Reference-informed prediction of alternative splicing and splicing-altering mutations from sequences.
Reference-informed prediction of alternative splicing and splicing-altering mutations from sequences.
复制标题
根据参考文献预测序列中的选择性剪接和剪接改变突变。
DOI：
10.1101/2024.03.22.586363
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发表时间：
2024
期刊：
影响因子：
--
通讯作者：
Zhang,Chaolin
中科院分区：
文献类型：
--
作者：
Xu,Chencheng;Bao,Suying;Chen,Hao;Jiang,Tao;Zhang,Chaolin
选择性剪接在高等真核生物的蛋白质多样性和基因表达调控中起着至关重要的作用，导致剪接失调的突变是一系列遗传疾病的基础。从基因组序列中计算预测选择性剪接不仅可以深入了解基因调控机制，还有助于识别致病突变和药物靶点。然而，目前的剪接位点使用的定量预测方法仍然具有有限的准确性。在这里，我们介绍了DeltaSplice，这是一种深度神经网络模型，经过优化，可以从同源基因的比较分析中了解突变对可变剪接定量变化的影响。该模型架构使DeltaSplice能够通过结合参考基因序列的已知剪接位点使用来执行“参考信息预测”，以改善其对剪接改变突变的预测。我们对DeltaSplice和其他几种最先进的方法进行了各种预测任务的基准测试，包括人类群体和神经发育障碍中谱系特异性剪接和剪接改变突变的进化序列差异，并证明DeltaSplice始终优于其他方法。DeltaSplice预测了人脑中约15%的剪接数量性状基因座（sQTL）作为因果剪接改变变体。它还预测了受自闭症和其他神经发育障碍（NDD）影响的一部分患者中剪接位点外的剪接改变从头突变，包括19个具有复发性剪接改变突变的基因。整合剪接改变突变与其他类型的从头突变负荷允许预测8个新的NDD风险基因。我们的工作扩展了计算机剪接模型的能力，在遗传诊断和基于剪接的精准医学的发展中具有潜在的应用。
Alternative splicing plays a crucial role in protein diversity and gene expression regulation in higher eukaryotes, and mutations causing dysregulated splicing underlie a range of genetic diseases. Computational prediction of alternative splicing from genomic sequences not only provides insight into gene-regulatory mechanisms but also helps identify disease-causing mutations and drug targets. However, the current methods for the quantitative prediction of splice site usage still have limited accuracy. Here, we present DeltaSplice, a deep neural network model optimized to learn the impact of mutations on quantitative changes in alternative splicing from the comparative analysis of homologous genes. The model architecture enables DeltaSplice to perform “reference-informed prediction” by incorporating the known splice site usage of a reference gene sequence to improve its prediction on splicing-altering mutations. We benchmarked DeltaSplice and several other state-of-the-art methods on various prediction tasks, including evolutionary sequence divergence on lineage-specific splicing and splicing-altering mutations in human populations and neurodevelopmental disorders, and demonstrated that DeltaSplice outperformed consistently. DeltaSplice predicted ∼15% of splicing quantitative trait loci (sQTLs) in the human brain as causal splicing-altering variants. It also predicted splicing-altering de novo mutations outside the splice sites in a subset of patients affected by autism and other neurodevelopmental disorders (NDDs), including 19 genes with recurrent splicing-altering mutations. Integration of splicing-altering mutations with other types of de novo mutation burdens allowed the prediction of eight novel NDD-risk genes. Our work expanded the capacity of in silico splicing models with potential applications in genetic diagnosis and the development of splicing-based precision medicine.