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: 10366897
• 负责人: JUSTIN B. KINNEY
• 金额: $ 78.79万
• 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/10366897
• 关键词: 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; Gaussian model; 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.

项目摘要/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-表型图谱的能力已经被 变异效应的多重分析(MAVES)，它测量数千到数百万个 基因类型变异是平行的。MAVE是一个总括的术语，包括大规模平行的报告分析 DNA或RNA调节序列的研究，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一个 迫切需要能够稳健和可重复性地推断定量基因的计算方法-- MAVES产生的大数据集的表型(G-P)图。在这里我们提出了一个统一的概念 以及从MAVE数据定量模拟G-P图的计算框架。这项建议是 我们意识到MAVE中无处不在的噪声和非线性 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可行。我们广泛的初步数据表明，这个模型 战略能够恢复高精度的G-P图，即使在存在重大混杂效应的情况下，以及 因此有可能有利于基因组学多个领域的MAVE研究。目标1将制定方法，以 对不同MAVE实验设计中出现的测量过程进行建模。目标2将会发展 在G-P图中模拟遗传互作的一般方法，并将结合使用这些方法 用新的实验来阐明最近批准的一种靶向药物的分子机制 另一种信使核糖核酸剪接。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。这些方法随后将与新的MAVE实验结合使用，以 为多效性转录因子如何在整个过程中调节基因表达开发一个生物物理模型 大肠埃希菌基因组。目标4将研究和开发处理量规自由和马虎的方法 模式，从而便于比较、解释和探索 推断出G-P图。我们开发的所有计算技术都将整合到一个强大而简单的- 使用名为MAVE-NN的Python包。我们将在不同的MAVE数据集阵列上对MAVE-NN进行基准测试， 包括发布的数据集和作为此项目的一部分生成的数据。总而言之，这项工作将填补 MAVE实验的分析，产生一个健壮、灵活和可扩展的计算平台，这将有助于 加快利用MAVES在基因组水平上了解人类遗传变异的影响。