Collaborative Research: ABI Innovation: Genome-Wide Inference of mRNA Isoforms and Abundance Estimation from Biased RNA-Seq Reads

合作研究：ABI 创新：mRNA 同工型的全基因组推断和有偏差的 RNA-Seq 读数的丰度估计

• 批准号: 1262107
• 负责人: Tao Jiang
• 金额: $ 56.99万
• 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: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1262107&HistoricalAwards=false
• 关键词: Collaborative; Research; ABI; Innovation; Genome

The University of California, Riverside and University of California, Los Angeles are awarded collaborative grants to identify mRNA isoforms on a genome-wide basis. Due to alternative splicing events in eukaryotic cells, the identification of mRNA isoforms (or transcripts) is a difficult problem in molecular biology. Traditional experimental methods for this purpose are time-consuming and cost ineffective. The emerging RNA-Seq technology provides a possible effective way to address this problem. This project aims to develop efficient and accurate methods for inferring isoforms and estimating their abundance levels from RNA-Seq data where the reads may be sampled non-uniformly due to the existence of various biases including positional, sequencing and mappability biases. In particular, a novel statistical framework based on quasi-multinomial distributions will be introduced and a companion expectation-maximization (EM) algorithm developed for estimating isoform abundance levels that can handle all above biases in RNA-Seq data. The algorithms will be implemented efficiently in C++, tested extensively on both simulated and real RNA-Seq data in human, mouse and drosophila, and made available to the public for free. The performance of the algorithms will be evaluated extensively using both simulated and real RNA-Seq data. In the latter case, perturbations to some important splicing factors will be introduced into selected cell lines to induce widespread alteration of splicing events. RNA-Seq data of these cells, combined with quantitative RT-PCR validation, will provide an enriched dataset to assess the performance of the algorithms in predicting both isoform abundance and relative variation. In addition, the validation results may provide insight on the regulatory functions of the splicing factors and serve as a testbed for further improvement of the algorithms.The broader impact of this project is twofold. First, RNA-Seq data analysis is a timely topic in bioinformatics due to the recent rapid advance in next generation sequencing (NGS) technologies and its potential impact in life sciences and medicine. Despite the success of many RNA-Seq applications, several challenges remain in the analysis of RNA-Seq data, one of which comes from the understanding and handling of biases in RNA-Seq reads. The approaches proposed in this project for treating RNA-Seq biases combine unique techniques from statistics, machine learning and combinatorial algorithms. Moreover, the experimental validation results may shed light on the regulatory functions of some important splicing factors. Second, the project will provide an excellent opportunity for the training of two computer science PhD students, a postdoc and two biology undergraduate students in the interdisciplinary field of computational biology and bioinformatics. Since many of the involved students are female, the research will also help improve the representation of women in science and engineering.

加州大学河滨分校和加州大学洛杉矶分校获得了合作资助，以在全基因组基础上鉴定 mRNA 亚型。由于真核细胞中存在选择性剪接事件，mRNA亚型（或转录本）的鉴定是分子生物学中的一个难题。用于此目的的传统实验方法既耗时又成本低。新兴的RNA-Seq技术为解决这一问题提供了一种可能有效的方法。该项目旨在开发高效、准确的方法来推断同种型并根据 RNA-Seq 数据估计其丰度水平，其中由于存在各种偏差（包括位置偏差、测序偏差和可映射性偏差），读数可能会不均匀采样。特别是，将引入一种基于准多项分布的新型统计框架，并开发一种配套的期望最大化（EM）算法，用于估计同种型丰度水平，该算法可以处理 RNA-Seq 数据中的所有上述偏差。这些算法将在 C++ 中高效实现，在人类、小鼠和果蝇的模拟和真实 RNA-Seq 数据上进行广泛测试，并免费向公众提供。将使用模拟和真实 RNA-Seq 数据广泛评估算法的性能。在后一种情况下，对一些重要剪接因子的干扰将被引入选定的细胞系中，以诱导剪接事件的广泛改变。这些细胞的 RNA-Seq 数据与定量 RT-PCR 验证相结合，将提供丰富的数据集来评估算法在预测同种型丰度和相对变异方面的性能。此外，验证结果可以提供对剪接因子调节功能的深入了解，并作为进一步改进算法的测试平台。该项目的更广泛影响是双重的。首先，由于下一代测序（NGS）技术最近的快速发展及其对生命科学和医学的潜在影响，RNA-Seq数据分析成为生物信息学中一个及时的话题。尽管许多 RNA-Seq 应用取得了成功，但在 RNA-Seq 数据分析中仍然存在一些挑战，其中之一来自对 RNA-Seq 读取中偏差的理解和处理。该项目提出的治疗 RNA-Seq 偏差的方法结合了统计学、机器学习和组合算法的独特技术。此外，实验验证结果可能有助于揭示一些重要剪接因子的调节功能。其次，该项目将为计算生物学和生物信息学跨学科领域培养两名计算机科学博士生、一名博士后和两名生物学本科生提供绝佳的机会。由于许多参与的学生是女性，这项研究也将有助于提高女性在科学和工程领域的代表性。

项目成果

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

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

H-PoP and H-PoPG: heuristic partitioning algorithms for single individual haplotyping of polyploids

H-PoP 和 H-PoPG：用于多倍体单个个体单倍型分析的启发式分区算法

• DOI: 10.1093/bioinformatics/btw537
• 发表时间: 2016-12-15
• 期刊: BIOINFORMATICS
• 影响因子: 5.8
• 作者: Xie, Minzhu;Wu, Qiong;Jiang, Tao
• 通讯作者: Jiang, Tao