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.

项目摘要 /摘要 基因组学的一个核心目标是了解基因型和表型之间的关系。最近几年， 定量研究基因型 - 表型图的能力已通过 多重效应的多重测定（MAVE），该测定法测量了数千至数百万的分子表型 基因型变体并联。 Mave是一个伞，包括大规模平行的记者测定法 DNA或RNA调节序列的研究，以及对蛋白质或 结构RNA。在多个基因组学科中快速采用MAVE技术已创造了一个 急性需求可以鲁棒和可重复地推断定量基因型 - 从小牛产生的大数据集中的表型（G-P）地图。在这里，我们提出了一个统一的概念 以及从MAVE数据定量建模G-P地图的计算框架。该提议是 我们的意识到，在Mave中考虑了无所不在的噪音和非线性 实验需要明确的MAVE测量过程和感兴趣的G-P图。 与大多数MAVE分析方法相比 使用现代深度学习框架可行。我们广泛的初步数据表明，这种建模 即使在存在重大混淆效果的情况下，策略也能够恢复高精度的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。 AIM 1将开发用于 建模在不同的MAVE实验设计中产生的测量过程。 AIM 2将发展 用于建模G-P地图中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靶向的最近批准的药物的分子机制 替代mRNA剪接。 AIM 3将开发用于推断反映生物物理模型的G-P地图的方法 基因调节的构造，包括热力学（即准平衡）和动力学（即非平衡性） 稳态）模型。然后，这些方法将与新的Mave实验一起使用 开发一种生物物理模型，以用于如何调节整个过程中的多效转录因子调节基因表达 大肠杆菌基因组。 AIM 4将研究和开发治疗量规自由和草率的方法 上述模型中的模式，从而促进了对比较，解释和探索 推断G-P地图。我们开发的所有计算技术都将纳入强大而易于的 使用的python软件包称为mave-nn。我们将在各种各样的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，这项工作将满足主要需求 MAVE实验的分析，得出一个可靠，灵活且可扩展的计算平台，这将有助于 加速使用Maves来理解基因组量表上人类遗传变异的影响。