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PLoS One. 2014 Jan 7;9(1):e84785. doi: 10.1371/journal.pone.0084785. eCollection 2014.

Assessment of whole genome amplification for sequence capture and massively parallel sequencing.

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Science for Life Laboratory, School of Biotechnology, Division of Gene Technology, Royal Institute of Technology, Stockholm, Sweden.
Science for Life Laboratory, School of Biotechnology, Division of Gene Technology, Royal Institute of Technology, Stockholm, Sweden ; Department of Forensic Genetics and Forensic Toxicology, National Board of Forensic Medicine, Linköping, Sweden ; Clinical Pharmacology, Department of Health and Medical Sciences, Linköping University, Linköping, Sweden.
Genomics Unit, Institut Gustave Roussy, Villejuif, France.
Department of Pathology, Institut Mutualiste Montsouris, Paris, France.
Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden.


Exome sequence capture and massively parallel sequencing can be combined to achieve inexpensive and rapid global analyses of the functional sections of the genome. The difficulties of working with relatively small quantities of genetic material, as may be necessary when sharing tumor biopsies between collaborators for instance, can be overcome using whole genome amplification. However, the potential drawbacks of using a whole genome amplification technology based on random primers in combination with sequence capture followed by massively parallel sequencing have not yet been examined in detail, especially in the context of mutation discovery in tumor material. In this work, we compare mutations detected in sequence data for unamplified DNA, whole genome amplified DNA, and RNA originating from the same tumor tissue samples from 16 patients diagnosed with non-small cell lung cancer. The results obtained provide a comprehensive overview of the merits of these techniques for mutation analysis. We evaluated the identified genetic variants, and found that most (74%) of them were observed in both the amplified and the unamplified sequence data. Eighty-nine percent of the variations found by WGA were shared with unamplified DNA. We demonstrate a strategy for avoiding allelic bias by including RNA-sequencing information.

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