Analysis Tools and Software for Second Generation Sequencing Data

第二代测序数据的分析工具和软件

• 批准号: 8806870
• 负责人: Rafael Angel Irizarry
• 金额: $ 8.38万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-11 至 2015-03-09
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8806870
• 关键词: Analysis; Tools; Software; Second; Generation

DESCRIPTION (provided by applicant): Second-generation sequencing (sec-gen) technology is poised to radically change how genomic data is obtained and used. Capable of sequencing millions of short strands of DNA in parallel, this technology can be used to assemble complex genomes for a small fraction of the price and time of previous technologies. In fact, a recently formed international consortium, the 1000 Genomes Project, plans to sequence the genomes of approximately 1,200 people. The possibility of comparative analysis at the sequence level of a large number of samples across multiple populations may be achievable within the next five years. These datasets also present unprecedented challenges in statistical analysis and data management. For example, a central goal of the 1000 Genomes Project is to quantify across-sample variation at the single nucleotide level. At this resolution, small error rates in sequencing prove significant, especially for rare variants. Furthermore, sec-gen sequencing is a relatively new technology for which potential biases and sources of obscuring variation are not yet fully understood. Therefore, modeling and quantifying the uncertainty inherent in the generation of sequencing reads is of utmost importance. Properly relating this uncertainty to the true underlying variation in the genome, especially, variation between and among populations will be essential for projects that use sec-gen sequencing data to meet their scientific goals. Although genome sequencing is the application that most attention has received, sec-gen technology is also being used to produce quantitative measurements related to applications previously associated with microarrays. Of these, chromatin immunoprecipitation followed by sequencing (ChIP- Seq) has been the most successful. Existing tools have been developed for analyzing one sample at a time. Methodology for drawing inference from multiple samples has not yet been developed. The demand for such methods will increase rapidly as the technology becomes more economical and multiple samples become standard. Other applications for which statistical methodology is needed are RNA and microRNA transcription analysis. In all these sequencing applications, a number of critical steps are required to convert raw intensity measures into the sequence reads that will be used in down-stream analysis. Ad-hoc approaches, that assign weights to each base call, are unsuitable. Our goal is to create a sound and unified statistical and computational methodology for representing and managing uncertainty throughout the sec-gen sequencing data analysis pipeline built on a robust, modular and extensible software platform.

描述(由申请人提供)：第二代测序(SEC-GEN)技术有望从根本上改变基因组数据的获取和使用方式。这项技术能够并行对数百万条DNA短链进行测序，可以用来组装复杂的基因组，而成本和时间只是以前技术的一小部分。事实上，最近成立的一个名为1000基因组计划的国际联盟计划对大约1200人的基因组进行测序。在未来五年内，有可能对多个种群的大量样本进行序列水平的比较分析。这些数据集还在统计分析和数据管理方面提出了前所未有的挑战。例如，1000基因组计划的一个中心目标是在单核苷酸水平上量化样本间的变异。在这种分辨率下，测序中的微小错误率被证明是显著的，特别是对于罕见的变种。此外，秒基因测序是一项相对较新的技术，其潜在的偏差和模糊变异的来源尚未完全了解。因此，对测序读数产生过程中固有的不确定性进行建模和量化是非常重要的。适当地将这种不确定性与基因组的真实潜在变异联系起来，特别是种群之间的变异，对于使用秒基因测序数据来实现其科学目标的项目来说是至关重要的。尽管基因组测序是最受关注的应用，秒基因技术也被用来产生与以前与微阵列相关的应用相关的定量测量。其中，染色质免疫沉淀和测序(CHIP-SEQ)是最成功的。现有的工具已经开发出来，可以一次分析一个样本。从多个样本中提取推断的方法尚未开发出来。随着技术变得更加经济，多样品成为标准，对这种方法的需求将迅速增加。其他需要统计方法的应用是RNA和microRNA转录分析。在所有这些测序应用中，需要许多关键步骤将原始强度测量转换为将在下游分析中使用的序列读数。为每个基本呼叫分配权重的自组织方法是不合适的。我们的目标是创建一种健全和统一的统计和计算方法，以在一个健壮、模块化和可扩展的软件平台上表示和管理整个SEC-GEN测序数据分析管道中的不确定性。