Computer Science and Engineering, College of Engineering, Pusan National University, Busan 46241, South Korea
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Dimitri Perrin
10Science and Engineering Faculty, Queensland University of Technology, Brisbane, QLD 4001, Australia
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Wing H. Wong
5Department of Statistics, Stanford University, Stanford, California 94305, USA
11Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California 94305, USA
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Alexej Abyzov
12Department of Health Sciences Research, Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota 55905, USA
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Alexander E. Urban
1Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California 94305, USA
2Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
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ABSTRACT

K562 is widely used in biomedical research. It is one of three tier-one cell lines of ENCODE and also most commonly used for large-scale CRISPR/Cas9 screens. Although its functional genomic and epigenomic characteristics have been extensively studied, its genome sequence and genomic structural features have never been comprehensively analyzed. Such information is essential for the correct interpretation and understanding of the vast troves of existing functional genomics and epigenomics data for K562. We performed and integrated deep-coverage whole-genome (short-insert), mate-pair, and linked-read sequencing as well as karyotyping and array CGH analysis to identify a wide spectrum of genome characteristics in K562: copy numbers (CN) of aneuploid chromosome segments at high-resolution, SNVs and Indels (both corrected for CN in aneuploid regions), loss of heterozygosity, mega-base-scale phased haplotypes often spanning entire chromosome arms, structural variants (SVs) including small and large-scale complex SVs and non-reference retrotransposon insertions. Many SVs were phased, assembled, and experimentally validated. We identified multiple allele-specific deletions and duplications within the tumor suppressor gene FHIT. Taking aneuploidy into account, we re-analyzed K562 RNA-seq and whole-genome bisulfite sequencing data for allele-specific expression and allele-specific DNA methylation. We also show examples of how deeper insights into regulatory complexity are gained by integrating genomic variant information and structural context with functional genomics and epigenomics data. Furthermore, using K562 haplotype information, we produced an allele-specific CRISPR targeting map. This comprehensive whole-genome analysis serves as a resource for future studies that utilize K562 as well as a framework for the analysis of other cancer genomes.

Footnotes

  • ↵9 Current affiliation: Department of Life Sciences, The University of the West Indies, Saint Augustine, Trinidad and Tobago

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted June 17, 2018.
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Comprehensive, integrated, and phased whole-genome analysis of the primary ENCODE cell line K562
Bo Zhou, Steve S. Ho, Stephanie U. Greer, Xiaowei Zhu, John M. Bell, Joseph G. Arthur, Noah Spies, Xianglong Zhang, Seunggyu Byeon, Reenal Pattni, Noa Ben-Efraim, Michael S. Haney, Rajini R. Haraksingh, Hanlee P. Ji, Giltae Song, Dimitri Perrin, Wing H. Wong, Alexej Abyzov, Alexander E. Urban
bioRxiv 192344; doi: https://doi.org/10.1101/192344
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Comprehensive, integrated, and phased whole-genome analysis of the primary ENCODE cell line K562
Bo Zhou, Steve S. Ho, Stephanie U. Greer, Xiaowei Zhu, John M. Bell, Joseph G. Arthur, Noah Spies, Xianglong Zhang, Seunggyu Byeon, Reenal Pattni, Noa Ben-Efraim, Michael S. Haney, Rajini R. Haraksingh, Hanlee P. Ji, Giltae Song, Dimitri Perrin, Wing H. Wong, Alexej Abyzov, Alexander E. Urban
bioRxiv 192344; doi: https://doi.org/10.1101/192344

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