Association Analysis Software for Mining Clinical Next-Gen Sequencing Data

用于挖掘临床下一代测序数据的关联分析软件

• 批准号: 8236680
• 负责人: TIMOTHY J DURFEE
• 金额: $ 15万
• 依托单位: DNASTAR, INC.
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-25 至 2013-06-24
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8236680
• 关键词: Automatic Data Processing; Back; Bioinformatics; Cataloging; Catalogs; Cereals; Clinical; Clinical Medicine; Clinical Research; Collaborations; Communities; Computer software; Computers; DNA Sequence; Data; Data Set; Databases; Detection; Development; Diagnosis; Diagnostics Research; Disease; Documentation; Ensure; Evaluation; Foundations; Generations; Genes; Genetic; Genome; Goals; Hour; Housing; Human Genome; Individual; Laboratories; Life; Maps; Marketing; Medical; Medical Research; Metadata; Mining; Modification; Performance; Phase; Physicians; Population; Price; Process; Prostate Cancer Vaccine; Reporting; Research Infrastructure; Research Personnel; Running; Sample Size; Sampling; Scientist; Sequence Analysis; Solid; Solutions; Suggestion; Technology; Testing; Treatment Efficacy; Vaccines; Variant; base; clinically relevant; cohort; cost effectiveness; exome; feeding; flexibility; genome wide association study; genome-wide; human disease; insight; meetings; member; next generation; oncology; programs; response; software development; tool

DESCRIPTION (provided by applicant): Remarkable improvements in throughput, accuracy and cost-effectiveness of next-generation sequencing (next-gen) technologies are ushering in a new era of clinical medicine. Genome wide association studies (GWAS) in particular have begun to leverage these advances to determine the complete catalog of common and rare variants for each member of a cohort. The resolving power of this approach has the potential to greatly accelerate our understanding, diagnosis and treatment of human disease. Unfortunately, analysis of these massive data sets requires that several disparate pieces of software be cobbled together including a large capacity next-gen sequencing assembler, variation detection modules, mapping and comparison tools for tens to hundreds of variant reports, statistical analysis packages, reporting tools, and so on. Combining and using these tools typically requires extensive bioinformatic expertise as the software is rarely well documented or supported and often depends on having elaborate hardware. These hurdles makes next-gen based GWAS inaccessible to the vast majority of the crucial user base, the physician researchers. The goal of this proposal is to assemble the essential next-gen based GWAS software components into a single coherent pipeline that that is fully equipped to meet the needs of the medical research community. Consistent with DNASTAR's 28 year tradition, the software will be easy to use, run on a reasonably priced (<$3000) desktop computer, and will be fully documented and supported. The pipeline will consist of two modules already available through DNASTAR, SeqMan NGen 3.0 (SM NGen 3.0) and ArrayStar. SM NGen 3.0, our recently released human genome scale assembly and analysis package, forms the front end of pipeline. Reference-guided assemblies of whole human genome or exome next-gen data sets produce variation reports including impact on gene features and associations with the dbSNP database. Putative variations can be verified by direct inspection of the alignment through the SeqMan Pro component of the package. Variation reports from each member of a GWAS cohort will then be fed into our multi-sample comparison and analysis program, ArrayStar, at the back end of the pipeline. ArrayStar has the infrastructure for multi-sample management and processing which can be easily adapted to GWAS analysis. These adaptations and their documentation are a central focus of this application. Critical to the successful development of this software is our collaboration with Dr. Douglas McNeel (Dept. of Oncology, UW-Madison). The exomes from a panel of prostate cancer vaccine recipients, including responders and non-responders, from the McNeel lab will be sequenced as input from which to build the pipeline using iterative cycles of development followed by evaluation by the McNeel group. This relationship offers an ideal opportunity to build the analysis and reporting software needed by physician researchers to form, test and validate GWAS generated hypotheses. PUBLIC HEALTH RELEVANCE: The easy to use tools to be developed and integrated in this project will dramatically enhance the efficiency of clinical and diagnostic research for a wide range of life scientists and medical professionals using next-generation DNA sequencing technologies, allowing new treatments to be brought to market sooner, enhancing scientists' understanding of treatment efficacy, and supporting the tailoring of different treatments to specific groups of individuals based on their genetic composition. These tools will be flexible enough to support critical analysis of large populations for clinical research and easy enough to use for all life scientists and medical professionals to feel comfortable with them.

描述（由申请人提供）：下一代测序（下一代）技术在通量、准确性和成本效益方面的显著改进正在引领临床医学的新时代。特别是全基因组关联研究（GWAS）已经开始利用这些进展来确定队列中每个成员的常见和罕见变异的完整目录。这种方法的分辨率有可能大大加快我们对人类疾病的理解、诊断和治疗。不幸的是，这些海量数据集的分析需要将几个不同的软件拼凑在一起，包括大容量的下一代测序组装器、变异检测模块、用于数十至数百个变异报告的映射和比较工具、统计分析包、报告工具、组合和使用这些工具通常需要广泛的生物信息学专业知识，因为软件很少有良好的文档记录或支持，并且通常依赖于精心制作的硬件。这些障碍使得绝大多数关键用户群（医生研究人员）无法访问基于下一代的GWAS。 该提案的目标是将基本的下一代GWAS软件组件组装到一个完整的连贯管道中，该管道完全能够满足医学研究界的需求。与DNASTAR 28年的传统一致，该软件将易于使用，在价格合理（<3000美元）的台式计算机上运行，并将得到充分的文档和支持。该管道将包括两个模块，已经通过DNASTAR，SeqMan NGen 3.0（SM NGen 3.0）和ArrayStar。SM NGen 3.0，我们最近发布的人类基因组规模组装和分析软件包，形成了管道的前端。全人类基因组或外显子组下一代数据集的参考引导组装产生变异报告，包括对基因特征的影响以及与dbSNP数据库的关联。通过软件包的SeqMan Pro组件直接检查比对，可以验证推定的变异。GWAS队列中每个成员的变异报告将在管道的后端输入我们的多样本比较和分析程序ArrayStar。ArrayStar具有多样品管理和处理的基础设施，可以轻松适应GWAS分析。这些调整及其文档是此应用程序的中心焦点。 成功开发该软件的关键是我们与道格拉斯麦克尼尔博士（部门）的合作。of Oncology，UW-Madison）。来自McNeel实验室的一组前列腺癌疫苗接受者（包括应答者和非应答者）的外显子组将被测序，作为使用迭代开发周期构建管道的输入，然后由McNeel小组进行评估。这种关系提供了一个理想的机会，建立医生研究人员所需的分析和报告软件，以形成，测试和验证GWAS生成的假设。 公共卫生关系：该项目中开发和集成的易于使用的工具将大大提高使用下一代DNA测序技术的广泛生命科学家和医学专业人员的临床和诊断研究的效率，使新的治疗方法更快地推向市场，提高科学家对治疗效果的理解，并支持根据特定群体的遗传组成，为他们量身定制不同的治疗方法。这些工具将足够灵活，以支持临床研究中对大量人群的批判性分析，并易于所有生命科学家和医学专业人员使用。