Evidence map›Paper›PMID 41597180›Full record

ReviewCells2026

Decoding the lncRNA World: Comprehensive Approaches to lncRNA Structure and Interactome Studies.

Mihyun Oh, Bo Lim Lee, Srinivas Somarowthu

Abstract readReview
In one paragraph

Review in Cells, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

3 authors.

Mihyun OhGraduate Program in Molecular and Cell Biology and Genetics, Graduate School of Biomedical Sciences and Professional Studies, College of Medicine, Drexel University, Philadelphia, PA 19102, USA.ORCID 0000-0003-0660-7703
Bo Lim LeeDepartment of Biochemistry and Molecular Biology, College of Medicine, Drexel University, Philadelphia, PA 19102, USA.
Srinivas SomarowthuDepartment of Biochemistry and Molecular Biology, College of Medicine, Drexel University, Philadelphia, PA 19102, USA.

Funding

Structural and Functional Studies of lncRNAs in Gene ActivationR01GM149780 · NIGMS · DREXEL UNIVERSITY · PI Srinivas Somarowthu · 2023 to 2026
$1.7M
NIH (NIGMS) R01GM149780
6 · The paper itself

Abstract

Recent advances in sequencing technologies have highlighted long non-coding RNAs (lncRNAs) as key regulators that perform essential biological functions without encoding proteins. Despite growing interest, the molecular mechanisms of most lncRNAs remain poorly understood, with only a few characterized in detail. A promising strategy to elucidate these mechanisms is to explore their structure-function relationships. Such studies require advanced biophysical and biochemical methods due to the large size and structural complexity of lncRNAs. Equally important is the analysis of lncRNA interactomes, which reveal how lncRNAs engage RNA-binding proteins and other biomolecules to drive conformational and functional changes underlying diverse biological pathways. Ultimately, integrative approaches combining structural and interactome analyses will yield deeper insight into lncRNA function and uncover new therapeutic opportunities. This review highlights recent advances in elucidating lncRNA structure-function relationships by integrating biophysical, biochemical, and sequencing-based approaches to overcome challenges of size and heterogeneity, identify functional binding partners, and inform therapeutic target development.

Indexed as

RNA, Long NoncodingAnimalsHumansNucleic Acid ConformationRNA-Binding ProteinsRNA-Binding ProteinsRNA, Long Noncodingchemical probinglncRNAsRNA structure

Identifiers

PMID41597180
PMCPMC12838836

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.