Ultrasensitive and high-efficiency screen of de novo low-frequency mutations by o2n-seq

Kaile Wang, Shujuan Lai, Xiaoxu Yang, Tianqi Zhu, Xuemei Lu, Chung-I Wu () and Jue Ruan ()
Additional contact information
Kaile Wang: Agricultural Genomics Institute, Chinese Academy of Agricultural Sciences
Shujuan Lai: Key Laboratory of Genomics and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences
Xiaoxu Yang: Center for Bioinformatics, State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University
Tianqi Zhu: Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences
Xuemei Lu: Key Laboratory of Genomics and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences
Chung-I Wu: Key Laboratory of Genomics and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences
Jue Ruan: Agricultural Genomics Institute, Chinese Academy of Agricultural Sciences

Nature Communications, 2017, vol. 8, issue 1, 1-11

Abstract: Abstract Detection of de novo, low-frequency mutations is essential for characterizing cancer genomes and heterogeneous cell populations. However, the screening capacity of current ultrasensitive NGS methods is inadequate owing to either low-efficiency read utilization or severe amplification bias. Here, we present o2n-seq, an ultrasensitive and high-efficiency NGS library preparation method for discovering de novo, low-frequency mutations. O2n-seq reduces the error rate of NGS to 10−5–10−8. The efficiency of its data usage is about 10–30 times higher than that of barcode-based strategies. For detecting mutations with allele frequency (AF) 1% in 4.6 Mb-sized genome, the sensitivity and specificity of o2n-seq reach to 99% and 98.64%, respectively. For mutations with AF around 0.07% in phix174, o2n-seq detects all the mutations with 100% specificity. Moreover, we successfully apply o2n-seq to screen de novo, low-frequency mutations in human tumours. O2n-seq will aid to characterize the landscape of somatic mutations in research and clinical settings.

Date: 2017
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/ncomms15335 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_ncomms15335

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/ncomms15335

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().