A Single Cell Level Based Method for Copy Number Variation Analysis by Low Coverage Massively Parallel Sequencing

Zhang, Chunlei, Zhang, Chunsheng, Chen, Shengpei, Yin, Xuyang, Pan, Xiaoyu, Lin, Ge, Tan, Yueqiu, Tan, Ke, Xu, Zhengfeng, Hu, Ping, Li, Xuchao, Chen, Fang, Xu, Xun, Li, Yingrui, Zhang, Xiuqing, Jiang, Hui and Wang, Wei (2013) A Single Cell Level Based Method for Copy Number Variation Analysis by Low Coverage Massively Parallel Sequencing. PLoS ONE, 8 1: . doi:10.1371/journal.pone.0054236


Author Zhang, Chunlei
Zhang, Chunsheng
Chen, Shengpei
Yin, Xuyang
Pan, Xiaoyu
Lin, Ge
Tan, Yueqiu
Tan, Ke
Xu, Zhengfeng
Hu, Ping
Li, Xuchao
Chen, Fang
Xu, Xun
Li, Yingrui
Zhang, Xiuqing
Jiang, Hui
Wang, Wei
Title A Single Cell Level Based Method for Copy Number Variation Analysis by Low Coverage Massively Parallel Sequencing
Journal name PLoS ONE   Check publisher's open access policy
ISSN 1932-6203
Publication date 2013-01-29
Sub-type Article (original research)
DOI 10.1371/journal.pone.0054236
Open Access Status DOI
Volume 8
Issue 1
Total pages 9
Place of publication San Francisco, United States
Publisher Public Library of Science
Language eng
Abstract Copy number variations (CNVs), a common genomic mutation associated with various diseases, are important in research and clinical applications. Whole genome amplification (WGA) and massively parallel sequencing have been applied to single cell CNVs analysis, which provides new insight for the fields of biology and medicine. However, the WGA-induced bias significantly limits sensitivity and specificity for CNVs detection. Addressing these limitations, we developed a practical bioinformatic methodology for CNVs detection at the single cell level using low coverage massively parallel sequencing. This method consists of GC correction for WGA-induced bias removal, binary segmentation algorithm for locating CNVs breakpoints, and dynamic threshold determination for final signals filtering. Afterwards, we evaluated our method with seven test samples using low coverage sequencing (4~9.5%). Four single-cell samples from peripheral blood, whose karyotypes were confirmed by whole genome sequencing analysis, were acquired. Three other test samples derived from blastocysts whose karyotypes were confirmed by SNP-array analysis were also recruited. The detection results for CNVs of larger than 1 Mb were highly consistent with confirmed results reaching 99.63% sensitivity and 97.71% specificity at base-pair level. Our study demonstrates the potential to overcome WGA-bias and to detect CNVs (>1 Mb) at the single cell level through low coverage massively parallel sequencing. It highlights the potential for CNVs research on single cells or limited DNA samples and may prove as a promising tool for research and clinical applications, such as pre-implantation genetic diagnosis/screening, fetal nucleated red blood cells research and cancer heterogeneity analysis.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Institute for Molecular Bioscience - Publications
 
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