Predicting Gene Structure: Vertebrate Genome Comparison

描述(由申请人提供)：从头基因预测是使用基因组序列作为唯一输入的基因结构的自动识别。我们建议继续一个项目，该项目已经显著提高了脊椎动物从头基因预测的准确性。当我们开始的时候，GENSCAN预测了一个正确的外显子-内含子结构，在一个开放阅读框架(ORF)中，只有10%的人类基因座位。我们现在已经发布了在35%的人类基因座上预测正确ORF的系统。我们预测的RT-PCR和测序已经验证了数百个新的人类基因。通过这次更新，我们的目标是继续推动脊椎动物基因预测的准确性及其在生物医学应用中的效用的改进。目的1提高脊椎动物基因结构预测的准确性-A。建立多基因组比对中信息模式的改进模型，比较多个脊椎动物基因组的序列将使我们能够估计每个位置的选择程度和模式，从而获得更准确的基因预测。我们提出了一种基于学习实际对齐列中存在的模式的稳健方法，即使它们部分是由于排序、对齐和组装错误造成的。所提出的模型是我们成功的TWINSCAN基因预测器的推广。在初步研究中，它的准确性超过了以往任何一种人类基因预测系统。B.开发目标DNA序列中信息模式的改进模型我们建议系统地模拟以前被认为过于罕见或难以捉摸而不值得注意的基因结构的规律性，例如剪接增强子和抑制子，内含子长度和剪接位点序列之间的相关性，以及内含子和非转录区域中重复插入的差异模式。目标2开发和维护软件、网络服务器和基因组注释我们的目标是通过向生物医学研究界提供更准确的基因预测来提高科学理解和人类健康。因此，我们将开发高质量的开源软件，各种基因组的参数集，以及一个用户可以提交序列进行注释的网络服务器。最后，我们将分发和展示每个脊椎动物基因组的每个新组装的注释。该项目将产生开源软件，比目前的任何系统都能更准确地预测脊椎动物基因组中的外显子-内含子结构。这也将提高RT-PCR进行基因验证的敏感性和特异性。

DESCRIPTION (provided by applicant): De novo gene prediction is the automated identification of gene structures using genome sequences as the only inputs. We propose to continue a project that has significantly improved the accuracy of de novo gene prediction in vertebrates. When we started, GENSCAN predicted a correct exon-intron structure throughout one open reading frame (ORF) at only 10% of human gene loci. We have now published systems that predict a correct ORF at 35% of human loci. RT-PCR and sequencing of our predictions have verified hundreds of new human genes. With this renewal we aim to continue driving improvements in the accuracy of vertebrate gene prediction and its utility for biomedical applications. Aim 1 Improve the accuracy of gene structure prediction in vertebrates - A. Develop improved models of informative patterns in multi-genome alignments Comparing the sequences of multiple vertebrate genomes should allow us to estimate the degree and pattern of selection at each site, lead ingto more accurate gene predictions. We propose a robust approach based on learning the patterns that exist in real alignment columns, even if they are due in part to sequencing, alignment, and assembly errors. The proposed model is a generalization of our successful TWINSCAN gene predictor. In preliminary studies its accuracy surpassed that of any previous gene prediction system for human. B. Develop improved models of informative patterns in the target DNA sequence We propose to systematically model regularities in gene structure that were previously considered too rare or elusive to be worthy of attention, such as splicing enhancers and suppressors, correlations between intron length and splice site sequence, and differential patterns of repeat insertion in introns versus non-transcribed regions. Aim 2 Develop and maintain software, web server, and genome annotations Our goal is to improve scientific understanding and human health by providing more accurate gene predictions to the biomedical research community. Therefore, we will develop high quality, open source software, parameter sets for a variety of genomes, and a web server where users can submit sequences for annotation. Finally, we will distribute and display annotation for each new assembly of every vertebrate genome. This project will result in open source software that predicts exon-intron structures in vertebrate genomes more accurately than any current system. It will also increase the sensitivity and specificity of gene verification by RT-PCR.