III: Small: Integrating and Interpreting Heterogeneous Genomic Data Through Deep Learning

全面鉴定基因组中编码的所有功能元件是基础和应用生物学研究的基本需求。尽管基因组的编码区已经被很好地理解，但占哺乳动物基因组98%以上的非编码区的研究却少得多，但它是理解基因调控、进化、复杂表型的遗传基础、该项目的目标是开发计算方法，通过利用来自公开可用的基因组数据和状态的大量数据来推断非编码序列的功能。最先进的机器学习算法。 这些算法可以极大地扩展现有基因组数据的应用，提高非编码序列致病性注释的准确性，为研究非编码序列编码的基因调控文法提供了一种新的途径。该项目还将为生物学家和计算机科学家之间的互动创造机会，并为本科生和研究生提供跨学科培训，特别是那些传统上代表性不足的群体。该项目的目标是开发一种基于深度学习的新计算框架，以理解非编码序列。在过去的几年里，研究人员已经生成了数千个关于染色质可及性、组蛋白修饰、DNA甲基化、蛋白质结合等的基因组规模数据集，涵盖了广泛的组织和细胞类型。该项目将整合这些异构数据集，通过基于卷积和递归神经网络的创新机器学习算法以及深度生成模型来获得非编码序列的全面表征。PI将开发深度学习算法，以映射非编码序列与不同基因组测量之间的关系，学习染色质状态并从这些测量中发现新的功能元件，并预测非编码遗传变异的影响。训练一个灵活的和可扩展的学习模型与大量的数据提供了一种方法来表征非编码序列在一个公正的和强大的方式，并提供了一个更好的机会，提取复杂的监管规则编码的非编码序列比传统的方法。该项目将为基因组学界提供一个多功能、模块化、开放源码的软件包工具箱，目的是大大提高目前基因组分析的准确性。

Comprehensive identification of all functional elements encoded in genomes is a fundamental need in both basic and applied biological research. Although the coding regions of genomes are well understood, the noncoding regions, representing over 98% of mammalian genomes, are far less studied, but hold the key to understanding gene regulation, evolution, genetic basis of complex phenotypes, etc. The goal of this project is to develop computational methods to infer the function of noncoding sequences by leveraging the plethora of data from publicly available genomic data and state-of-the-art algorithms from machine learning. These algorithms can greatly expand the utility of existing genomic data, improving the accuracy of annotating pathogenicity of noncoding variants, and offering a new way of studying grammars of gene regulation encoded by noncoding sequence. The project will additionally create opportunities to facilitate interactions between biologists and computer scientists, and offer interdisciplinary training for both undergraduate and graduate students, especially those from traditionally underrepresented groups.The goal of this project is to develop a new computational framework based on deep learning to understand noncoding sequences. Over the past few years, researchers have generated thousands of genome-scale datasets on chromatin accessibility, histone modifications, DNA methylation, protein-binding, and others, spanning a broad range of tissue and cell types. This project will integrate these heterogeneous datasets to derive a comprehensive characterization of noncoding sequence through innovative machine learning algorithms based on convolutional and recurrent neural nets, and deep generative models. The PI will develop deep learning algorithms to map the relationship between noncoding sequences and the diverse genomic measurements, learn chromatin states and discover novel functional elements from these measurements, and predict effects of noncoding genetic variants. Training a flexible and scalable learning model with large amounts of data provides a way of characterizing noncoding sequences in an unbiased and robust fashion, and offers a better chance of extracting complex regulatory rules encoded within noncoding sequences than conventional methods. This project will provide the genomics community with a versatile, modular, open-source toolbox of software packages, with the goal of greatly improving the accuracy of current genome analyses.