Computational genome-wide RNA profiling using next-generation sequencing

描述(申请人提供)：最近的证据表明，非编码RNA在细胞中普遍存在，它们的功能和结构的变化比之前想象的要大得多。多个新的RNA类别与许多疾病有关，了解这些RNA是如何工作的是至关重要的。虽然令人兴奋的发现正在积累，但我们对这些新RNA的功能了解仍然有限。在这里，我们建议将一种新的高通量RNA双链测序技术与新的计算方法相结合，在基因组水平上经济地研究新的功能非编码RNA。我们建议开发两种计算方法来在基因组水平上表征新发现的假定的非编码RNA。首先，我们将开发一种最大似然方法，使用优先对单链或双链核苷酸排序的RNA-SEQ分析来估计RNA二级结构。其次，我们将开发一个机器学习框架，利用已知RNA的长度和结构特征来预测新型非编码RNA的功能类别。这些结构和功能预测将通过比较基因组学和实验得到验证。我们将开发数据库和分析软件，并调查人类基因组和其他五种模式生物。总而言之，我们的发现将对非编码RNA生物学产生巨大的洞察力，并将对这些重要分子的继续研究产生重大影响。 公共卫生相关性：我们建议开发计算方法，通过利用一种新的双链RNA测序技术来研究新的非编码RNA转录本。我们的第一个目标是开发一种最大似然算法，使用双链或单链RNA测序来估计二级结构。我们还将开发一个机器学习框架，使用来自RNA-SEQ实验的长度和结构特征来预测新型非编码RNA的功能类别。这些方法将被用来使用来自人类和五个模式生物的实验数据来注释所有的RNA转录本。

DESCRIPTION (provided by applicant): Recent evidence has shown that non-coding RNAs are ubiquitous in the cell and that their functions and structure vary to a greater extent than previously imagined. Multiple new RNA classes have been implicated in many diseases, and understanding how these RNAs work is a critical need. While exciting discoveries are accumulating, our functional knowledge of these new RNAs remains limited. Here we propose to couple a new high-throughput RNA duplex sequencing technology with new, computational methods to economically study novel functional non-coding RNA at a genomic scale. We propose to develop two computational methodologies to characterize putative newly found non-coding RNAs on the genomic scale. First, we will develop a maximum likelihood approach that estimates RNA secondary structure using RNA-seq assays that preferentially sequence single- or double-stranded nucleotides. Second, we will develop a machine-learning framework that predicts the functional category of novel non-coding RNAs using length and structure features of known RNAs. These structural and functional predictions will be validated by comparative genomics and experimentation. We will develop databases and analysis software, and investigate the human genome and five other model organisms. In total, our findings will yield tremendous insights into non-coding RNA biology and will substantially impact continued study of these important molecules. PUBLIC HEALTH RELEVANCE: We propose to develop computational methods to study novel non-coding RNA transcripts by leveraging a new duplex RNA sequencing technique. Our first objective is to develop a maximum likelihood algorithm that estimates secondary structure using double-stranded or single-stranded RNA sequencing. We will also develop a machine-learning framework that predicts the functional category of novel non-coding RNAs using length and structure features from RNA-seq experiments. These methods will be used to annotate all RNA transcripts using experimental data from human and five model organisms.