Evidence map›Paper›PMID 38782298›Full record

ArticleJournal of advanced research2025

Systematic evaluation of single-cell RNA-seq analyses performance based on long-read sequencing platforms.

Enze Deng, Qingmei Shen, Jingna Zhang, Yaowei Fang, Lei Chang, Guanzheng Luo, Xiaoying Fan

Abstract read
In one paragraph

Article in Journal of advanced research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Review
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  6. Article
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  8. Article
  9. Long and Accurate: How HiFi Sequencing is Transforming Genomics.Genomics, proteomics & bioinformatics · 2025
    Review
  10. Article
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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

7 authors.

Enze DengMOE Key Laboratory of Gene Function and Regulation, Guangdong Province Key Laboratory of Pharmaceutical Functional Genes, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China; Guangzhou National Laboratory, No. 9 XingDaoHuanBei Road, Guangzhou International Bio Island, Guangzhou 510005, Guangdong Province, China.
Qingmei ShenGuangzhou National Laboratory, No. 9 XingDaoHuanBei Road, Guangzhou International Bio Island, Guangzhou 510005, Guangdong Province, China; GMU-GIBH Joint School of Life Sciences, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou 510005, China.
Jingna ZhangGuangzhou National Laboratory, No. 9 XingDaoHuanBei Road, Guangzhou International Bio Island, Guangzhou 510005, Guangdong Province, China.
Yaowei FangGuangzhou National Laboratory, No. 9 XingDaoHuanBei Road, Guangzhou International Bio Island, Guangzhou 510005, Guangdong Province, China.
Lei ChangGMU-GIBH Joint School of Life Sciences, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou 510005, China.
Guanzheng LuoMOE Key Laboratory of Gene Function and Regulation, Guangdong Province Key Laboratory of Pharmaceutical Functional Genes, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China.
Xiaoying FanGuangzhou National Laboratory, No. 9 XingDaoHuanBei Road, Guangzhou International Bio Island, Guangzhou 510005, Guangdong Province, China; GMU-GIBH Joint School of Life Sciences, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou 510005, China. Electronic address: fan_xiaoying@gzlab.ac.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe rapid development of next-generation sequencing (NGS)-based single-cell RNA sequencing (scRNA-seq) allows for detecting and quantifying gene expression in a high-throughput manner, providing a powerful tool for comprehensively understanding cellular function in various biological processes. However, the NGS-based scRNA-seq only quantifies gene expression and cannot reveal the exact transcript structures (isoforms) of each gene due to the limited read length. On the other hand, the long read length of third-generation sequencing (TGS) technologies, including Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio), enable direct reading of intact cDNA molecules.

objectivesBoth ONT and PacBio have been used in conjunction with scRNA-seq, but their performance in single-cell analyses has not been systematically evaluated.

methodsTo address this, we generated ONT and PacBio data from the same single-cell cDNA libraries containing different amount of cells.

resultsUsing NGS as a control, we assessed the performance of each platform in cell type identification. Additionally, the reliability in identifying novel isoforms and allele-specific gene/isoform expression by both platforms was verified, providing a systematic evaluation to design the sequencing strategies in single-cell transcriptome studies.

conclusionBeyond gene expression analysis, which the NGS-based scRNA-seq only affords, TGS-based scRNA-seq achieved gene splicing analyses, identifying novel isoforms. Attribute to higher sequencing quality of PacBio, it outperforms ONT in accuracy of novel transcripts identification and allele-specific gene/isoform expression.

Indexed as

High-Throughput Nucleotide SequencingRNA-SeqSequence Analysis, RNASingle-Cell AnalysisAnimalsGene Expression ProfilingGene LibraryHumansSingle-Cell Gene Expression AnalysisAllele-specific gene/isoformCell type identificationNovel isoformSingle cell RNA-sequencingThird-generation sequencing

Identifiers

PMID38782298
PMCPMC12126699

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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.