scEvoSel maps evolutionary selection signals onto single-cell transcriptomic atlases to reveal selection-relevant cellular contexts

Clicks: 13
ID: 337648
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #272 of 278 articles by views in molecular biology and evolution

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 278 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Selection is a core driving force of evolution. Although genome-wide scanning can identify numerous genomic regions affected by selection, it remains challenging to further reveal the specific cellular contexts in which these selection signals act. To address this issue, we developed scEvoSel, an analytical framework that integrates population-level selection signals with single-cell RNA sequencing data to identify selection-relevant cell types. We calculated DCMS scores based on the population variation data from wild boars and domestic pigs and mapped them to the single-cell atlas of skeletal muscle from wild boars and Duroc pigs. The results showed that scEvoSel accurately identified fibro-adipogenic progenitors (FAPs) and myocytes, which exhibited significant changes in cell proportions, as well as lymphoid cells, which showed breed-associated transcriptional divergence. Moreover, we also calculated DCMS scores derived from population variation data of red junglefowl and Leghorn chickens and mapped them to the single-cell atlas of the liver of Leghorn chicken. Hepatocytes, endothelial cells, Kupffer cells and B cells were identified by scEvoSel as the primary selection-relevant cell types. Additionally, scEvoSel enables the analysis of key genes and functional pathways affected by selection within the corresponding cellular contexts. Overall, scEvoSel provides a framework for resolving population selection signals at single-cell resolution, expands the research dimensions for functional interpretation of selection signals, and offers a methodological tool for investigating cellular contexts associated with selection-driven evolutionary processes and complex trait formation.
Reference Key
openalex_W7224129965 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Simin Zhao, Yizhou Hu, Guobin Chang, Qixin Guo
Are you one of these authors? Claim your profile
Journal molecular biology and evolution
Year 2026
DOI
10.1093/molbev/msag271
URL
https://doi.org/10.1093/molbev/msag271
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.