Over the last decade, a rich variety of massively parallel assays have revolutionized our understanding of how biological sequences encode quantitative molecular phenotypes. These assays include deep mutational scanning, high-throughput SELEX, and massively parallel reporter assays. Here, we review these experimental methods and how the data they produce can be used to quantitatively model sequence-function relationships. In doing so, we touch on a diverse range of topics, including the identification of clinically relevant genomic variants, the modeling of transcription factor binding to DNA, the functional and evolutionary landscapes of proteins, and cis-regulatory mechanisms in both transcription and mRNA splicing. We further describe a unified conceptual framework and a core set of mathematical modeling strategies that studies in these diverse areas can make use of. Finally, we highlight key aspects of experimental design and mathematical modeling that are important for the results of such studies to be interpretable and reproducible.

中文翻译：

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大规模平行测定和定量序列-功能关系。

在过去的十年中，种类繁多的大规模平行测定彻底改变了我们对生物序列如何编码定量分子表型的理解。这些检测包括深度突变扫描、高通量 SELEX 和大规模平行报告检测。在这里，我们回顾了这些实验方法以及它们产生的数据如何用于对序列-函数关系进行定量建模。在此过程中，我们涉及了广泛的主题，包括临床相关基因组变异的鉴定、转录因子与 DNA 结合的建模、蛋白质的功能和进化景观，以及转录和 mRNA 剪接中的顺式调控机制. 我们进一步描述了一个统一的概念框架和一组核心数学建模策略，这些不同领域的研究可以利用这些策略。最后，我们强调了实验设计和数学建模的关键方面，这些方面对于此类研究结果的可解释性和可重复性很重要。