EAGER: Transcript-Based Differential Expression Analysis for Population Data Without Predefined Conditions

EAGER：在没有预定义条件的情况下对群体数据进行基于转录的差异表达分析

• 批准号: 1646333
• 负责人: Tao Jiang
• 金额: $ 20万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1646333&HistoricalAwards=false
• 关键词: EAGER; Transcript; Based; Differential; Expression

With the emergence of precision medicine, there is increased demand for more sensitive molecular biomarkers. A fundamental computational step in the discovery of molecular biomarkers is to identify genes that are expressed differently across different samples. This project investigates new algorithmic approaches for performing differential expression analysis at the transcript level for samples without predefined biological conditions. Such an analysis is critical to both clinical and biological studies on population (or cohort) data. For example, it can be used to discover molecular biomarkers to classify cancer samples into subtypes so that better diagnosis and therapy methods can be developed for each subtype. It can also be used to characterize individual cells involved in different biological processes. Efficient software tools can be built based on the new analysis approaches proposed in this project which can help biologists to discover more sensitive biomarkers than the existing methods. Specifically, this project studies three approaches for differential transcript expression analysis on population data. The first two approaches treat either the exons or full transcripts of a gene as the basic expression elements and then apply a gene-level differential expression analysis method on these expression elements. The third approach is a hybrid of the first two. It uses a splice graph to represent the transcripts of a gene and a new modular decomposition algorithm to partition the graph into small components that correspond to independent alternative splicing events. Moreover, a robust clustering algorithm is employed to deal with an arbitrary number of conditions in the input population. The software implementations of the three approaches are calibrated and tested extensively on both simulated and real sequence data to establish their practical utility to the public.

随着精准医疗的出现，对更敏感的分子生物标志物的需求也在增加。发现分子生物标志物的一个基本计算步骤是识别在不同样本中表达不同的基因。该项目研究了在没有预先定义的生物条件的情况下，在转录本水平上进行差异表达分析的新算法方法。这样的分析对于人群（或队列）数据的临床和生物学研究至关重要。例如，它可以用来发现分子生物标志物，将癌症样本分类为亚型，以便为每个亚型开发更好的诊断和治疗方法。它也可以用来表征参与不同生物过程的单个细胞。基于本项目提出的新分析方法，可以构建有效的软件工具，帮助生物学家发现比现有方法更敏感的生物标志物。具体而言，本项目研究了人口数据差异转录表达分析的三种方法。前两种方法将基因的外显子或全转录本作为基本表达元件，然后对这些表达元件应用基因水平差异表达分析方法。第三种方法是前两种方法的混合。它使用剪接图来表示基因的转录本，并使用一种新的模块化分解算法将剪接图划分为小的组件，这些组件对应于独立的可选剪接事件。此外，采用鲁棒聚类算法处理输入总体中任意数量的条件。这三种方法的软件实现经过了模拟和真实序列数据的广泛校准和测试，以确定它们对公众的实际效用。

项目成果

TAPAS: tool for alternative polyadenylation site analysis

TAPAS：替代聚腺苷酸化位点分析工具

• DOI: 10.1093/bioinformatics/bty110
• 发表时间: 2018-08-01
• 期刊: BIOINFORMATICS
• 影响因子: 5.8
• 作者: Arefeen, Ashraful;Liu, Juntao;Jiang, Tao
• 通讯作者: Jiang, Tao

SDEAP: a splice graph based differential transcript expression analysis tool for population data

• DOI: 10.1093/bioinformatics/btw513
• 发表时间: 2016-12
• 期刊: Bioinformatics
• 影响因子: 5.8
• 作者: Ei-Wen Yang;Tao Jiang

OMGS: Optical Map-based Genome Scaffolding

OMGS：基于光学图谱的基因组支架

• DOI: 10.1007/978-3-030-17083-7_12
• 发表时间: 2019
• 期刊: RECOMB 2019 - ACM Annual Conference on Research in Computational Molecular Biology
• 作者: W. Pan, T. Jiang

DeepHINT: understanding HIV-1 integration via deep learning with attention

DeepHINT：通过深度学习和注意力理解 HIV-1 整合

• DOI: 10.1093/bioinformatics/bty842
• 发表时间: 2019-05-15
• 期刊: BIOINFORMATICS
• 影响因子: 5.8
• 作者: Hu, Hailin;Xiao, An;Zeng, Jianyang
• 通讯作者: Zeng, Jianyang

Analysis of Ribosome Stalling and Translation Elongation Dynamics by Deep Learning

通过深度学习分析核糖体停滞和翻译延伸动力学

• DOI: 10.1016/j.cels.2017.08.004
• 发表时间: 2017
• 期刊: Cell Systems
• 影响因子: 9.3
• 作者: Zhang Sai;He Xuan;Zeng Jianyang;Hu Hailin;Zhou Jingtian;Jiang Tao;Jiang Tao;Jiang Tao;Jiang Tao;Zeng JY
• 通讯作者: Zeng JY