Development and benchmarking of improved computational methods for transcript-level expression analysis using RNA-seq data

使用 RNA-seq 数据进行转录水平表达分析的改进计算方法的开发和基准测试

• 批准号: BB/J009415/1
• 负责人: Magnus Rattray
• 金额: $ 39.81万
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2012
• 资助国家: 英国
• 起止时间: 2012 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FJ009415%2F1
• 关键词: Development; benchmarking; improved; computational; methods

After sequencing of the human genome was completed, Scientists were surprised to discover that there are far fewer protein-coding genes than was previously predicted. One reason that an organism as complex as human can be built from a relatively small number of genes is that each gene encodes more than one protein. An intermediate molecule, messenger RNA (mRNA), carries the information from the genome in the cell nucleus to ribosomes which create proteins. These mRNA molecules are also known as transcripts and their full complement is termed the transcriptome. Before they mature these transcripts are edited to form the template for different proteins. This editing process is called splicing and different transcripts that result are called splice variants or isoforms. An additional complexity in the transcriptome is due to the fact that each gene has multiple copies (for example 2 in human, 6 in wheat) and these different copies, called alleles, can be expressed differently under different conditions or in different tissues. The transcriptome is a collection of transcripts which includes all the allele-specific gene isoforms that are expressed in the cell along with other non-coding RNA molecules. Splicing and allele usage are fundamental ways that the function of genes can be modulated in a tissue-specific manner. Therefore developing technologies to accurately measure transcript expression is a necessary step towards understanding and modelling cells and tissues. A recently developed experimental technology called RNA-seq gives unprecedented access to data about the transcriptome. Computational methods are required to interpret these data which are in the form of a list containing millions of short RNA sequence fragments. These fragments are difficult to interpret because, for example, the same fragment could have come from a large number of different gene isoforms. The question is, which one? Computational methods can be used to answer this question and infer the concentration of different gene isoforms in the sample given these data. In this project we will develop a new computational method, implemented in publically available free software, which uses advanced statistical procedures to solve this problem. An important distinguishing feature of the method is the ability to associate inferred concentrations with a degree of uncertainty which captures technical and biological sources of error as well as the inherent difficulty of the problem due to the difficulty of assigning fragments to gene isoforms. We will create benchmark data that allows us to assess the performance or our method and other available published methods, allowing researchers and end-users of different methods to understand their properties. Finally, we will adapt an existing computer program, puma, to work with the processed RNA-seq data in order to identify genes which change between conditions, which have similar expression patterns or which contribute most to the variance in the data.

在人类基因组测序完成后，科学家们惊讶地发现，蛋白质编码基因比之前预测的要少得多。像人类这样复杂的生物体可以由相对较少的基因构建而成，其中一个原因是每个基因编码不止一种蛋白质。信使RNA （mRNA）是一种中间分子，它将细胞核基因组的信息传递给产生蛋白质的核糖体。这些mRNA分子也被称为转录本，它们的完整补体被称为转录组。在它们成熟之前，这些转录本被编辑成不同蛋白质的模板。这种编辑过程称为剪接，产生的不同转录本称为剪接变体或同种异构体。转录组的另一个复杂性是由于每个基因都有多个拷贝（例如人类有2个，小麦有6个），这些不同的拷贝被称为等位基因，在不同的条件下或在不同的组织中可以以不同的方式表达。转录组是转录本的集合，包括在细胞中与其他非编码RNA分子一起表达的所有等位基因特异性基因同种异构体。剪接和等位基因的使用是基因功能以组织特异性方式调节的基本途径。因此，开发准确测量转录物表达的技术是理解和模拟细胞和组织的必要步骤。最近开发的一项名为RNA-seq的实验技术为获取转录组数据提供了前所未有的途径。需要计算方法来解释这些数据，这些数据以包含数百万个短RNA序列片段的列表形式存在。这些片段很难解释，因为，例如，相同的片段可能来自大量不同的基因同种异构体。问题是，是哪一个？计算方法可以用来回答这个问题，并根据这些数据推断样品中不同基因同种异构体的浓度。在这个项目中，我们将开发一种新的计算方法，在公开的自由软件中实现，它使用先进的统计程序来解决这个问题。该方法的一个重要区别特征是能够将推断的浓度与一定程度的不确定性联系起来，这种不确定性捕获了技术和生物学上的错误来源，以及由于难以将片段分配给基因同种异构体而导致的问题的固有困难。我们将创建基准数据，使我们能够评估我们的方法和其他可用的已发表方法的性能，从而使不同方法的研究人员和最终用户能够了解它们的特性。最后，我们将调整现有的计算机程序puma，与处理过的RNA-seq数据一起工作，以识别在不同条件下变化的基因，具有相似表达模式的基因或对数据差异贡献最大的基因。

项目成果

Improved variational Bayes inference for transcript expression estimation.

改进了用于转录表达估计的变分贝叶斯推理。

• DOI: 10.1515/sagmb-2013-0054
• 发表时间: 2014
• 期刊: Statistical applications in genetics and molecular biology
• 影响因子: 0.9
• 作者: Papastamoulis P

Fast and accurate approximate inference of transcript expression from RNA-seq data

从 RNA-seq 数据快速准确地近似推断转录本表达

• DOI: 10.48550/arxiv.1412.5995
• 发表时间: 2014
• 作者: Hensman J

Fast and accurate approximate inference of transcript expression from RNA-seq data.

• DOI: 10.1093/bioinformatics/btv483
• 发表时间: 2015-12-15
• 期刊: Bioinformatics (Oxford, England)
• 作者: Hensman J;Papastamoulis P;Glaus P;Honkela A;Rattray M
• 通讯作者: Rattray M

A Bayesian model selection approach for identifying differentially expressed transcripts from RNA sequencing data.

• DOI: 10.1111/rssc.12213
• 发表时间: 2018-01
• 期刊: Journal of the Royal Statistical Society. Series C, Applied statistics
• 作者: Papastamoulis P;Rattray M
• 通讯作者: Rattray M

Bayesian estimation of differential transcript usage from RNA-seq data.

根据 RNA-seq 数据对转录本使用差异进行贝叶斯估计。

• DOI: 10.1515/sagmb-2017-0005
• 发表时间: 2017
• 期刊: Statistical applications in genetics and molecular biology
• 影响因子: 0.9
• 作者: Papastamoulis P