Probabilistic methods to identify noncoding RNA genes

鉴定非编码 RNA 基因的概率方法

• 批准号: 6536488
• 负责人: Elena Rivas
• 金额: $ 9.51万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-05-01 至 2004-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6536488
• 关键词: RNA; functional /structural genomics; genetic mapping; mathematical model; model design /development; nucleic acid structure; structural genes

DESCRIPTION (provided by the applicant): Because of the sheer volume of raw sequence data that is currently available, we need fast computational methods in order to identify potentially interesting functional genomic elements. In particular, there is also an accumulation of large quantities of data concerning conserved features from related genomes; these also need fast computational classification methods, which one could use to select a few prime candidates for experimental functional verification. We propose a probabilistic model to computationally classify conserved regions from related genomes using a combined approach that incorporates together structural (intrinsic) information with comparative (extrinsic) information. Our method contains three different models to account for three different types of structural alignment: RNA, coding or "something else". Because the algorithm is probabilistic, that gives us the ability to naturally compare the different models, and allows us to classify conserved features into one of the three functional classes. We are mostly interested in using this algorithm as a screen for novel RNA gene identification, and ultimately will perform experimental verification of putative novel RNA genes predicted with this algorithm in model organisms such as C.elegans and E. coli.

描述（由申请人提供）：由于原始材料的绝对体积， 目前可用的序列数据，我们需要快速的计算方法 以鉴定潜在的感兴趣的功能基因组元件。在 特别是大量数据的积累， 关于相关基因组的保守特征;这些也需要快速 计算分类方法，其中一个可以用来选择几个素数 实验功能验证的候选人。 我们提出了一个概率模型来计算保守区域的分类 从相关的基因组中提取， 结构（内在）信息与比较（外在）信息。 我们的方法包含三种不同的模型来解释三种不同的类型 结构对齐的关键：RNA，编码或“其他东西”。因为算法 是概率性的，这让我们能够自然地比较 模型，并允许我们将保守的特征分类为三个 函数类我们最感兴趣的是使用这个算法作为一个 筛选新的RNA基因鉴定，并最终将执行 实验验证推定的新的RNA基因预测与此 算法在模式生物如秀丽隐杆线虫和E.杆菌