Discovery of Novel RNA Genes in Genomic DNA Sequences

描述（由申请人提供）： 到目前为止，后基因组分析的重点是蛋白质编码基因及其产物的识别和注释。相比之下，编码功能性RNA的DNA序列的鉴定在很大程度上被忽视了。功能性RNA可以编码与其他RNA或蛋白质相互作用的反式作用分子，如tRNA、rRNA、RNaseP RNA、小核仁RNA和microRNA;它们也可以顺式作为调节mRNA转录后表达的非翻译区发挥作用。最近的实验和计算研究表明，在原核生物中可能有数百种未知的功能RNA，在真核生物中可能有数千种。 该提案的主要目标是开发一种计算方法来识别完整DNA基因组中的新RNA基因和功能RNA元件。机器学习技术将用于通过与不编码RNA的序列进行比较来识别功能性RNA编码序列的标志。几种类型的信号可用于区分功能性RNA，包括：1）全局序列组成的差异，2）计算的RNA二级结构特征，即折叠自由能和3）RNA结构共有的特定序列元件。这些和其他参数可以使用机器学习方法快速尝试和测试。该方法将针对输入数据库、参数化和机器学习方法和架构针对个体基因组进行优化。结果将通过交叉验证测试、比较基因组学和计算的二级结构和折叠自由能进行计算评估。RNA表达和功能的实验研究将与合作者一起进行。 初步的研究已经证明了这种方法预测新的功能RNA在大肠杆菌中的能力。大肠杆菌，其他细菌和古细菌的支持计算交叉验证和实验确认。我们将测试和应用这种方法来发现新的功能RNA在真核生物基因组，包括S。certaliae、C.以及人类。这些更大更复杂的基因组的RNA预测将需要优化计算参数，以及开发适当的输入数据集和训练算法。 人类基因组中新功能RNA的预测为了解新的调控和发育过程提供了机会。与人类疾病有关的已知RNA，如端粒酶RNA（癌症、衰老）、XIST RNA（X染色体失活）和BIC（原癌基因），强调了开发一种方法来鉴定人类RNA基因的完整补体的重要性。

DESCRIPTION (provided by applicant): To date, post-genomic analysis has focused on identification and annotation of protein-coding genes and their products. In comparison, the identification of DNA sequences encoding functional RNA has been largely neglected. Functional RNAs can encode trans-acting molecules that interact with other RNAs or proteins, such as tRNAs, rRNAs, RNaseP RNA, small nucleolar RNAs and microRNAs; they can also function in cis as untranslated regions that regulate post-transcriptional expression of mRNAs. Recent experimental and computational studies suggest that there may be hundreds of unknown functional RNAs in prokaryotes and thousands in eukaryotes. The primary goal of this proposal is to develop a computational approach to the identification of novel RNA genes and functional RNA elements in complete DNA genomes. Machine learning techniques will be used to recognize hallmarks of functional RNA coding sequences by comparison with sequences that do not encode RNAs. Several types of signals are useful in discriminating functional RNA including: 1) differences in global sequence composition, 2) calculated RNA secondary structure features, i.e. free energy of folding and 3) specific sequence elements common to RNA structure. These and other parameters can be rapidly tried and tested using machine learning methods. This method will be optimized for individual genomes with respect to input databases, parameterization and machine learning method and architecture. Results will be evaluated computationally by cross-validation testing, comparative genomics, and calculated secondary structure and free energy of folding. Experimental studies of RNA expression and function will be conducted in conjunction with collaborators. Preliminary studies have demonstrated the power of this approach to predict novel functional RNAs in E. coli, other bacteria and archaea as supported by computational cross-validation and experimental confirmation. We will test and apply this approach to discover new functional RNAs in eukaryotic genomes including S. cervisiae, C. elegans, and humans. RNA prediction for these larger and more complex genomes will require the optimization of computational parameters, as well as the development of appropriate input datasets and training algorithms. The prediction of novel functional RNAs in the human genome presents an opportunity to understand new regulatory and developmental processes. Known RNAs implicated in human disease, such as telomerase RNA (cancer, aging), XIST RNA (X-chromosome inactivation) and BIC (a proto-oncogene) underscore the importance of developing a method to identify the full complement of human RNA genes.