A unified quantitative modeling strategy for multiplex assays of variant effect

用于变异效应多重分析的统一定量建模策略

• 批准号: 10646167
• 负责人: JUSTIN B. KINNEY
• 金额: $ 80.55万
• 依托单位: COLD SPRING HARBOR LABORATORY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-15 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10646167
• 关键词: Acceleration; Accounting; Acute; Address; Adopted; Adoption; Architecture; Area; Benchmarking; Biological Assay; Biology; Clinical; Communicable Diseases; Computational Technique; Computer Analysis; Computing Methodologies; DNA; Data; Data Set; Defect; Development; Dimensions; Discipline; Disease; Drug Targeting; Effectiveness; Equilibrium; Escherichia coli; Evolution; Experimental Designs; Failure; Freedom; Gene Expression; Gene Expression Regulation; Genetic; Genetic Models; Genetic Transcription; Genetic Variation; Genome; Genomic DNA; Genomics; Genotype; Goals; Human Genetics; Joints; Kinetics; Maps; Mathematics; Measurement; Measures; Messenger RNA; Methods; Modeling; Modernization; Molecular; Nature; Neural Network Simulation; Noise; Output; Performance; Pharmaceutical Preparations; Phenotype; Process; Proteins; Publishing; Pythons; RNA; RNA Splicing; Reporter; Reproducibility; Research; Ribonucleic Acid Regulatory Sequences; Science; Spinal Muscular Atrophy; Techniques; Technology; Thermodynamics; Variant; Work; biophysical model; computational platform; computer framework; computer infrastructure; deep learning; deep neural network; experimental study; flexibility; genome-wide; innovation; insight; interest; large datasets; mRNA Precursor; molecular phenotype; multiple datasets; multiplex assay; mutation screening; small molecule; success; therapy development; transcription factor

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.

项目总结/摘要 基因组学的一个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-表型图谱的能力已经因以下技术的发展而发生了革命性的变化 变异效应多重测定（MAVE），测量数千至数百万人的分子表型， 平行的基因型变体。MAVE是一个涵盖性术语，包括针对以下的大规模平行报告基因测定： DNA或RNA调控序列的研究，以及蛋白质或 结构RNA。跨多个基因组学科的MAVE技术的快速采用创造了一个 迫切需要计算方法，可以稳健和可重复地推断定量基因型- 表型（G-P）图来自MAVE产生的大型数据集。在这里，我们提出了一个统一的概念， 和用于从MAVE数据定量地建模G-P图的计算框架。这项建议是 动机是我们认识到，占噪声和非线性是无处不在的MAVE 实验需要对MAVE测量过程和感兴趣的G-P图进行明确建模。 这种联合推理策略比大多数MAVE分析方法的计算要求更高，但它 使用现代深度学习框架是可行的。我们大量的初步数据显示， 该策略能够恢复高精度的G-P图，即使在存在主要混淆效应的情况下， 因此，有可能使MAVE研究在基因组学的多个领域受益。目标1将制定方法， 对不同MAVE实验设计中出现的测量过程进行建模。Aim 2将开发 G-P图中遗传相互作用建模的一般方法，并将结合使用这些方法 通过新的实验来阐明最近批准的一种药物的分子机制， 选择性mRNA剪接。目标3将开发用于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。这些方法将与新的MAVE实验结合使用， 开发一个生物物理模型，用于研究多效性转录因子如何在整个细胞中调节基因表达。 大肠杆菌基因组。Aim 4将研究和开发处理量规自由度和马虎的方法。 模式，从而促进比较，解释，和探索 G-P映射我们开发的所有计算技术将被纳入一个强大而简单的- 一个名为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一部分生成的数据。总之，这项工作将填补一个主要的需要， MAVE实验的分析，产生一个强大的，灵活的，可扩展的计算平台，将有助于 加速使用MAVE来了解人类基因组规模遗传变异的影响。