A unified quantitative modeling strategy for multiplex assays of variant effect

项目概要/摘要 基因组学的中心目标是了解基因型和表型之间的关系。近年来， 定量研究基因型-表型图谱的能力因 变异效应多重检测 (MAVE)，可测量数千至数百万的分子表型 平行的基因型变异。 MAVE 是一个涵盖性术语，包括大规模并行报告分析 DNA 或 RNA 调控序列的研究，以及蛋白质或蛋白质的深度突变扫描分析 结构RNA。 MAVE 技术在多个基因组学科中的快速采用创造了 迫切需要能够稳健且可重复地推断定量基因型的计算方法 表型 (G-P) 映射来自 MAVE 生成的大型数据集。在这里我们提出一个统一的概念 以及根据 MAVE 数据对 G-P 地图进行定量建模的计算框架。这个提议是 出于我们意识到考虑 MAVE 中无处不在的噪声和非线性的动机 实验需要对 MAVE 测量过程和感兴趣的 G-P 图进行显式建模。 这种联合推理策略比大多数 MAVE 分析方法对计算的要求更高，但它是 使用现代深度学习框架是可行的。我们广泛的初步数据表明，该模型 即使存在重大混杂效应，该策略也能够恢复高精度 G-P 地图，并且 因此有可能使基因组学多个领域的 MAVE 研究受益。目标 1 将开发方法 对不同 MAVE 实验设计中出现的测量过程进行建模。目标2将发展 在 G-P 图谱中模拟遗传相互作用的一般方法，并将结合使用这些方法 通过新的实验来阐明最近批准的靶向药物的分子机制 选择性 mRNA 剪接。目标 3 将开发推断反映生物物理模型的 G-P 图的方法 基因调控，包括热力学（即准平衡）和动力学（即非平衡） 稳态）模型。然后，这些方法将与新的 MAVE 实验结合使用，以 开发一个生物物理模型来解释多效性转录因子如何在整个过程中调节基因表达 大肠杆菌基因组。目标 4 将研究和开发处理规范自由度和草率的方法 上述类别模型中的模式，从而便于比较、解释和探索 推断的 G-P 图。我们开发的所有计算技术都将被纳入一个强大且简单的计算技术中 使用名为 MAVE-NN 的 Python 包。我们将在各种 MAVE 数据集上对 MAVE-NN 进行基准测试， 包括已发布的数据集和作为该项目的一部分生成的数据。总而言之，这项工作将满足以下方面的主要需求： 对 MAVE 实验的分析，产生一个强大、灵活且可扩展的计算平台，这将有助于 加速 MAVE 的使用，以了解人类遗传变异在基因组规模上的影响。

PROJECT SUMMARY / ABSTRACT A central goal of genomics is to understand the relationship between genotype and phenotype. In recent years, the ability to quantitatively study genotype-phenotype maps has been revolutionized by the development of multiplex assays of variant effect (MAVEs), which measure molecular phenotypes for thousands to millions of genotypic variants in parallel. MAVE is an umbrella term that includes massively parallel reporter assays for studies of DNA or RNA regulatory sequences, as well as deep mutational scanning assays of proteins or structural RNAs. The rapid adoption of MAVE techniques across multiple genomic disciplines has created an acute need for computational methods that can robustly and reproducibly infer quantitative genotype- phenotype (G-P) maps from the large datasets that MAVEs produce. Here we propose a unified conceptual and computational framework for quantitatively modeling G-P maps from MAVE data. This proposal is motivated by our realization that accounting for the noise and nonlinearities that are omnipresent in MAVE experiments requires explicit modeling of both the MAVE measurement process and the G-P map of interest. This joint inference strategy is more computationally demanding than most MAVE analysis methods, but it is feasible using modern deep learning frameworks. Our extensive preliminary data show that this modeling strategy is able to recover high-precision G-P maps even in the presence of major confounding effects, and thus has the potential to benefit MAVE studies in multiple areas of genomics. Aim 1 will develop methods for modeling the measurement processes that arise in diverse MAVE experimental designs. Aim 2 will develop general methods for modeling genetic interactions within G-P maps, and will use these methods in conjunction with new experiments to elucidate the molecular mechanism of a recently approved drug that targets alternative mRNA splicing. Aim 3 will develop methods for inferring G-P maps that reflect biophysical models of gene regulation, including both thermodynamic (i.e., quasi-equilibrium) and kinetic (i.e., non-equilibrium steady-state) models. These methods will then be used, in conjunction with new MAVE experiments, to develop a biophysical model for how a pleiotropic transcription factor regulates gene expression throughout the Escherichia coli genome. Aim 4 will study and develop methods for treating gauge freedoms and sloppy modes in the above classes of models, thereby facilitating the comparison, interpretation, and exploration of inferred G-P maps. All of the computational techniques we develop will be incorporated into a robust and easy- to-use Python package called MAVE-NN. We will benchmark MAVE-NN on a diverse array of MAVE datasets, including published datasets and data generated as part of this project. In all, this work will fill a major need in the analysis of MAVE experiments, yielding a robust, flexible, and scalable computational platform that will help accelerate the use of MAVEs for understanding the effects of human genetic variation at the genomic scale.