FexSplice:一种基于LightGBM的模型,用于预测单个核酸变体的剪接效应,该变体会影响前子的第一个核酸G
Atefeh Joudaki1, Jun-Ichi Takeda1, Akio Masuda1
1Division of Neurogenetics, Center for Neurological Diseases and Cancer, Nagoya University Graduate School of Medicine, 65 Tsurumai, Showa-ku, Nagoya 466-8550, Japan.
Genes
|September 28, 2023
概括
第一个外体核酸中的单核酸变体 (Fex-SNVs) 可以改变mRNA前拼接. 一个新的机器学习模型,FexSplice,根据序列特征预测拼接效应,帮助疾病变异分析.
科学领域:
- 基因组学就是基因组学.
- 分子生物学分子生物学
- 生物信息学是一种生物信息学.
背景情况:
- 异位子 (Fex-SNVs) 的第一个核酸中的单核酸变体 (SNVs) 与疾病有关,但它们的拼接影响尚不清楚.
- 现有的方法难以预测Fex-SNV的拼接后果,并且没有专门的数据库可用.
研究的目的:
- 从文献和数据库中识别和描述影响拼接的和中性的Fex-SNV.
- 开发Fex-SNVs引起的异常拼接的预测模型.
- 创建一个用户友好的网络服务,用于预测Fex-SNV拼接效应.
主要方法:
- 文献审查和dbSNP数据库挖掘,以治理影响拼接的106个和106个中性的Fex-SNVs.
- 提取与拼接 cis 元素相关的 115 个特征,包括聚胺系特征.
- 使用十倍交叉验证开发和评估机器学习模型 (SVM,随机森林,LightGBM).
主要成果:
- 轻GBM模型在区分从中性Fex-SNVs影响的拼接方面表现出了卓越的性能.
- 预测的关键特征包括聚皮里米丁管道的长度和核酸组成.
- 最好的LightGBM模型实现了高精度 (0.80),MCC (0.57),AUROC (0.86) 和AUPRC (0.87) 的高精度.
结论:
- Fex-SNVs可以显著影响mRNA前拼接,并且它们的效应可以使用机器学习来预测.
- 开发的FexSplice网络服务为评估Fex-SNV的拼接潜力提供了宝贵的工具.
- 这项工作增强了对影响基因表达和疾病的遗传变异的理解和预测.
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