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拼接阅读:通过剩余块和合成数据增强改进正规和非正规拼接位置预测

Sahil Thapa, Khushali Samderiya, Rohit Menon

bioRxiv : the preprint server for biology

|February 23, 2026

概括

新的深度学习模型SpliceRead准确地预测了常见和罕见的拼接位置. 它通过使用合成数据和残留连接来克服现有方法的局限性,改进基因表达分析.

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:

准确的拼接部位预测对于理解基因表达和相关疾病至关重要.
现有的模型往往对正规拼接地点有偏见,导致罕见的,非正规变异的检测不佳,以及由于不平衡的数据集导致的高错误负率.

研究的目的:

开发一种新的深度学习模型,SpliceRead,以改进规范和非规范拼接站点的分类.
解决现有模型在检测罕见拼接位变异方面的局限性,并提高整体拼接位预测准确性.

主要方法:

开发了SpliceRead,这是一个包含剩余卷积块和合成数据增强的深度学习模型.
利用数据增强策略生成多种非正规拼接位序列.
采用剩余连接来促进梯度流并捕获微妙的基因组序列特征.

主要成果:

在多种数据集上,SpliceRead在关键指标 (F1分数,准确性,精度,回忆) 上表现优于最先进的模型.
与基线方法相比,在非正典拼接位点实现了显著较低的错误分类率.
通过交叉验证,跨物种测试和输入长度概括的验证证实了模型的稳定性和适应性.

结论:

拼接阅读为拼接位置预测提供了一个强大的和可泛化的框架,对于低频,非正规变体特别有效.
该模型提高了模型和非模型生物的基因注释的准确性.
开源代码有助于进一步的研究和应用在拼接地点分析.

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