Evidence map›Paper›PMID 39596636›Full record

ArticleGenes2024

Illumina SBS Sequencing and DNBSEQ Perform Similarly for Single-Cell Transcriptomics.

Nadine Bestard-Cuche, David A D Munro, Meryam Beniazza, Josef Priller, Anna Williams, Andrea Corsinotti

Abstract read
In one paragraph

Article in Genes, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. From DNA to Big Data: NGS Technologies and Their Applications.Methods in molecular biology (Clifton, N.J.) · 2025
    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

6 authors.

Nadine Bestard-CucheCentre for Regenerative Medicine, Institute for Regeneration and Repair, MS Society Edinburgh Centre for MS Research, University of Edinburgh, Edinburgh EH16 4UU, UK.
David A D MunroUK Dementia Research Institute, University of Edinburgh, Edinburgh EH8 9AL, UK.
Meryam BeniazzaCentre for Regenerative Medicine, Institute for Regeneration and Repair, MS Society Edinburgh Centre for MS Research, University of Edinburgh, Edinburgh EH16 4UU, UK.
Josef PrillerUK Dementia Research Institute, University of Edinburgh, Edinburgh EH8 9AL, UK.
Anna WilliamsCentre for Regenerative Medicine, Institute for Regeneration and Repair, MS Society Edinburgh Centre for MS Research, University of Edinburgh, Edinburgh EH16 4UU, UK.ORCID 0000-0002-6329-382X
Andrea CorsinottiCentre for Regenerative Medicine, Institute for Regeneration and Repair, MS Society Edinburgh Centre for MS Research, University of Edinburgh, Edinburgh EH16 4UU, UK.ORCID 0000-0003-4481-0999

Funding

Multiple Sclerosis Society NAUK Dementia Research Institute NAUniversity of Edinburgh NA
6 · The paper itself

Abstract

BACKGROUND/

objectivesHigh-throughput single-cell RNA sequencing (scRNA-seq) workflows produce libraries that demand extensive sequencing. However, standard next-generation sequencing (NGS) methods remain expensive, contributing to the high running costs of single-cell experiments and often negatively affecting the sample numbers and statistical strength of such projects. In recent years, a plethora of new sequencing technologies have become available to researchers through several manufacturers, often providing lower-cost alternatives to standard NGS.

methodsIn this study, we compared data generated from mouse scRNA-seq libraries sequenced with both standard Illumina sequencing by synthesis (Illumina SBS) and MGI's DNA nanoball sequencing (DNBSEQ).

resultsOur findings reveal similar overall performance using both technologies. DNBSEQ exhibited mildly superior sequence quality compared to Illumina SBS, as evidenced by higher Phred scores, lower read duplication rates and a greater number of genes mapping to the reference genome. Yet these improvements did not translate into meaningful differences in single-cell analysis parameters in our experiments, including detection of additional genes within cells, gene expression saturation levels and numbers of identified cells, with both technologies demonstrating equally robust performance in these aspects. The data produced by both sequencing platforms also produced comparable analytical outcomes for single-cell analysis. No significant difference in the annotation of cells into different cell types was observed and the same top genes were differentially expressed between populations and experimental conditions.

conclusionsOverall, our data demonstrate that alternative technologies can be applied to sequence scRNA-seq libraries, generating virtually indistinguishable results compared to standard methods, and providing cost-effective alternatives.

Indexed as

High-Throughput Nucleotide SequencingSequence Analysis, RNASingle-Cell AnalysisAnimalsGene Expression ProfilingMiceTranscriptomeDNBSEQ sequencingIllumina SBS sequencingsingle-cell RNAseq

Identifiers

PMID39596636
PMCPMC11594097

What OpenQuestion holds

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LicenceCC BY
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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.