ReviewComputational and structural biotechnology journal2026
Long-Read Sequencing Reveals RNA Splicing Complexity in Human Diseases.
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.
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.
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.
Who cites it
1 citing paper in PubMed.
- Cancer-associated fusion transcripts: mechanisms, functional roles, and clinical implications.Clinical and experimental medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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
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Registered trials
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.