PREDICTION OF GENE LOCATIONS USING STOCHASTIC MODELS

使用随机模型预测基因位置

• 批准号: 2209036
• 负责人: MARK BORODOVSKY
• 金额: $ 12.31万
• 依托单位: GEORGIA INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-03-15 至 1997-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2209036
• 关键词: PREDICTION; GENE; LOCATIONS; USING; STOCHASTIC

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.

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。 这个问题众所周知，但距离成功还很远 解决了。 解决这个问题的新方法利用了剪接位点和 随机形式的编码/非编码 DNA 序列信息 模型。 有几个必须实现的具体目标： 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 对先前编译的真核学习集进行统计分析 根据拟合优度检验的 DNA。 另外，最有效的类型 非编码 DNA 序列（内含子）的普通马尔可夫链模型 必须基于内含子学习集的分析来确定。 2) 计算值所需的一组改进参数 辨别能（估计剪接的相对活性 site）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编码/非编码DNA模型 序列（在贝叶斯类型算法中连接在一起，找出 DNA片段的编码潜力的值）必须被组合并且 增强作为一种新的多阶段基因鉴定方法 地点。 4) 评估方法的准确性后，调整决策范围 阈值，提高计算性能，并创建 该方法的交互环境，将制作软件 可供科学界使用。