Paradigms, innovations, and biological applications of RNA velocity: a comprehensive review
Wang Y., Li J., Zha H., Liu S., Huang D., Fu L.
Narrative Review on Hip, published in Brief Bioinform (2025) — summary generated from the PubMed abstract.
Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.
- Level A · Stronger Clinical Evidence
- Level B · Emerging clinical evidence with positive signals
- Level C · Early human research exploring benefits
- Level D · Scientific groundwork from lab and animal studies
- Emerging · Emerging topic under active research
This page is generated from the PubMed record. The Thai description is an automated summary of bibliographic fields and the abstract, not a full translation, and is not medical advice.
- Study type
- Narrative Review
- Journal
- Brief Bioinform (2025)
- Reported sample size
- —
- Source database
- Europe PMC
- PMID
- 40668554
- PMCID
- PMC12265890
- DOI
- 10.1093/bib/bbaf339
- Citations
- 1
Abstract (original English)
Single-cell RNA sequencing enables unprecedented insights into cellular heterogeneity and lineage dynamics. RNA velocity, by modeling the temporal relationship between spliced and unspliced transcripts, extends this capability to predict future transcriptional states and uncover the directionality of cellular transitions. Since the introduction of foundational frameworks such as Velocyto and scVelo, an expanding array of computational tools has emerged, each based on distinct biophysical assumptions and modeling paradigms. To provide a structured overview of this rapidly evolving field, we categorize RNA velocity models into three classes: steady-state methods, trajectory methods, and state extrapolation methods, according to their underlying approaches to transcriptional kinetics inference. For each category, we systematically analyze both the overarching principles and the individual methods, comparing their assumptions, kinetic models, and computational strategies and assessing their respective strengths and limitations. To demonstrate the biological utility of these tools, we summarize representative applications of RNA velocity across developmental biology and diseased microenvironments. We further introduce emerging extensions of RNA velocity methods that go beyond classical splicing kinetics. Finally, we discuss existing limitations regarding model assumptions, preproces
What this study does not prove
- • This study does not prove SVF is an approved treatment or a replacement for standard care.
- • This is a narrative review: it collects no new patient data and does not systematically appraise evidence quality.
Evidence level
Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.
How we grade evidenceBrowse all related research
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