Evidence map›Paper›PMID 42017050›Full record

ReviewComputational and structural biotechnology journal2026

Long-Read Sequencing Reveals RNA Splicing Complexity in Human Diseases.

Xiangmin Tan, Ping Wang, Yang Li, Bo Yuan, Qiang Sun, Yaran Liu

Abstract readReview
In one paragraph

Review in Computational and structural biotechnology journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Xiangmin TanShandong Key Lab of Complex Medical Intelligence and Aging, Shandong Medical and Pharmaceutical University, Yantai 264003, Shandong, P. R. China.
Ping WangShandong Key Lab of Complex Medical Intelligence and Aging, Shandong Medical and Pharmaceutical University, Yantai 264003, Shandong, P. R. China.ORCID https://orcid.org/0009-0000-3105-9855
Yang LiCenter for RNA Medicine, the Fourth Affiliated Hospital of School of Medicine, International School of Medicine, Zhejiang University, Yiwu 322000, Zhejiang, P. R. China.ORCID https://orcid.org/0000-0002-8200-1546
Bo YuanInstitute of Artificial Intelligence, Beihang University, Beijing 100191, P. R. China.
Qiang SunCenter for RNA Medicine, the Fourth Affiliated Hospital of School of Medicine, International School of Medicine, Zhejiang University, Yiwu 322000, Zhejiang, P. R. China.ORCID https://orcid.org/0000-0002-6621-7064
Yaran LiuShandong Key Lab of Complex Medical Intelligence and Aging, Shandong Medical and Pharmaceutical University, Yantai 264003, Shandong, P. R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Transcriptome sequencing is essential for understanding gene expression and RNA features. However, short-read RNA sequencing struggles to analyze complex and full-length messenger RNA molecules. These limitations primarily arise from fragmented read lengths, which make it difficult to accurately characterize alternative splicing patterns, exon structures, or transcription start and termination sites. Long-read RNA sequencing (lrRNA-seq) is an innovative technology that has revolutionized transcriptomic analysis. By end-to-end sequencing, it provides comprehensive insights into transcriptomic structural and regulatory complexity. Moreover, by eliminating the need for transcript assembly and reducing inference errors associated with short-read data, lrRNA-seq can precisely determine exon-intron structures, alternative splicing patterns, transcription initiation and termination sites, alternative polyadenylation, and noncanonical RNA processing events. In this review, we provide a detailed overview of the working principles and technological innovations of lrRNA-seq and emphasize its advantages in transcriptome research. In addition, we systematically assess the methodological aspects, focusing on isoform analysis, quantification, error correction, and algorithm development, which improve the reliability of lrRNA-seq analyses. We further discuss recent applications and developments of lrRNA-seq related to various diseases. Recent studies have revealed disease-related splicing dysregulation, discovered novel pathogenic isoforms, and clarified RNA-mediated mechanisms. Furthermore, we discuss emerging efforts to integrate long-read sequencing with single-cell and spatial transcriptomics, thereby permitting the characterization of splicing complexity across specific cells, tissues, and microenvironments within the whole organism. In conclusion, lrRNA-seq is a transformative technology for advancing disease diagnostics and precision medicine.

Identifiers

PMID42017050
PMCPMC13094348

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.