Statistical methods for estimation of copy number from next-generation sequencing

估计下一代测序拷贝数的统计方法

• 批准号: 7935506
• 负责人: Peter J Park
• 金额: $ 37.62万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-22 至 2012-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7935506
• 关键词: Address; Algorithms; Area; Attention; Bioinformatics; Biological; Clinical; Communities; Computer Systems; Computing Methodologies; Confidence Intervals; Copy Number Polymorphism; DNA copy number; Data; Data Sources; Detection; Development; Disease; Experimental Designs; Genome; Genomics; Health; Human; Human Genome; Individual; Length; Location; Malignant Neoplasms; Maps; Modeling; Morphologic artifacts; Nature; Patients; Phenotype; Population; Procedures; Process; Reading; Research; Research Design; Research Infrastructure; Research Personnel; Resolution; Resources; Sampling; Sensitivity and Specificity; Series; Single Nucleotide Polymorphism; Solid; Statistical Methods; Structure; System; Techniques; Technology; Translating; Translations; Variant; Work; base; chromatin immunoprecipitation; cluster computing; comparative genomic hybridization; computerized data processing; cost; design; disease phenotype; experience; genome sequencing; improved; interest; markov model; method development; next generation; novel; research study; tool; user-friendly; web interface

DESCRIPTION (provided by applicant): This application addresses board Challenge Area (08) Genomics and specific challenge topic, 08-DA-102 Improved Bioinformatics Analysis for Deep Sequencing. The number of human samples undergoing whole-genome sequencing is expected to increase dramatically in the next few years, as advances in next-generation sequencing technologies continue to lower the cost of sequencing. In addition to detection of sequence variation, these data can be used to estimate DNA copy number variation and subsequently to examine correlation between copy number and phenotype. In this proposal, we aim to develop a series of computational steps and integrated analysis pipeline for accurate estimation of copy number from next-generation sequencing data. This involves efficient processing of the sequencing data, including appropriate alignment procedures and correction for experiment artifacts. For estimation of the copy number along chromosomal location, we will develop novel segmentation procedures, both for a single sample and for multiple samples, to take advantage of the specific nature of sequencing data. Importantly, we also address issues in experimental design, especially the effect of depth of sequencing (genome coverage) and read length on the resolution and accuracy of copy number profiles. We use data from a number of platforms including Solexa, SOLiD, and CompleteGenomes for our studies. The pipeline developed in this proposal will be implemented on a powerful distributed computing system and will be made available freely to the research community. The results of this project will thus enable efficient extraction of copy number from whole-genome sequencing data and will facilitate rapid translation of next-generation sequencing technology to identify structural variations associated with normal or disease phenotypes.

描述（由申请人提供）： 该应用程序解决了板挑战领域（08）基因组学和特定的挑战主题，08-DA-102改进的生物信息学分析深度测序。随着下一代测序技术的进步继续降低测序成本，预计未来几年进行全基因组测序的人类样本数量将大幅增加。除了检测序列变异之外，这些数据还可用于估计DNA拷贝数变异，并随后检查拷贝数与表型之间的相关性。在这项提案中，我们的目标是开发一系列的计算步骤和集成的分析管道，以准确估计下一代测序数据的拷贝数。这涉及测序数据的有效处理，包括适当的比对程序和实验伪影的校正。为了估计沿着染色体位置的拷贝数沿着，我们将开发新的分割程序，用于单个样品和多个样品，以利用测序数据的特定性质。重要的是，我们还解决了实验设计中的问题，特别是测序深度（基因组覆盖率）和读取长度对拷贝数分布图的分辨率和准确性的影响。我们使用来自多个平台的数据，包括Solexa、SOLiD和CompleteGenomes。本提案中开发的管道将在一个功能强大的分布式计算系统上实现，并将免费提供给研究界。因此，该项目的结果将能够从全基因组测序数据中有效提取拷贝数，并将促进下一代测序技术的快速翻译，以识别与正常或疾病表型相关的结构变异。