Evidence map›Paper›PMID 33575589›Full record

ArticleNAR genomics and bioinformatics2020

Comparative performance of the BGI and Illumina sequencing technology for single-cell RNA-sequencing.

Anne Senabouth, Stacey Andersen, Qianyu Shi, Lei Shi, Feng Jiang, Wenwei Zhang, Kristof Wing, Maciej Daniszewski, Samuel W Lukowski, Sandy S C Hung and 6 more

Abstract read
In one paragraph

Article in NAR genomics and bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing 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

34 citing papers in PubMed.

  1. Article
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  3. Article
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  7. Utility Analyses of AVITI Sequencing Chemistry.bioRxiv : the preprint server for biology · 2024
    Article
  8. Article
  9. Article
  10. Article
  11. Single-cell RNA sequencing for the study of kidney disease.Molecular medicine (Cambridge, Mass.) · 2023
    Review
  12. Review
  13. Article
  14. Article
  15. From Genomics to Metagenomics in the Era of Recent Sequencing Technologies.Methods in molecular biology (Clifton, N.J.) · 2023
    Article
  16. Article
  17. Whole genome sequencing andData in brief · 2022
    Article
  18. Review
  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

16 authors.

Anne SenabouthGarvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia.
Stacey AndersenInstitute for Molecular Bioscience, University of Queensland, St Lucia, QLD 4067, Australia.
Qianyu ShiMGI, BGI-Shenzhen, Shenzhen 518083, China.
Lei ShiMGI, BGI-Shenzhen, Shenzhen 518083, China.
Feng JiangMGI, BGI-Shenzhen, Shenzhen 518083, China.
Wenwei ZhangBGI-Shenzhen, Shenzhen 518083, China.
Kristof WingMenzies Institute for Medical Research, School of Medicine, University of Tasmania, Hobart, TAS 7000, Australia.
Maciej DaniszewskiDepartment of Anatomy and Neuroscience, The University of Melbourne, Parkville, VIC 3052, Australia.
Samuel W LukowskiInstitute for Molecular Bioscience, University of Queensland, St Lucia, QLD 4067, Australia.ORCID 0000-0002-8598-7902
Sandy S C HungCentre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Parkville, VIC 3052, Australia.
Quan NguyenInstitute for Molecular Bioscience, University of Queensland, St Lucia, QLD 4067, Australia.
Lynn FinkBGI Australia, 300 Herston Rd, Herston, QLD 4006 Australia.
Anthony BeckhouseBGI Australia, 300 Herston Rd, Herston, QLD 4006 Australia.
Alice PébayDepartment of Anatomy and Neuroscience, The University of Melbourne, Parkville, VIC 3052, Australia.
Alex W HewittMenzies Institute for Medical Research, School of Medicine, University of Tasmania, Hobart, TAS 7000, Australia.
Joseph E PowellGarvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia.ORCID 0000-0001-9031-6356

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The libraries generated by high-throughput single cell RNA-sequencing (scRNA-seq) platforms such as the Chromium from 10× Genomics require considerable amounts of sequencing, typically due to the large number of cells. The ability to use these data to address biological questions is directly impacted by the quality of the sequence data. Here we have compared the performance of the Illumina NextSeq 500 and NovaSeq 6000 against the BGI MGISEQ-2000 platform using identical Single Cell 3' libraries consisting of over 70 000 cells generated on the 10× Genomics Chromium platform. Our results demonstrate a highly comparable performance between the NovaSeq 6000 and MGISEQ-2000 in sequencing quality, and the detection of genes, cell barcodes, Unique Molecular Identifiers. The performance of the NextSeq 500 was also similarly comparable to the MGISEQ-2000 based on the same metrics. Data generated by both sequencing platforms yielded similar analytical outcomes for general single-cell analysis. The performance of the NextSeq 500 and MGISEQ-2000 were also comparable for the deconvolution of multiplexed cell pools via variant calling, and detection of guide RNA (gRNA) from a pooled CRISPR single-cell screen. Our study provides a benchmark for high-capacity sequencing platforms applied to high-throughput scRNA-seq libraries.

Identifiers

PMID33575589
PMCPMC7671348

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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.