Computational genome-wide RNA profiling using next-generation sequencing

描述（由申请人提供）：最近的证据表明，非编码rna在细胞中普遍存在，其功能和结构的变化比以前想象的要大得多。多种新的RNA类别与许多疾病有关，了解这些RNA如何工作是一个关键的需要。虽然令人兴奋的发现不断积累，但我们对这些新rna的功能知识仍然有限。在这里，我们建议将一种新的高通量RNA双工测序技术与新的计算方法结合起来，在基因组尺度上经济地研究新的功能性非编码RNA。我们建议发展两种计算方法来表征基因组尺度上新发现的非编码rna。首先，我们将开发一种最大似然方法，使用优先排序单链或双链核苷酸的RNA-seq测定来估计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被比较基因组学和实验验证。我们将开发数据库和分析软件，并研究人类基因组和其他五种模式生物。总的来说，我们的发现将对非编码RNA生物学产生巨大的见解，并将对这些重要分子的持续研究产生重大影响。

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.