PREDICTION OF GENE LOCATIONS USING STOCHASTIC MODELS

拟议项目的总体目标是开发一种改进的 用于预测新测序DNA中基因位置的计算机算法。 这个问题是众所周知的，但还远远没有成功 解决了。解决这个问题的一种新方法是同时利用剪接位点和 随机形式的DNA序列信息的编码/非编码 模特们。有几个具体目标必须实现： 1)最有效的非平稳马尔可夫链模型 蛋白质编码区(外显子)的选择必须基于 已编撰的真核生物学习集的统计分析 DNA根据拟合优度检验。此外，最高效的类型 非编码DNA序列(内含子)的普通马尔可夫链模型 必须基于对内含子学习集的分析来确定。 2)一组改进的参数，用于计算 识别能量(估计剪接的相对活性 站点)将从已知剪接的扩展学习集中提取 网站。 3)剪接位点随机模型和DNA编码/非编码模型 序列(在贝叶斯类型的算法中连接在一起，找出 DNA片段的编码潜在值)必须组合并 作为一种新的多阶段基因鉴定方法 地点。 4)在对方法的准确性进行评估后，对决策的标度 阈值、提高计算性能和创建 针对交互环境的方法，将软件制作完成 可供科学界使用。

The general objective of the proposed project is to develop an improved computer algorithm for predicting gene locations in newly sequenced DNA. This problem is well known but still far from being successfully resolved. A new approach to the problem utilizes both splicing site and coding/noncoding DNA sequence information in the form of stochastic models. There are several specific aims that have to be achieved: 1) The most efficient type of nonstationary Markov chain model of the protein coding region (exons) has to be chosen on the basis of statistical analysis of previously compiled learning sets of eukaryotic DNA according to the goodness-of-fit test. Also, the most efficient type of an ordinary Markov chain model of noncoding DNA sequences (introns) has to be determined based on the analysis of the intron learning set. 2) An improved set of parameters needed for calculation of the value of the discrimination energy (estimating the relative activity of a splicing site) will be extracted from an expanded learning set of known splicing sites. 3) Splicing site stochastic models and models of coding/noncoding DNA sequences (joined together in a Bayes type algorithm finding out the value of the coding potential of a DNA fragment) have to be combined and enhanced as a new multistage method for the identification of gene locations. 4) After evaluating the method's accuracy, scaling of decision making thresholds, improving computational performance, and creating an interactive environment for the method, the software will be made available to the scientific community.