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合作资助，在全基因组的基础上鉴定mRNA同种异构体。由于真核细胞中剪接事件的选择性，mRNA亚型（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的有效方法。该项目旨在开发有效和准确的方法来推断同工异构体并从RNA-Seq数据中估计其丰度水平，其中由于存在各种偏差，包括位置，测序和可映射性偏差，读取可能不均匀取样。特别是，将引入一种基于拟多项分布的新型统计框架，并开发一种伴随的期望最大化（EM）算法，用于估计异构体丰度水平，可以处理RNA-Seq数据中的所有上述偏差。该算法将在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，并免费向公众开放。算法的性能将使用模拟和真实RNA-Seq数据进行广泛评估。在后一种情况下，对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改变。这些细胞的RNA-Seq数据，结合定量RT-PCR验证，将提供一个丰富的数据集，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，验证结果可以提供剪接因子的调控功能，并为进一步改进算法提供测试平台。这个项目的广泛影响是双重的。首先，由于下一代测序（NGS）技术的快速发展及其对生命科学和医学的潜在影响，RNA-Seq数据分析在生物信息学中是一个及时的话题。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，其中一个挑战来自对RNA-Seq读取中的偏差的理解和处理。本项目中提出的处理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剪接因子的调控功能。二是在计算生物学和生物信息学交叉领域培养2名计算机科学博士研究生、1名博士后和2名生物学本科生。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。

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.