five

Ruiz et al. (2024) - eDNA epigenetic clock for seabass

收藏
Figshare2024-06-12 更新2026-04-08 收录
下载链接:
https://figshare.com/articles/dataset/Ruiz_et_al_2024_-_eDNA_epigenetic_clock_for_seabass/25466890/4
下载链接
链接失效反馈
官方服务:
资源简介:
While acquiring age information is crucial for efficient stock management and biodiversity conservation, traditional aging methods fail to offer a universal, non-invasive, and precise way of estimating a wild animal’s age. DNA methylation from tissue DNA (tDNA) was recently proposed as a method to overcome these issues, and showed more accurate results than telomere-length-based age assessments. Here, we used environmental DNA (eDNA) for the first time as a template for age estimation, focusing on the larval phase (10–24 days post-hatch) of cultured <i>Dicentrarchus labrax </i>(seabass), a species of major economic and conservation interest. We were able to directly detect various modification types (<i>e.g.,</i> cytosine and adenosine methylation in all contexts) across the whole genome using amplification-free nanopore sequencing. However, aging sites were only present in the mitogenome, which could be a specific feature of eDNA methylation or the consequence of better DNA protection within mitochondria. By considering qualitative and quantitative information about aging sites according to an objective model selection framework, our epigenetic clock reached a training accuracy of 2.6 days (Median Absolute Error) and provided excellent age estimates afterwards (R² = 0.99). Such performances surpass those of previously established clocks, notably for adult <i>D. labrax</i> even when scaling MAE to the age range, which could be linked to a more dynamic epigenome during early life stages. Overall, our study demonstrates that eDNA can be used for precise age and biodiversity assessments involving third-generation sequencing of mitogenomes, although further methodological developments are needed before field applications can be envisaged.
提供机构:
Sposito, Gérard; Pellissier, Loïc; albouy, camille; RUIZ, Eliot; Lüthi, Martina; Panfili, Jacques; Schmidlin, Michel; Leprieur, Fabien
创建时间:
2024-06-12
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作