GENE PREDICTION BY MARKOV MODELS & COMPLEMENTARY METHODS

该项目的目标是建立强大的基因新方法 利用已有的积极经验和想法进行识别 经过验证的基因标记方法。中出现了两个重要问题 测序实验：预测原核生物中的先锋基因 和真核生物，以及真核DNA中基因结构的预测。 我们计划开发GeneMark-Genesis自我培训计划，这将 新测序中的并行模型学习和基因预测 原核生物基因组，包括已识别基因的分类 分成几个班级。 我们打算提高原核生物基因标记方法的准确性。 通过开发GeneMark-HMM版本进行基因识别 使用次要的统计模式。 在真核生物中，GeneMark程序家族将扩展一个 程序GeneMark-H，它结合了蛋白质编码的马尔可夫模型和 剪接位点和其他边界的马尔可夫模型的非编码区 网站。 另一个潜在的强大发展是NetGeneMark计划， 将高质量剪接位点检测系统Netgene与 基因标记对编码潜能的预测。所有方法都适用于 需要不断增加的原始DNA分析和解释 在人类基因组计划中。

The goal of the project is to build powerful new methods of gene identification utilizing the positive experience and ideas of the already proven GeneMark method. There are two important problems that appear in sequencing experiments: prediction of pioneer genes, both in prokaryotes and eukaryotes, and prediction of gene structure in eukaryotic DNA. We plan to develop the self-training program GeneMark-Genesis, that will parallel model learning and gene prediction in newly sequenced prokaryotic genomes, including the classification of identified genes into several classes. We intend to improve the accuracy of the GeneMark method for prokaryotic gene identification by developing a GeneMark-HMM version which will make use of minor statistical patterns. In eukaryotes, the family of GeneMark programs will be extended by a program GeneMark-H, that combines Markov models for protein-coding and non-coding regions with Markov models for splice sites and other boundary sites. Another potentially powerful development is the NetGeneMark program, integrating a quality splice site detection system, NetGene, with prediction of coding potential by GeneMark. All methods are to work for raw DNA analysis and interpretation for which there is an increasing need in the Human Genome project.