Evidence map›Paper›PMID 40247051›Full record

ReviewNature genetics2025

Circular RNA discovery with emerging sequencing and deep learning technologies.

Jinyang Zhang, Fangqing Zhao

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 1 pooled it
–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

25 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  11. Exploring the CeRNA landscape in plants: advances, methods, and challenges.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026
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  19. Target, silence, replace: a review on RNA-based drugs in modern medicine.Frontiers in cell and developmental biology · 2026
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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

2 authors.

Jinyang ZhangInstitute of Zoology, Chinese Academy of Sciences, Beijing, China. zhangjinyang@ioz.ac.cn.ORCID http://orcid.org/0000-0002-5163-894X
Fangqing ZhaoInstitute of Zoology, Chinese Academy of Sciences, Beijing, China. zhfq@ioz.ac.cn.ORCID http://orcid.org/0000-0002-6216-1235

Funding

National Science Foundation of China | National Natural Science Foundation of China-Yunnan Joint Fund (NSFC-Yunnan Joint Fund) 32025009, 32130020
6 · The paper itself

Abstract

Circular RNA (circRNA) represents a type of RNA molecule characterized by a closed-loop structure that is distinct from linear RNA counterparts. Recent studies have revealed the emerging role of these circular transcripts in gene regulation and disease pathogenesis. However, their low expression levels and high sequence similarity to linear RNAs present substantial challenges for circRNA detection and characterization. Recent advances in long-read and single-cell RNA sequencing technologies, coupled with sophisticated deep learning-based algorithms, have revolutionized the investigation of circRNAs at unprecedented resolution and scale. This Review summarizes recent breakthroughs in circRNA discovery, characterization and functional analysis algorithms. We also discuss the challenges associated with integrating large-scale circRNA sequencing data and explore the potential future development of artificial intelligence (AI)-driven algorithms to unlock the full potential of circRNA research in biomedical applications.

Indexed as

Deep LearningRNA, CircularSequence Analysis, RNAAlgorithmsAnimalsArtificial IntelligenceHigh-Throughput Nucleotide SequencingHumansSingle-Cell AnalysisRNA, Circular

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.