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

加州大学河滨分校和加州大学洛杉矶分校获得了在全基因组基础上识别信使核糖核酸亚型的合作资助。由于真核细胞中存在不同的剪接事件，对mRNA异构体(或转录本)的鉴定是分子生物学中的一个难题。传统的实验方法耗时长，成本低。新兴的RNA-Seq技术为解决这一问题提供了一种可能的有效途径。该项目旨在开发有效和准确的方法来从RNA-Seq数据中推断异构体并估计其丰度水平，其中由于位置、测序和可映射性偏差等各种偏差的存在，可能会对读数进行非均匀采样。特别是，将引入一个基于准多项分布的新的统计框架，并开发一个配套的期望最大化(EM)算法来估计异构体丰度水平，该算法可以处理RNA-Seq数据中的所有上述偏差。这些算法将用C++高效实现，并在模拟和真实的人类、小鼠和果蝇的RNA-Seq数据上进行广泛测试，并向公众免费提供。算法的性能将使用模拟和真实的RNA-Seq数据进行广泛的评估。在后一种情况下，一些重要剪接因子的扰动将被引入到选定的细胞系中，以诱导剪接事件的广泛变化。这些细胞的RNA-Seq数据，结合定量RT-PCR验证，将提供丰富的数据集，以评估算法在预测异构体丰度和相对变异方面的性能。此外，验证结果可能为剪接因子的调控功能提供洞察，并作为进一步改进算法的试验台。首先，RNA-Seq数据分析是生物信息学中一个及时的话题，因为最近下一代测序(NGS)技术的快速进步及其对生命科学和医学的潜在影响。尽管许多RNA-Seq应用取得了成功，但在RNA-Seq数据的分析中仍然存在一些挑战，其中之一来自对RNA-Seq读取中的偏差的理解和处理。这个项目中提出的处理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的剪接因子的调控功能。其次，该项目将为在计算生物学和生物信息学的跨学科领域培训两名计算机科学博士后、一名博士后和两名生物学本科生提供一个极好的机会。由于许多参与研究的学生都是女性，这项研究还将有助于提高女性在科学和工程领域的代表性。

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.