Title |
Germline and somatic variant identification using BGISEQ-500 and HiSeq X Ten whole genome sequencing
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Published in |
PLOS ONE, January 2018
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DOI | 10.1371/journal.pone.0190264 |
Pubmed ID | |
Authors |
Ann-Marie Patch, Katia Nones, Stephen H. Kazakoff, Felicity Newell, Scott Wood, Conrad Leonard, Oliver Holmes, Qinying Xu, Venkateswar Addala, Jenette Creaney, Bruce W. Robinson, Shujin Fu, Chunyu Geng, Tong Li, Wenwei Zhang, Xinming Liang, Junhua Rao, Jiahao Wang, Mingyu Tian, Yonggang Zhao, Fei Teng, Honglan Gou, Bicheng Yang, Hui Jiang, Feng Mu, John V. Pearson, Nicola Waddell |
Abstract |
Technological innovation and increased affordability have contributed to the widespread adoption of genome sequencing technologies in biomedical research. In particular large cancer research consortia have embraced next generation sequencing, and have used the technology to define the somatic mutation landscape of multiple cancer types. These studies have primarily utilised the Illumina HiSeq platforms. In this study we performed whole genome sequencing of three malignant pleural mesothelioma and matched normal samples using a new platform, the BGISEQ-500, and compared the results obtained with Illumina HiSeq X Ten. Germline and somatic, single nucleotide variants and small insertions or deletions were independently identified from data aligned human genome reference. The BGISEQ-500 and HiSeq X Ten platforms showed high concordance for germline calls with genotypes from SNP arrays (>99%). The germline and somatic single nucleotide variants identified in both sequencing platforms were highly concordant (86% and 72% respectively). These results indicate the potential applicability of the BGISEQ-500 platform for the identification of somatic and germline single nucleotide variants by whole genome sequencing. The BGISEQ-500 datasets described here represent the first publicly-available cancer genome sequencing performed using this platform. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Australia | 4 | 24% |
United States | 2 | 12% |
Germany | 1 | 6% |
United Kingdom | 1 | 6% |
Qatar | 1 | 6% |
China | 1 | 6% |
Hong Kong | 1 | 6% |
Unknown | 6 | 35% |
Demographic breakdown
Type | Count | As % |
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Scientists | 10 | 59% |
Members of the public | 7 | 41% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 72 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 15 | 21% |
Student > Ph. D. Student | 11 | 15% |
Student > Doctoral Student | 8 | 11% |
Student > Master | 8 | 11% |
Other | 5 | 7% |
Other | 14 | 19% |
Unknown | 11 | 15% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 23 | 32% |
Agricultural and Biological Sciences | 20 | 28% |
Medicine and Dentistry | 4 | 6% |
Mathematics | 2 | 3% |
Computer Science | 2 | 3% |
Other | 8 | 11% |
Unknown | 13 | 18% |