Interpreting function of non-coding sequences with synthetic biology and machine learning

用合成生物学和机器学习解释非编码序列的功能

基本信息

批准号：
10417177
负责人：
Ryan Zachary Friedman
金额：
$ 3.08万
依托单位：
WASHINGTON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2023-06-04
项目状态：
已结题

项目摘要

PROJECT SUMMARY/ABSTRACT Most disease-associated variants lie in non-coding regions of the genome and exert their influence through effects on gene expression. However, we lack a predictive framework to interpret such non-coding variants, limiting how genomic data is used in precision medicine. We may be able to interpret non-coding variants with new machine learning algorithms, but so far the practical applications of machine learning in functional genomics have been limited because of two major challenges. First, the size and diversity of training data sets in functional genomics are orders of magnitude smaller than in applications where machine learning has been successful, such as image recognition and product recommendation. A second challenge is that if training data are not collected in an appropriate in vitro cellular model, then the resulting machine learning models may not generalize to relevant in vivo cell types. To improve the application of machine learning to non-coding variants, I propose to address both the limited size of training data sets and the efficacy of cell culture models. A core principle of machine learning is that model performance improves with more data. In Aim 1, I propose to increase the size and diversity of training data by performing iterative cycles of machine learning and experimental validation with Massively Parallel Reporter Assays (MPRAs). The key aspect of my approach is to algorithmically design each successive MPRA library to contain sequences that are most likely to improve the next round of modeling. I recently trained my first model on data that I collected from MPRA experiments of cis-regulatory sequences that function in mammalian photoreceptors. To avoid any issues with cell lines, I performed these experiments in ex vivo developing retinas, which retain the appropriate tissue architecture. However, unlike photoreceptors, most cell types are not experimentally tractable in their native physiological context. Thus, it will be important to determine how well in vitro cell lines recapitulate in vivo cis-regulation. In Aim 2, I propose to determine whether a tractable cell culture model can recapitulate results from ex vivo retinas. I will use existing MPRA data from ex vivo retinas as a standard to compare against data collected in cell lines engineered to express combinations of photoreceptor transcription factors. I aim to address whether engineering tractable cell lines to express tissue-specific transcription factors might be a general approach for collecting data to train machine learning models that generalize to in vivo systems. Successful completion of these aims will produce a general approach to increase the size and diversity of functional genomic training data, and may result in a general method for producing experimentally tractable systems for machine learning applications, ultimately helping us better apply genomic data to precision medicine.

项目摘要/摘要大多数与疾病相关的变体位于基因组的非编码区域，并通过对基因表达的影响。但是，我们缺乏解释此类非编码变体的预测框架，限制了基因组数据在精确医学中的使用。我们可能能够用新机器学习算法，但到目前为止，机器学习在功能中的实际应用由于两个主要挑战，基因组学受到了限制。首先，培训数据集的规模和多样性在功能基因组中，基因组学的数量级比机器学习的应用小。成功，例如图像识别和产品建议。第二个挑战是，如果培训数据未在适当的体外蜂窝模型中收集，那么所得的机器学习模型可能不会推广到相关的体内细胞类型。为了改善机器学习到非编码变体的应用，我建议解决训练数据集的有限尺寸和细胞培养模型的功效。机器学习的核心原则是，模型性能通过更多数据改进。在AIM 1中，我建议通过执行机器学习的迭代周期和实验验证，具有大量并联报告基因测定法（MPRA）。我方法的关键方面是算法将每个连续的MPRA库设计以包含最有可能改善该序列的序列下一轮建模。我最近训练了我的第一个模型，该模型是从MPRA实验中收集的数据在哺乳动物感光器中起作用的顺式调节序列。为了避免细胞系问题，我在开发视网膜的外体内进行了这些实验，该视网膜保留了适当的组织结构。但是，与感光体不同，大多数细胞类型在其天然生理学中无法实验性处理语境。因此，确定体外细胞系在体内调节中概括的程度非常重要。在 AIM 2，我建议确定是否可以易于探讨细胞培养模型可以概括离体的结果视网膜。我将使用来自Vivo视网膜的现有MPRA数据作为与收集的数据进行比较的标准细胞系设计，以表达光感受器转录因子的组合。我的目的是解决是否工程可拖动的细胞系以表达组织特异性转录因子可能是一种通用方法收集数据以训练将体内系统推广的机器学习模型。成功完成这些目标将产生一种一般方法来增加功能基因组训练的大小和多样性数据，并可能导致一种通用方法，用于生成用于机器学习的实验可行系统应用，最终帮助我们更好地将基因组数据应用于精确医学。