Gene Prediction by Markov Models and Complementary Methods

描述（由申请人提供）：我们建议在几个重要的方向上扩展在前一个资助期开发的用于真核基因发现的从头算自训练算法。首先，我们将此算法升级为多级数据挖掘方法，以允许在基因组计划的早期阶段构建一致的“基因组-转录组-蛋白质组”数据结构。在这里，我们将通过对现有和丰富的数据段（匿名基因组序列）进行无监督机器学习，并随后对缺失的生物信息（蛋白质编码基因和蛋白质）进行计算建模，来补偿实验数据（如EST数据）的各个片段中的信息不足。自训练算法的一个重要的新特征将是利用蛋白质水平信息来监测和增加由无监督迭代算法导出的模型的生物相关性。其次，我们将增强早期开发的小规模自训练算法，并在真菌和其他“紧凑”真核生物基因组（如秀丽隐杆线虫和黑腹果蝇）上进行测试，以处理最复杂的真核生物基因组。在这种更高层次的复杂性中，我们看到宿主基因只占据基因组的一小部分的物种，其GC组成可能是不均匀的，充满了转座因子和假基因（除了动物基因组，一些真菌病原体的基因组以及人类寄生虫及其载体也属于这一类）。第三，对于包含细菌，古细菌，病毒和真菌物种的人类微生物组，位于同质谱中基因组的另一端，我们将开发改进的从头基因识别算法和工具。这项工作将与美国和国外领先的基因组中心的测序和注释小组密切联系。

DESCRIPTION (provided by applicant): We propose to extend the ab initio self-training algorithms for eukaryotic gene finding developed in the previous grant period in several important directions. First we will upgrade this algorithm to a multilevel data mining approach to allow construction of a consistent "genome- transcriptome-proteome" data structure at the early stages of a genome project. Here, we will compensate for an information deficit in various segments of experimental data (such as EST data) by unsupervised machine learning on existing and abundant data segments (an anonymous genomic sequence) with subsequent computational modeling of missing biological information (protein-coding genes and proteins). An important new feature of the self-training algorithm will be the utilization of protein level information to monitor and increase biological relevance of the models derived by the unsupervised iterative algorithm. Second, we will enhance the self-training algorithm developed earlier on a smaller scale and tested on fungal and other "compact" eukaryotic genomes (such as Caenorhabditis elegans and Drosophila melanogaster) to work with most complex eukaryotic genomes. At this higher level of complexity we see species with host genes occupying just a small fraction of genome which can be inhomogeneous in GC composition, populated with transposable elements and pseudogenes (besides animal genomes, genomes of some fungal pathogens as well as human parasites and their vectors fall into this category). Third, for the human microbiome containing bacterial, archaeal, viral and fungal species, situated at yet another end of the genome in homogeneity spectrum, we will develop improved algorithms and tools for ab initio gene identification. This work will be done in close contact with sequencing and annotation groups from leading genome centers both in the US and abroad.