Massively parallel reporter assays (MPRAs) are powerful tools for quantifying the impacts of sequence variation on gene expression. Reading out molecular phenotypes with sequencing enables interrogating the impact of sequence variation beyond genome scale. Machine learning models integrate and codify information learned from MPRAs and enable generalization by predicting sequences outside the training data set. Models can provide a quantitative understanding of cis-regulatory codes controlling gene expression, enable variant stratification, and guide the design of synthetic regulatory elements for applications from synthetic biology to mRNA and gene therapy. This review focuses on cis-regulatory MPRAs, particularly those that interrogate cotranscriptional and post-transcriptional processes: alternative splicing, cleavage and polyadenylation, translation, and mRNA decay.

中文翻译：

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使用大规模并行报告基因检测和机器学习解码生物学


大规模平行报告基因检测 （MPRA） 是量化序列变异对基因表达影响的强大工具。通过测序读出分子表型可以询问序列变异对基因组规模之外的影响。机器学习模型整合和编纂从 MPRA 中学习的信息，并通过预测训练数据集之外的序列来实现泛化。模型可以提供对控制基因表达的顺式调控密码的定量理解，实现变异分层，并指导合成调控元件的设计，用于从合成生物学到 mRNA 和基因治疗的应用。本综述重点介绍顺式调节 MPRAs，特别是那些询问共转录和转录后过程的 MPRAs：选择性剪接、切割和多聚腺苷酸化、翻译和 mRNA 衰变。