Mondragon-Estrada Enrique, Morton Sarah U
Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Boston, MA 02115, USA; Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA 02115, USA.
Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Boston, MA 02115, USA; Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA.
STAR Protoc. 2025 Apr 7;6(2):103738. doi: 10.1016/j.xpro.2025.103738.
Functional impact of noncoding variants can be predicted using computational approaches. Although predictive scores can be insightful, implementing the scores for a custom variant set and associating scores with complex traits require multiple phases of analysis. Here, we present a protocol for prioritizing variants by generating deep-learning-predicted functional scores and relating them with brain traits. We describe steps for score prediction, statistical comparison, phenotype correlation, and functional enrichment analysis. This protocol can be generalized to different models and phenotypes. For complete details on the use and execution of this protocol, please refer to Mondragon-Estrada et al..
非编码变异的功能影响可以通过计算方法进行预测。尽管预测分数可能具有启发性，但为自定义变异集实施这些分数并将分数与复杂性状相关联需要多个分析阶段。在这里，我们提出了一个协议，通过生成深度学习预测的功能分数并将它们与脑性状相关联来对变异进行优先级排序。我们描述了分数预测、统计比较、表型相关性和功能富集分析的步骤。该协议可以推广到不同的模型和表型。有关此协议的使用和执行的完整详细信息，请参考蒙德拉贡 - 埃斯特拉达等人的研究。
