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

Guangdong Provincial Key Laboratory of Human Diseases Genome, BGI-Shenzhen, Guangdong, China.
PLoS ONE (Impact Factor: 3.23). 01/2013; 8(1):e54236. DOI: 10.1371/journal.pone.0054236
Source: PubMed


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.

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Available from: Xuyang Yin, Jan 21, 2014
    • "An advanced method for the detection of structural rearrangements utilizes paired-end read information by creating a bona fide list of discordantly mapped read pairs and identifies candidate rearrangements supported by more than one pair from this list (Voet et al., 2013). Although correction of GC bias is possible (Baslan et al., 2012; Voet et al., 2013; Zhang et al., 2013), other confounding factors such as allelic dropout or preferential allelic amplification cannot be easily corrected for and may introduce false positives in SNP and CNV detection. Random sequencing errors represent another source of uncertainty for SNP detection. "
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