ABI Innovation: Next Generation Quantitative RNA Sequence Analysis

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。准确地确定RNA的产量和RNA的结构是这项研究的目标。许多实验都对RNA进行高通量测序，以显示有多少基因表达，基因组DNA的哪些部分正在制造RNA，以及DNA区域是如何结合在一起形成功能RNA的。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的噪声。当试图将RNA序列与基因组序列进行比较时，错误也会发生，该基因组序列中有缺口或没有正确组装。噪声和误差的影响是，计算每种类型的RNA存在的量不是非常准确，这可能会给出误导性的结果。这项研究的目的是开发克服技术问题的方法，以便能够对生物过程进行良好的量化和更好的理解。新的算法将被合并到可供科学界感兴趣的成员使用的软件包中，以便改进的好处将被广泛分享。此外，更好地分析RNA测序实验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。高通量的RNA测序已经证明自己是发现基因和注释编码和非编码基因的新亚型的宝贵工具。然而，它仍然没有实现其最终承诺，即提供对转录丰度的定量和比较测量。这一差距是由一系列技术因素造成的。其中包括通过使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、由假基因等平行序列引起的错误比对引起的噪声、由未注释的转录本引入的偏差、正义/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导致的起源偏差。该项目的目标是开发克服或回避所有这些因素的方法，以努力实现定量分析的RNA测序承诺。我们的研究计划包括开发计算模型和高效算法来同时重新平衡基因和伪基因之间以及基因家族内基因之间的读取，健壮的无比对方法来估计转录丰度和等位基因特异的表达模式，以及从头开始的方法来从单个样本中使用DNAseq和RNAseq来发现异构体和新的转录本。拟议的计算工具将纳入广泛科学界采用的共同应用框架下的软件包。该项目的结果可在http://www.cs.ucla.edu/~weiwang/NSF1565137.html上查阅。

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