Computational Gene Modeling and Genome Sequence Assembly

描述(由申请人提供)： DNA测序技术的新发展极大地促进了使用测序来回答生物学和医学中的基本问题。全基因组测序正被用于研究癌症，发现患者基因组中的致病基因变异，以及研究人类遗传多样性。许多WGS项目正在为其基因组尚未测序的物种启动。通过RNA-SEQ对信使RNA进行测序已经导致了许多项目的爆炸性增长，这些项目旨在表征多种细胞类型和许多物种中的转录基因，同时发现新的基因和已知基因的新剪接变体。这些基于测序的研究产生了大量的数据，这反过来需要复杂、高效和创新的新算法，使组装这些基因组并识别其基因内容成为可能。我们建议开发新的基于云计算的组装算法，从最新的测序技术产生的短片段中组装基因组。同时，我们将继续改进现有的汇编器，扩展它们以处理新的和多样化的数据类型，包括“第三代”序列。我们还将接触外部团体，帮助他们组装新品种，根据需要修改我们的软件，并继续推动组装技术的极限。 在基因发现领域最令人兴奋的技术发展之一是RNA-seq，这是一种用于捕获细胞中的mrna并对其进行排序的新协议。这项技术正在很好地取代作为获取转录蛋白编码基因的传统EST测序方法，以及用于测量转录水平的微阵列杂交实验。我们建议开发新的算法，以利用已经开始出现的大量新的RNA-SEQ数据。我们已经开发了两个新的算法，tophat和cufflink，用于RNA-SEQ分析，这是第一个能够发现以前未知的剪接位点和异构体的算法。这些工具具有处理更广泛种类的序列数据的新功能，构成了我们开发集成基因搜索器的计划的基础，这些基因搜索器可以识别新基因、已知基因的新异构体和融合基因，并将这些方法包括在基因组注释管道中。

DESCRIPTION (provided by applicant): New developments in DNA sequencing technology have spurred a tremendous increase in the use of sequencing to answer fundamental questions in biology and medicine. Whole- genome sequencing is being used to study cancer, to discover disease-causing gene variants in patient genomes, and to study human genetic diversity. Numerous WGS projects are being launched for species whose genomes have not yet been sequenced. Sequencing of messenger RNA through RNA-seq has led to an explosion of projects to characterize transcribed genes in multiple cell types and in many species, and simultaneously to discover new genes and new splice variants of known genes. These sequencing-based studies generate enormous amounts of data, which in turn require sophisticated, efficient, and innovative new algorithms that will make it possible to assemble these genomes and identify their gene content. We propose to develop new cloud-computing based assembly algorithms to assemble genomes from short reads generated by the latest sequencing technologies. In parallel, we will continue to improve our existing assemblers, extending them to handle new and diverse data types, including "3rd-generation" sequences. We will also reach out to outside groups to help them assemble novel species, modifying our software as needed and continuing to push the limits of assembly technology. One of the most exciting recent technology developments in the gene finding arena is RNA- seq, a new protocol for capturing and sequencing the mRNA in a cell. This technique is well on its way to replacing both conventional EST sequencing as a method for capturing transcribed protein-coding genes, and microarray hybridization experiments for measuring transcript levels. We propose to develop new algorithms to take advantage of the flood of new RNA-seq data that has begun to appear. We have already developed two new algorithms, TopHat and Cufflinks, for RNA-seq analysis, which are the first to be able to discover previously unknown splice sites and isoforms. These tools, enhanced with new features to handle a wider variety of sequence data, form the basis of our plans to develop integrated gene finders that can identify novel genes, novel isoforms of known genes, and fusion genes, and to include these methods in a genome annotation pipeline.