Evidence map›Paper›PMID 29563514›Full record

ArticleScientific reports2018

Comparison of single cell sequencing data between two whole genome amplification methods on two sequencing platforms.

DaYang Chen, HeFu Zhen, Yong Qiu, Ping Liu, Peng Zeng, Jun Xia, QianYu Shi, Lin Xie, Zhu Zhu, Ya Gao and 4 more

Abstract readComparative StudyEvaluation Study
In one paragraph

Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Review
  6. Article
  7. Article
  8. Cancer evolution: Darwin and beyond.The EMBO journal · 2021
    Review
  9. Article
  10. Article
  11. Genomic Analysis of Circulating Tumor Cells at the Single-Cell Level.The Journal of molecular diagnostics : JMD · 2020
    Article
  12. Article
  13. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

DaYang ChenBGI Education Center, University of Chinese Academy of Sciences, Shenzhen, 518083, China.
HeFu ZhenSchool of Bioscience and Bioengineering, South China University of Technology, Guangzhou, 510006, China.
Yong QiuBGI-Shenzhen, Shenzhen, China.
Ping LiuBGI-Shenzhen, Shenzhen, China.
Peng ZengBGI-Shenzhen, Shenzhen, China.
Jun XiaBGI-Shenzhen, Shenzhen, China.
QianYu ShiBGI-Shenzhen, Shenzhen, China.
Lin XieBGI-Shenzhen, Shenzhen, China.
Zhu ZhuBGI-Shenzhen, Shenzhen, China.
Ya GaoBGI-Shenzhen, Shenzhen, China.
GuoDong HuangBGI-Shenzhen, Shenzhen, China.
Jian WangBGI-Shenzhen, Shenzhen, China.
HuanMing YangBGI-Shenzhen, Shenzhen, China.
Fang ChenLaboratory of Genomics and Molecular Biomedicine, Department of Biology, University of Copenhagen, DK-2100, Copenhagen, Denmark. Fangchen@genomics.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Research based on a strategy of single-cell low-coverage whole genome sequencing (SLWGS) has enabled better reproducibility and accuracy for detection of copy number variations (CNVs). The whole genome amplification (WGA) method and sequencing platform are critical factors for successful SLWGS (<0.1 × coverage). In this study, we compared single cell and multiple cells sequencing data produced by the HiSeq2000 and Ion Proton platforms using two WGA kits and then comprehensively evaluated the GC-bias, reproducibility, uniformity and CNV detection among different experimental combinations. Our analysis demonstrated that the PicoPLEX WGA Kit resulted in higher reproducibility, lower sequencing error frequency but more GC-bias than the GenomePlex Single Cell WGA Kit (WGA4 kit) independent of the cell number on the HiSeq2000 platform. While on the Ion Proton platform, the WGA4 kit (both single cell and multiple cells) had higher uniformity and less GC-bias but lower reproducibility than those of the PicoPLEX WGA Kit. Moreover, on these two sequencing platforms, depending on cell number, the performance of the two WGA kits was different for both sensitivity and specificity on CNV detection. The results can help researchers who plan to use SLWGS on single or multiple cells to select appropriate experimental conditions for their applications.

Indexed as

Cell LineGenome, HumanGenomicsHigh-Throughput Nucleotide SequencingHumansReproducibility of ResultsSensitivity and SpecificitySingle-Cell AnalysisWhole Genome Sequencing

Identifiers

PMID29563514
PMCPMC5862989

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.