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

Overview of attention for article published in PLoS ONE, January 2013
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

Mentioned by

twitter
1 tweeter
patent
9 patents

Citations

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48 Dimensions

Readers on

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104 Mendeley
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Title
A Single Cell Level Based Method for Copy Number Variation Analysis by Low Coverage Massively Parallel Sequencing
Published in
PLoS ONE, January 2013
DOI 10.1371/journal.pone.0054236
Pubmed ID
Authors

Chunlei Zhang, Chunsheng Zhang, Shengpei Chen, Xuyang Yin, Xiaoyu Pan, Ge Lin, Yueqiu Tan, Ke Tan, Zhengfeng Xu, Ping Hu, Xuchao Li, Fang Chen, Xun Xu, Yingrui Li, Xiuqing Zhang, Hui Jiang, Wei Wang

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.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 104 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
Italy 1 <1%
United Kingdom 1 <1%
Australia 1 <1%
Singapore 1 <1%
Unknown 98 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 31 30%
Student > Ph. D. Student 23 22%
Student > Master 10 10%
Unspecified 9 9%
Other 5 5%
Other 26 25%
Readers by discipline Count As %
Agricultural and Biological Sciences 47 45%
Biochemistry, Genetics and Molecular Biology 24 23%
Medicine and Dentistry 12 12%
Unspecified 9 9%
Computer Science 4 4%
Other 8 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 May 2018.
All research outputs
#1,336,585
of 12,184,158 outputs
Outputs from PLoS ONE
#23,065
of 133,757 outputs
Outputs of similar age
#37,335
of 285,625 outputs
Outputs of similar age from PLoS ONE
#986
of 6,571 outputs
Altmetric has tracked 12,184,158 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 133,757 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.6. This one has done well, scoring higher than 82% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 285,625 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 6,571 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.