Wang Yu, Liang Nan, Gao Ge
State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Biomedical Pioneering Innovative Center (BIOPIC) and Beijing Advanced Innovation Center for Genomics (ICG), Center for Bioinformatics (CBI), Peking University, 100871, Beijing, China.
Changping Laboratory, 102206, Beijing, China.
Nat Commun. 2024 Dec 30;15(1):10917. doi: 10.1038/s41467-024-55392-7.
Deciphering how noncoding DNA determines gene expression is critical for decoding the functional genome. Understanding the transcription effects of noncoding genetic variants are still major unsolved problems, which is critical for downstream applications in human genetics and precision medicine. Here, we integrate regulatory-specific neural networks and tissue-specific gradient-boosting trees to build SVEN: a hybrid sequence-oriented architecture that can accurately predict tissue-specific gene expression level and quantify the tissue-specific transcriptomic impacts of structural variants across more than 350 tissues and cell lines. We further systematically screen a large-scale structural variants dataset derived from 3622 individuals and clinical structural variants from ClinVar, and provide an overview of transcriptomic impacts of structural variants in population. As a sequence-oriented model, SVEN is also able to predict regulatory effects for small noncoding variants. We expect that SVEN will enable more effective in silico analysis and interpretation of human genome-wide disease-related genetic variants.
解读非编码DNA如何决定基因表达对于解码功能基因组至关重要。了解非编码基因变异的转录效应仍是主要未解决的问题，这对于人类遗传学和精准医学的下游应用至关重要。在此，我们整合了调控特异性神经网络和组织特异性梯度提升树来构建SVEN：一种面向序列的混合架构，它可以准确预测组织特异性基因表达水平，并量化跨350多个组织和细胞系的结构变异的组织特异性转录组影响。我们进一步系统地筛选了来自3622名个体的大规模结构变异数据集以及ClinVar中的临床结构变异，并概述了群体中结构变异的转录组影响。作为一种面向序列的模型，SVEN还能够预测小非编码变异的调控效应。我们期望SVEN将实现对人类全基因组疾病相关基因变异更有效的计算机分析和解释。
