Gene Prediction by Markov Models & Complementary Methods

项目描述（由申请人提供）：本项目的目标是将本团队先前为原核基因寻找算法GeneMark和GeneMark.hmm成功开发的自我训练范例引入真核基因寻找。鉴于真核生物基因组的数量不断增加，需要进行大规模测序工作，这一目标具有最重要的实际意义。更好地了解包括人类在内的这些生物的基因组生物学，对研究、控制和预防人类疾病具有至关重要的意义。我们计划开发一种从头算算法，用于匿名真核生物基因组的基因鉴定，并通过无监督学习程序建立基因模型。此外，我们计划开发该算法的扩展，将现有的外部信息（cDNA，蛋白质序列数据）集成到基因识别和自我训练程序中。最后，我们计划开发新的算法工具，从可用的序列数据中生成额外的外在信息。这些工具将对自我训练的基因发现算法以及新预测蛋白质的表征都很有用。

DESCRIPTION (provided by applicant): The goal of this project is bring to eukaryotic gene finding a paradigm of self-training successfully developed previously by our group for prokaryotic gene finding algorithms GeneMark and GeneMark.hmm. This objective is of the outmost practical importance given the growing number of genomes of eukaryotic organisms that undergo large scale sequencing efforts. Better understanding of genome based biology of these organisms, including human, has critical importance for studying, controlling and preventing human diseases. We plan to develop an ab initio algorithm for gene identification for anonymous eukaryotic genomes with gene models built by an unsupervised learning procedure. Also we plan to develop an extension of this algorithm integrating existing extrinsic information (cDNA, protein sequence data) into the gene identification and self-training procedures. Finally we plan to develop novel algorithmic tools for generating additional extrinsic information from available sequence data. These tools will be useful for both the self-trained gene finding algorithms as well as for the characterization of newly predicted proteins.