Probabilistic methods to identify noncoding RNA genes

描述(由申请人提供)：由于原料的体积太大 目前可用的序列数据，我们需要快速的计算方法 以确定潜在的有趣的功能基因组元件。在……里面 特别是，还积累了大量的数据 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，可以用来选择几个素数 进行实验功能验证的候选对象。 我们提出了一个概率模型来计算分类保守区域 使用一种结合在一起的组合方法从相关基因组中 结构性(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 结构比对：RNA、编码或“其他东西”。因为这个算法 是概率的，这使我们能够自然地比较不同的 模型，并允许我们将保守特征分类为以下三种之一 功能类。我们最感兴趣的是将此算法用作 筛选新的RNA基因识别，并最终将执行 用这一方法预测的新RNA基因的实验验证 在秀丽隐杆线虫和大肠杆菌等模式生物中的算法。

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.