Predictive Modeling of Alternative Splicing and Polyadenylation from Millions of Random Sequences

数百万随机序列的选择性剪接和聚腺苷酸化的预测模型

• 批准号: 9306648
• 负责人: Georg Seelig
• 金额: $ 59.66万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-21 至 2021-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9306648
• 关键词: Adopted; Algorithms; Alternative Splicing; Area; Basic Science; Behavior; Big Data; Biological Assay; Biological Phenomena; CRISPR/Cas technology; Clinical Medicine; Code; Complex; Computers; DNA Sequence; Data; Data Set; Databases; Dependency; Disease; Gene Expression; Gene Expression Regulation; Generations; Genes; Genetic; Genetic Polymorphism; Genetic Variation; Genome; Genomics; Haplotypes; Human; Human Genome; Lead; Learning; Libraries; Machine Learning; Measurement; Measures; Mediating; Mendelian disorder; Modeling; Mutation; Natural Language Processing; Nucleotides; Polyadenylation; Protein Isoforms; Proteins; Publishing; RNA Splicing; RNA-Binding Proteins; Regulation; Regulator Genes; Reporter; Research; Risk; Scientist; Shapes; Specific qualifier value; Testing; Training; Transcript; Untranslated RNA; Validation; Variant; Work; base; clinically relevant; data modeling; disease-causing mutation; exon skipping; experimental study; genetic variant; human disease; knock-down; novel strategies; predictive modeling; repaired; synthetic biology; synthetic construct

The proportion of the human genome that underlies gene regulation dwarfs the proportion that encodes proteins. However, we remain poorly equipped for identifying which genetic variants compromise gene regulatory function in ways that may contribute to risk for both rare and common human diseases. Understanding how non-coding sequences regulate gene expression, as well as being able to predict the functional consequences of genetic variation for gene regulation, are paramount challenges for the field. Here, we propose to combine synthetic biology, massively parallel functional assays, and machine learning to profoundly advance our understanding of the `regulatory code' of the human genome. While challenging, the task of unravelling complex codes from large amounts of empirical data is not without precedent. For example, over the past decade, computer scientists working in natural language processing have made immense progress, driven in large part by a combination of algorithmic and computational improvements and enormously larger training datasets than were available to the previous generations of scientists working in this area. Inspired by the revolutionizing impact of “big data” for traditional problems in machine learning, we propose to model gene regulatory phenomena using training datasets with several orders of magnitude more examples than naturally exist in the human genome. We predict that the models learned from massive numbers of synthetic examples will strongly outperform models learned from the small number of natural examples. We will demonstrate our approach by developing comprehensive, quantitative, and predictive models for alternative splicing and alternative polyadenylation, two widespread regulatory mechanisms by which a single gene can code for multiple transcripts and proteins. However, we anticipate that this basic paradigm – specifically, the massively parallel measurement of the functional behavior of extremely large numbers of synthetic sequences followed by quantitative modeling of sequence-function relationships – can be generalized to advance our understanding of diverse forms of gene regulation.

基因调节基础的人类基因组的比例使编码的比例相形见,， 蛋白质。但是，我们在确定哪些遗传变异损害基因的基因方面仍然很差 监管功能的方式可能导致稀有人类疾病和常见的人类疾病的风险。 了解非编码序列如何调节基因表达，并能够预测 基因调节遗传变异的功能后果是该领域的最高挑战。这里， 我们建议将合成生物学，大量平行功能测定和机器学习结合起来 深刻地促进了我们对人类基因组的“监管守则”的理解。虽然具有挑战性，但 从大量经验数据中阐明复杂代码的任务并非没有先例。例如， 在过去的十年中，从事自然语言处理的计算机科学家巨大 进步，很大程度上是由算法和计算改进以及 与前几代科学家相比，在此工作的培训数据集大大要大得多 区域。受“大数据”对机器学习中传统问题的革命性影响的启发，我们 使用训练数据集对基因调节现象进行建模的提案，并具有多个数量级 人类基因组中天然存在的例子。我们预测模型从大规模学习 合成示例的数量将极大地超过从少数自然中学到的模型 例子。我们将通过发展全面，定量和预测来证明我们的方法 替代剪接和替代聚腺苷酸化的模型，两种宽度的调节机制 单个基因可以为多个转录本和蛋白质编码。但是，我们预计这个基本 范式 - 具体来说，极大的功能行为的大规模平行测量 合成序列的数量，然后进行序列功能关系的定量建模 - 可以是 概括以促进我们对潜水员基因调节形式的理解。