Deciphering transcriptome complexity via long‐read sequencing
14 affiliations
- 1School of Mathematics and Statistics Wuhan University Wuhan China
- 2Department of Reproductive Medicine The First Affiliated Hospital of Xi'an Jiaotong University Xi'an China
- 3State Key Laboratory of Medical Proteomics, National Center for Protein Sciences (Beijing) Research Unit of Proteomics‐driven Cancer Precision Medicine (Chinese Academy of Medical Sciences) Beijing China
- 4The Conservation of Endangered Wildlife Key Laboratory of Sichuan Province Chengdu Research Base of Giant Panda Breeding Chengdu China
- 5Department of Urology of The First Affiliated Hospital & Liangzhu Laboratory Zhejiang University School of Medicine Hangzhou China
- 6Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China
- 7Chinese Institutes for Medical Research (CIMR) Beijing China
- 8Department of Neurology Xuanwu Hospital Capital Medical University, National Center for Neurological Disorders Beijing China
- 9Beijing Municipal Geriatric Medical Research Center Beijing China
- 10School of Artificial Intelligence Wuhan University Wuhan China
- 11Stanley and Judith Frankel Institute for Heart and Brain Health University of Michigan Medical Center Ann Arbor Michigan USA
- 12Benagen Institute Wuhan China
- 13Institute for Integrative Systems Biology Spanish National Research Council Paterna Spain
- 14Institute for Math & AI Wuhan University Wuhan China
Abstract
Transcriptomics is moving beyond gene‐level quantification toward isoform‐resolved interrogation of alternative splicing, transcript structural variation, and repeat‐derived transcription. Yet short‐read sequencing remains intrinsically limited in accurately reconstructing full‐length transcripts and resolving complex repetitive regions, including transposable elements. Recent advances in long‐read sequencing, exemplified by Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT), offer a transformative opportunity to directly observe complete RNA molecules and thereby overcome these bottlenecks. However, practical adoption is hindered by demanding library construction and the need to process noisy, fast‐evolving long‐read data, and the field still lacks a unified resource that guides researchers through the entire experimental and analytical workflow. This review fills that gap by providing a concise, end‐to‐end, and implementation‐oriented roadmap for long‐read transcriptomics. We distill the key decisions from platform and library strategy selection to core computational processing and downstream interpretation, and we summarize emerging frontiers and best‐practice recommendations. By offering a reusable framework and practical checklists, this guide empowers a broader community to exploit long reads for standardized, reproducible, isoform‐level discovery at unprecedented resolution.
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BibTeX
@article{xu2026deciphering,
title = {{Deciphering transcriptome complexity via long‐read sequencing}},
author = {Chuwen Xu and Jia Li and Chenxi Yin and Keying Li and Tian Wang and Shiwen Gao and Yue Yu and Cheng Chang and Ye Wang and Guoqing Tong and Liang Gong and Wen Hu and Guoliang Chai and Yilai Han and Qian Qin and Suoqin Jin and Xiufen Zou and Bo Li and Tianyuan Zhang and Ana Conesa and Yunhao Wang and Dingjie Wang},
journal = {iMeta},
year = {2026},
month = oct,
doi = {10.1002/imt2.70171},
url = {https://doi.org/10.1002/imt2.70171},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Deciphering transcriptome complexity via long‐read sequencing
AU - Chuwen Xu
AU - Jia Li
AU - Chenxi Yin
AU - Keying Li
AU - Tian Wang
AU - Shiwen Gao
AU - Yue Yu
AU - Cheng Chang
AU - Ye Wang
AU - Guoqing Tong
AU - Liang Gong
AU - Wen Hu
AU - Guoliang Chai
AU - Yilai Han
AU - Qian Qin
AU - Suoqin Jin
AU - Xiufen Zou
AU - Bo Li
AU - Tianyuan Zhang
AU - Ana Conesa
AU - Yunhao Wang
AU - Dingjie Wang
PY - 2026
DA - 2026/10/04
JO - iMeta
DO - 10.1002/imt2.70171
UR - https://doi.org/10.1002/imt2.70171
AB - Transcriptomics is moving beyond gene‐level quantification toward isoform‐resolved interrogation of alternative splicing, transcript structural variation, and repeat‐derived transcription. Yet short‐read sequencing remains intrinsically limited in accurately reconstructing full‐length transcripts and resolving complex repetitive regions, including transposable elements. Recent advances in long‐read sequencing, exemplified by Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT), offer a transformative opportunity to directly observe complete RNA molecules and thereby overcome these bottlenecks. However, practical adoption is hindered by demanding library construction and the need to process noisy, fast‐evolving long‐read data, and the field still lacks a unified resource that guides researchers through the entire experimental and analytical workflow. This review fills that gap by providing a concise, end‐to‐end, and implementation‐oriented roadmap for long‐read transcriptomics. We distill the key decisions from platform and library strategy selection to core computational processing and downstream interpretation, and we summarize emerging frontiers and best‐practice recommendations. By offering a reusable framework and practical checklists, this guide empowers a broader community to exploit long reads for standardized, reproducible, isoform‐level discovery at unprecedented resolution.
SN - 2770-5986
SN - 2770-596X
ER -