GENE PREDICTION--MARKOV MODELS AND COMPLEMENTARY METHODS

基因预测--马尔可夫模型和补充方法

• 批准号: 6286238
• 负责人: MARK BORODOVSKY
• 金额: $ 41.47万
• 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 2003-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6286238
• 关键词: DNA; RNA splicing; computer assisted sequence analysis; computer program /software; computer system design /evaluation; eukaryote; frameshift mutation; genetic mapping; introns; mathematical model; model design /development; nucleic acid sequence; protein sequence; statistics /biometry

DESCRIPTION (Adapted from the Investigator's Abstract): The goal of the project is to build more accurate and powerful DNA sequence interpretation algorithms utilizing the positive experience and ideas of previously proven GeneMark and GenMark.hmm methods. We plan to improve the quality of gene finding in prokaryotic genomes in terms of reliable and accurate prediction of gene starts and detection of frameshift sequencing errors. We also plan to develop a machine-learning iterative procedure for deriving all necessary models for precise gene prediction/annotation from totally anonymous prokaryotic sequences. For eukaryotic species, we will improve the accuracy of the ab initio method GeneMark.hmm by building more accurate models for splice sites and initiation/termination sites, and we will address the problem of accurately finding intergenic regions with polyadenilation sites and promoters. On the basis of GeneMark.hmm, we plan to develop an integrated gene finding approach by "projecting" pieces of diverse extrinsic evidence into DNA level, the translating them into DNA patterns and combining these patterns with statistical patterns of DNA coding and non-coding sequence within a generalized HMM model. The most intriguing sources of this additional information are evolutionary conserved regions in DNA sequences of closely related species, functional motifs in protein sequences and protein sequence patterns reflecting three dimensional structural motifs. All these newly developed methods, as well as several others mentioned in the proposal, will deal with anonymous DNA for which interpretation is increasingly needed in the post-genomic era.

描述（改编自研究者摘要）：项目的目标 是建立更准确和强大的DNA序列解释算法 利用先前证明的基因标记的积极经验和想法， GenMark.嗯方法。 我们计划提高原核基因组中基因发现的质量， 可靠准确地预测基因起始和检测移码 排序错误。我们还计划开发一个机器学习迭代 用于推导精确基因的所有必要模型的程序 从完全匿名的原核序列中进行预测/注释。 对于真核生物，我们将提高从头算方法的准确性 通过建立更精确的剪接位点模型， 启动/终止位点，我们将准确解决 发现具有多聚腺苷酸化位点和启动子的基因间区域。 在基因标记的基础上，我们计划开发一个整合的基因发现 通过将不同的外在证据“投射”到DNA水平， 将它们转化为DNA模式，并将这些模式与 广义DNA编码序列和非编码序列的统计模式 HMM模型这些额外信息的最有趣的来源是 近缘物种DNA序列中进化保守区， 蛋白质序列中的功能基序和蛋白质序列模式 三维结构图案。 所有这些新开发的方法，以及在 这项提案将处理匿名DNA，对匿名DNA的解释越来越多， 在后基因组时代所需要的。