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

III：小：通过深度学习整合和解释异质基因组数据

• 批准号: 1715017
• 负责人: Xiaohui Xie
• 金额: $ 47.08万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1715017&HistoricalAwards=false
• 关键词: III; Small; Integrating; Interpreting; Heterogeneous

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.

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为人们所熟知，但对占哺乳动物基因组 98% 以上的非编码区的研究却少之又少，但它们却是理解基因调控、进化、复杂表型的遗传基础等的关键。该项目的目的是开发计算方法，通过利用来自公开基因组数据的大量数据和来自机器学习的最先进算法来推断非编码序列的功能。 这些算法可以极大地扩展现有基因组数据的效用，提高非编码变异致病性注释的准确性，并为研究非编码序列编码的基因调控语法提供新的途径。该项目还将创造机会促进生物学家和计算机科学家之间的互动，并为本科生和研究生提供跨学科培训，特别是那些来自传统上代表性不足群体的学生。该项目的目标是开发一种基于深度学习的新计算框架理解非编码序列。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，通过基于卷积和循环神经网络以及深度生成模型的创新机器学习算法，得出非编码序列的全面表征。 PI 将开发深度学习算法来绘制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素，并预测非编码遗传变异的影响。使用大量数据训练灵活且可扩展的学习模型提供了一种以无偏且稳健的方式表征非编码序列的方法，并且比传统方法提供了更好的机会提取非编码序列中编码的复杂监管规则。该项目将为基因组学界提供一个多功能、模块化、开源软件包工具箱，目标是大大提高当前基因组分析的准确性。

项目成果

Learning Sample-Specific Policies for Sequential Image Augmentation

• DOI: 10.1145/3474085.3475602
• 发表时间: 2021-10
• 期刊: Proceedings of the 29th ACM International Conference on Multimedia
• 作者: Pu Li;Xiaobai Liu;Xiaohui Xie

SynergyNet: A Fusion Framework for Multiple Sclerosis Brain MRI Segmentation with Local Refinement

• DOI: 10.1109/isbi45749.2020.9098610
• 发表时间: 2020-04
• 期刊: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)
• 作者: Y. S. Vang;Yingxin Cao;P. Chang;D. Chow;A. Brandt;F. Paul;M. Scheel;Xiaohui Xie

Undistillable: Making A Nasty Teacher That CANNOT teach students

• 发表时间: 2021-05
• 期刊: ArXiv
• 作者: Haoyu Ma;Tianlong Chen;Ting-Kuei Hu;Chenyu You;Xiaohui Xie;Zhangyang Wang

FactorNet: A deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data

• DOI: 10.1016/j.ymeth.2019.03.020
• 发表时间: 2019-08-15
• 期刊: METHODS
• 影响因子: 4.8
• 作者: Quang, Daniel;Xie, Xiaohui
• 通讯作者: Xie, Xiaohui

SAILER: scalable and accurate invariant representation learning for single-cell ATAC-seq processing and integration.

• DOI: 10.1093/bioinformatics/btab303
• 发表时间: 2021-07-12
• 期刊: Bioinformatics (Oxford, England)
• 作者: Cao Y;Fu L;Wu J;Peng Q;Nie Q;Zhang J;Xie X
• 通讯作者: Xie X