Evidence map›Paper›PMID 24465483›Full record

ArticlePloS one2014

PSCC: sensitive and reliable population-scale copy number variation detection method based on low coverage sequencing.

Xuchao Li, Shengpei Chen, Weiwei Xie, Ida Vogel, Kwong Wai Choy, Fang Chen, Rikke Christensen, Chunlei Zhang, Huijuan Ge, Haojun Jiang and 5 more

Abstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. A Chinese patient with Rothmund-Thomson syndrome.Molecular genetics & genomic medicine · 2024
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Low-pass whole-genome sequencing in clinical cytogenetics: a validated approach.Genetics in medicine : official journal of the American College of Medical Genetics · 2016
    Article
  18. Article
  19. Article
  20. Article
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

15 authors.

Xuchao LiBGI-Shenzhen, Shenzhen, China.
Shengpei ChenBGI-Shenzhen, Shenzhen, China ; State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.
Weiwei XieBGI-Shenzhen, Shenzhen, China.
Ida VogelDepartment of Clinical Genetics, Aarhus University Hospital, Aarhus, Denmark.
Kwong Wai ChoyDepartment of Obstetrics and Gynaecology, The Chinese University of Hong Kong, Shatin, NT, Hong Kong.
Fang ChenBGI-Shenzhen, Shenzhen, China.
Rikke ChristensenDepartment of Clinical Genetics, Aarhus University Hospital, Aarhus, Denmark.
Chunlei ZhangBGI-Shenzhen, Shenzhen, China.
Huijuan GeBGI-Shenzhen, Shenzhen, China.
Haojun JiangBGI-Shenzhen, Shenzhen, China ; State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.
Chang YuBGI-Shenzhen, Shenzhen, China.
Fang HuangGuangzhou Children's Social Welfare Home, Guangzhou, China.
Wei WangBGI-Shenzhen, Shenzhen, China ; Clinical laboratory of BGI Health, Shenzhen, China.
Hui JiangBGI-Shenzhen, Shenzhen, China.
Xiuqing ZhangBGI-Shenzhen, Shenzhen, China ; The Guangdong Enterprise Key Laboratory of Human Disease Genomics, BGI-Shenzhen, Shenzhen, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCopy number variations (CNVs) represent an important type of genetic variation that deeply impact phenotypic polymorphisms and human diseases. The advent of high-throughput sequencing technologies provides an opportunity to revolutionize the discovery of CNVs and to explore their relationship with diseases. However, most of the existing methods depend on sequencing depth and show instability with low sequence coverage. In this study, using low coverage whole-genome sequencing (LCS) we have developed an effective population-scale CNV calling (PSCC) method. METHODOLOGY/PRINCIPAL

findingsIn our novel method, two-step correction was used to remove biases caused by local GC content and complex genomic characteristics. We chose a binary segmentation method to locate CNV segments and designed combined statistics tests to ensure the stable performance of the false positive control. The simulation data showed that our PSCC method could achieve 99.7%/100% and 98.6%/100% sensitivity and specificity for over 300 kb CNV calling in the condition of LCS (∼2×) and ultra LCS (∼0.2×), respectively. Finally, we applied this novel method to analyze 34 clinical samples with an average of 2× LCS. In the final results, all the 31 pathogenic CNVs identified by aCGH were successfully detected. In addition, the performance comparison revealed that our method had significant advantages over existing methods using ultra LCS. CONCLUSIONS/SIGNIFICANCE: Our study showed that PSCC can sensitively and reliably detect CNVs using low coverage or even ultra-low coverage data through population-scale sequencing.

Indexed as

AlgorithmsDNA Copy Number VariationsGenome, HumanGenome-Wide Association StudyHigh-Throughput Nucleotide SequencingHumansSensitivity and Specificity

Identifiers

PMID24465483
PMCPMC3897425

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.