Advances in computational gene finding

描述（申请人提供）：过去20年的技术成就使基因组测序成为一项相对简单的任务。然而，解码这些信息被证明是困难得多的，是本世纪的重大挑战之一。推进我们对基因结构和调控的理解，最终将导致对抗癌症和其他疾病的新疗法，更便宜和更有营养的食物，更少浪费的材料和能源，以及更好地了解我们自己。基因组时代产生的最重要的产品之一将是每个生物体的基因目录。即使在最好的情况下，制作这些目录也是一项艰巨的任务。基因组测序的步伐不断加快，如果我们能够理解这些新的基因组，它们代表了丰富的信息。该提案旨在通过推进计算基因发现的最新技术来提高我们对基因组的认识。我们的算法利用未开发的新信息源，并有望提高我们发现新基因和已知同源基因的能力。我们的具体计划包括：（a）对任何基因组的基因预测程序进行自动化训练，（B）开发第一种算法，该算法将基因结构的广义隐马尔可夫模型与蛋白质家族结构的轮廓隐马尔可夫模型相结合，（c）创建第一个基因发现器，该基因发现器包含关于超螺旋应力下DMA双链体稳定性的信息，（d）建立新的算法，利用全基因组表达阵列和大规模并行测序方法等高通量转录本分析技术，以及（e）通过互联网提供网上应用程序和支持。

DESCRIPTION (provided by applicant): The technological achievements of the past 20 years have made sequencing a genome a relatively simple task. Decoding this information, however, has proved to be much more difficult, and is one of the great challenges for this century. Advancing our understanding of how genes are structured and regulated will eventually lead to novel therapeutics for combating cancer and other diseases, to cheaper and more nutritious food, to less wasteful materials and energy sources, and to a greater understanding of ourselves. One of the enduring, and most important products resulting from the genome era will be the catalogs of genes for each organism. Producing these catalogs is a difficult task even under the best of circumstances. The pace of genome sequencing continues to increase, and these new genomes represent a wealth of information if we can understand them. This proposal seeks to improve our knowledge of genomes by advancing the state of the art in computational gene finding. Our algorithms leverage untapped and new sources of information, and are expected to improve our ability to find both novel genes and genes with known homologs. Our specific plans include (a) automated training of gene prediction programs for any genome, (b) developing the first algorithm that merges a generalized hidden Markov model for gene structure with a profile hidden Markov model for protein family structure, (c) creating the first gene finder that incorporates information about DMA duplex stability under superhelical stresses, (d) building new algorithms that take advantage of high-throughput transcript profiling technologies such as whole genome expression arrays and massively parallel sequencing methods, and (e) providing web-based applications and support via the Internet.