ABI Innovation: Next Generation Quantitative RNA Sequence Analysis

ABI 创新：下一代定量 RNA 序列分析

• 批准号: 1565137
• 负责人: Wei Wang
• 金额: $ 60万
• 依托单位: University of California-Los Angeles
• 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: 2016-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1565137&HistoricalAwards=false
• 关键词: ABI; Innovation; Next; Generation; Quantitative

The proper function and health of an organism rests on the correct expression of it's genes: in the first step in expression, RNA molecules are produced from the genes in a number of possible forms. Accurately determining how much RNA is produced and the structure of that RNA are the goals of this research. Many experiments do high throughput sequencing of RNA to show how much gene expression is taking place, what parts of the genomic DNA are making the RNA, and how DNA regions combine to make functional RNA. There are many steps required to process RNA and get sequence data, leading to a lot of noise in the data. Errors also occur when trying to compare the RNA sequence to a genome sequence that has gaps in it or that was not correctly assembled. The effect of the noise and errors is that calculating how much of each type of RNA is present is not very accurate, which can give misleading results. The aim of this research is to develop methods that overcome the technical problems so that good quantitation and better understanding of biological processes are possible. The new algorithms will be incorporated into software packages available for use by interested members of the scientific community, so that the benefits of the improvements will be widely shared. In addition, better analysis of RNA sequencing experiments is expected to have a positive impact on many scientific disciplines, from basic cell biology to development of clinical tests. High-throughput sequencing of RNA has proven itself as an invaluable tool for gene discovery and the annotation of new isoforms for both coding and non-coding genes. However, it is still falls short on its ultimate promise of providing quantitative and comparative measures of transcript abundance. This gap is due to a series of technical factors. Among them are biases introduced by employing an inexact reference genome as the standard for associating sequence data to transcripts, noise due to misalignments causes by paralogous sequence such as pseudogenes, biases introduced by unannotated transcripts, sense/antisense transcript interference, and origin bias due to aligning diploid data to a haploid model. The objective of the project is to develop methods that either overcome or side-step all of these factors in an effort to deliver on the promise of RNA sequencing for quantitative analysis. Our research plan includes developing computational models and efficient algorithms for simultaneous rebalancing reads between genes and pseudogenes and genes within gene families, robust alignment-free methods for estimating transcript abundances and allele-specific expression patterns, and de novo approach for isoform and novel transcript discovery using DNAseq and RNAseq from a single sample. The proposed computational tools will be integrated into software packages under common application framework adopted by the broad scientific community. The results of the project can be found at http://www.cs.ucla.edu/~weiwang/NSF1565137.html

生物体的正常功能和健康取决于其基因的正确表达：在表达的第一步，RNA分子以多种可能的形式从基因中产生。准确确定产生了多少RNA以及RNA的结构是这项研究的目标。许多实验对RNA进行高通量测序，以显示发生了多少基因表达，基因组DNA的哪些部分正在制造RNA，以及DNA区域如何联合收割机来制造功能性RNA。处理RNA和获得序列数据需要很多步骤，导致数据中存在大量噪声。当试图将RNA序列与其中有缺口或未正确组装的基因组序列进行比较时，也会发生错误。噪声和误差的影响是，计算每种类型的RNA存在的数量并不是很准确，这可能会产生误导性的结果。本研究的目的是开发克服技术问题的方法，以便能够更好地定量和更好地理解生物过程。新的算法将被纳入软件包，供科学界感兴趣的成员使用，以便广泛分享改进的好处。此外，RNA测序实验的更好分析预计将对许多科学学科产生积极影响，从基础细胞生物学到临床测试的发展。 RNA的高通量测序已被证明是基因发现和编码和非编码基因的新亚型注释的宝贵工具。然而，它仍然是福尔斯短的最终承诺，提供定量和比较措施的转录丰度。这一差距是由一系列技术因素造成的。其中包括通过采用不精确的参考基因组作为将序列数据与转录本相关联的标准而引入的偏差、由于旁系同源序列（如假基因）引起的错配而引起的噪声、由未注释的转录本引入的偏差、有义/反义转录本干扰以及由于将二倍体数据与单倍体模型比对而引起的起源偏差。该项目的目标是开发克服或回避所有这些因素的方法，以实现RNA测序定量分析的承诺。我们的研究计划包括开发计算模型和有效的算法，用于同时重新平衡基因和假基因以及基因家族内的基因之间的读取，用于估计转录本丰度和等位基因特异性表达模式的稳健的无干扰方法，以及使用DNAseq和RNAseq从单个样品中发现异构体和新转录本的从头方法。拟议的计算工具将被整合到软件包中，并在广泛的科学界采用的通用应用框架下使用。该项目的结果可以在http://www.cs.ucla.edu/~weiwang/NSF1565137.html上找到