ReviewiMeta2026
Deciphering transcriptome complexity via long-read sequencing.
Review in iMeta, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
22 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Identifiers
What OpenQuestion holds
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.