Computational Gene Modeling and Genome Sequence Assembly

计算基因建模和基因组序列组装

• 批准号: 8335454
• 负责人: Steven L. Salzberg
• 金额: $ 59.52万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8335454
• 关键词: Algorithms; Alternative Splicing; Area; Base Sequence; Bioinformatics; Biology; Brugia malayi; Cells; Code; Complex; Computer software; Computers; DNA; DNA Sequence; Data; Development; Disease; Explosion; Expressed Sequence Tags; Floods; Future; Gene Expression; Gene Fusion; Generations; Genes; Genetic Variation; Genome; Government; Histocompatibility Testing; Human; Human Biology; Human Genetics; Human Genome; Institution; Malignant Neoplasms; Measures; Medicine; Memory; Messenger RNA; Methods; Microarray Analysis; Modeling; Mus; Output; Patients; Protein Isoforms; Proteins; Protocols documentation; RNA; RNA Splicing; Reading; Research Personnel; Running; Scientist; Site; Source Code; System; Techniques; Technology; Time; Transcript; Universities; Variant; Work; base; cell type; computing resources; design; fusion gene; genome sequencing; human disease; improved; innovation; insight; novel; open source; programs; research study; software development; technology development; tool

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.

描述（由申请人提供）： DNA测序技术的新发展促进了测序在回答生物学和医学中的基本问题方面的巨大增长。全基因组测序被用于研究癌症，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项目正在为基因组尚未测序的物种启动。通过RNA-seq对信使RNA进行测序导致了大量项目的爆发，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，这反过来又需要复杂，高效和创新的新算法，这将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，从最新测序技术产生的短读段组装基因组。与此同时，我们将继续改进现有的汇编器，扩展它们以处理新的和不同的数据类型，包括“第三代”序列。我们还将接触外部团体，帮助他们组装新物种，根据需要修改我们的软件，并继续推动组装技术的极限。 基因发现竞技场中最令人兴奋的最新技术发展之一是RNA- seq，这是一种用于捕获细胞中mRNA并对其进行测序的新方案。这项技术正在取代传统的EST测序作为捕获转录蛋白编码基因的方法，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。我们已经开发了两种新的算法，TopHat和Cufflinks，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，增强了新的功能，以处理更广泛的序列数据，形成了我们计划开发集成的基因查找器的基础，可以识别新基因，已知基因的新亚型和融合基因，并将这些方法纳入基因组注释管道。