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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

基本信息

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
项目类别：
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

项目摘要

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