SynMall资源用于描述同名变化的功能影响
Chen Ye1, Xiaoyan Li1, Na Cheng2
1Anhui University.
Genome research
|December 17, 2025
概括
同义单核酸变体 (sSNVs) 可以影响疾病,但缺乏注释. SynMall是一个新的资源,它用功能见解和机器学习工具SynScore对2500万个人类sSNV进行了编目,用于病原性预测.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 同义单核酸变体 (sSNVs) 被认为是疾病的贡献者.
- 现有的数据库为sSNV提供了有限的功能洞察力.
- 解读同义变化的功能影响至关重要.
研究的目的:
- 介绍SynMall,这是一个全面的资源,用于破译同义变化的功能影响.
- 为人类sSNVs提供多层次的注释,整合进化和人口数据.
- 开发SynScore,这是一个机器学习框架,用于优先考虑致病性sSNVs.
主要方法:
- 编目了2500万个潜在的人类sSNV,并整合了45种非人类物种的数据.
- 提供多层次的注释 (DNA,RNA,蛋白质),包括ACMG指南,等位基因频率和手工制作/LLM特征.
- 开发了SynScore,这是一个机器学习模型,集成ACMG指南和生物特征来推断病原性.
主要成果:
- SynMall集成了2500万人类sSNV的进化,人口和多层次注释.
- SynScore在全基因组致病性推断方面实现了最先进的性能.
- SynMall促进了对miRNA-mRNA相互作用,拼接,稳定性和密码子使用的sSNV影响的机械探索.
结论:
- SynMall为了解同名突变的功能作用提供了一个有价值的,统一的平台.
- SynScore有效地优先考虑致病性sSNV,帮助疾病研究.
- 该资源支持高级表示学习和对同名变化的机制探索.
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