Evidence map›Paper›PMID 40657556›Full record

ReviewAdvanced genetics (Hoboken, N.J.)2025

Decoding RNA-Protein Interactions: Methodological Advances and Emerging Challenges.

Wenkai Yi, Jian Yan

Abstract readReview
In one paragraph

Review in Advanced genetics (Hoboken, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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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.

Wenkai YiDepartment of Biomedical Sciences Tung Biomedical Sciences Centre City University of Hong Kong Kowloon Tong Hong Kong.ORCID https://orcid.org/0000-0002-0113-2975
Jian YanDepartment of Biomedical Sciences Tung Biomedical Sciences Centre City University of Hong Kong Kowloon Tong Hong Kong.ORCID https://orcid.org/0000-0002-1267-2870

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

RNA-protein interactions are fundamental to cellular processes such as gene regulation and RNA metabolism. Over the past decade, significant advancements in methodologies have transformed the ability to study these interactions with unprecedented resolution and specificity. This review systematically compares RNA- and protein-centric approaches, highlighting their strengths, limitations, and optimal applications. RNA-centric methods, including hybridization-based pulldowns, proximity labeling, and CRISPR-assisted techniques, enable the identification of proteins interacting with specific RNAs, even low-abundance or transient partners. Protein-centric strategies, such as immunoprecipitation-based CLIP-seq, and emerging proximity-tagging methods, map RNA interactomes of RNA-binding proteins with nucleotide precision. This study evaluates key innovations like LACE-seq and ARTR-seq, which minimize cell input requirements, and HyPro-MS, which bypasses genetic modifications. Guidelines for method selection are provided, emphasizing experimental goals, RNA abundance, interaction dynamics, and technical constraints. Critical challenges are also discussed, including capturing low-affinity interactions, resolving RNA structural complexities, and integrating multi-omics data. This review underscores the importance of method-tailoring to biological contexts, offering a roadmap for researchers to navigate the evolving landscape of RNA-protein interaction studies. By bridging technical advancements with practical recommendations, this study aims to accelerate discoveries in RNA biology, therapeutic development, and precision medicine.

Indexed as

Protein‐centric methodsRNARNA‐binding proteinsRNA‐centric methodsRNA–protein interactions

Identifiers

PMID40657556
PMCPMC12245534

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.