Software Systems for Detecting Rare Muations

用于检测罕见突变的软件系统

• 批准号: 7745833
• 负责人: TODD M SMITH
• 金额: $ 11万
• 依托单位: GEOSPIZA, INC.
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2010-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7745833
• 关键词: Address; Algorithms; Americas; Basic Science; Bioconductor; Biological Assay; Biology; Cancer Diagnostics; Cataloging; Catalogs; Cause of Death; Cells; Clinic; Clinical; Communities; Community Developments; Complex; Complex Mixtures; Computer software; DNA; DNA Sequence; Data; Data Set; Detection; Development; Diagnostic; Diagnostic tests; Disease; Drug resistance; Environment; Error Sources; Experimental Designs; Future; Genetic; Genetic Variation; Genome; Goals; Health; Heart Diseases; Human; Individual; Laboratories; Leadership; Life; Link; Malignant Neoplasms; Maps; Measures; Medicine; Methods; Morphologic artifacts; Mutation; Mutation Detection; Noise; Online Systems; Output; Pharmacotherapy; Phase; Phenotype; Play; Population; Positioning Attribute; Preparation; Probability; Procedures; Process; Reading; Relative (related person); Research; Research Personnel; Research Project Grants; Resources; Role; Sampling; Sampling Biases; Sampling Studies; Scientist; Sensitivity and Specificity; Sequence Alignment; Small Business Innovation Research Grant; Software Tools; Somatic Mutation; Source; System; Techniques; Technology; Testing; Time; Variant; Vision; War; Work; anticancer research; base; cancer cell; cancer type; clinical application; computerized tools; cost; data management; design; effective therapy; improved; insight; instrument; neoplastic cell; new technology; next generation; novel; open source; pancreatic neoplasm; prognostic; prognostic indicator; public health relevance; research study; response; single molecule; software development; software systems; statistics; tool; trend; tumor; tumor growth; web based interface

DESCRIPTION (provided by applicant): In November 2008, The Scientist opened an on-line opinion piece with the following quote: "After tens of billions of US federal dollars (plus billions more from private sources) and nearly 40 years of aggressive research, the war on cancer is depressingly far from over. Cancer will soon become the leading cause of death in America, passing heart disease. At some point in their lives, 43% of the public will get some form of cancer." While much progress has been made over the years, effective treatments for many forms of cancer are still lacking. Until the many forms of cancer are better understood, treatment options will continue to lag behind. Next generation DNA sequencing (NGS) technologies hold great promise as tools for building a new understanding of cancer and its origins. Deep sequencing provides more sensitive ways to detect the germline and somatic mutations that cause different types of cancer as well as identify new mutations within small subpopulations of tumor cells that can be prognostic indicators of tumor growth or drug resistance. The ultimate goal is to use NGS technologies in the clinic. Before this vision can be realized, many obstacles must be overcome. Assay costs must be significantly lowered and sample throughput must be substantially increased relative to today's capabilities. Achieving this goal will require that we have streamlined procedures for sample preparation and laboratory processes, a complete understanding of NGS systems, error profiles, and assay dynamics, and robust validatable software systems to support diagnostic tests in the clinical enterprise. Geospiza's FinchLab software platform addresses a large number of issues related to operating NGS instruments and laboratory processes in clinical environments. However, our understanding of NGS errors and how to completely characterize NGS datasets, with respect to their potential to deliver high quality information, is incomplete. Through the proposed research, Geospiza and collaborators at the Mayo Clinic will remove many of the obstacles that keep this vision of cancer diagnostics from becoming reality. In the Phase I project, we will test the feasibility of developing clinical systems by characterizing a limited number of NGS datasets for true variants, false positive, and false negative errors by cataloging discrepant bases relative to control sequences, with respect to sequence contexts, random noise, laboratory steps, and instrument artifacts. The catalogs will then be used to develop statistical algorithms that can analyze large numbers of aligned reads and assign variant detection probabilities to individual bases, as well as calculate summary statistics that can be used to assign descriptive values to datasets from individual samples, and subsequently identify sample artifacts and issues related to sample processing. Geospiza will combine the insights gained, and new software tools developed, into the FinchLab system to give researchers better ways to work with NGS data and more clear-cut methods for visualizing genetic assay results presented in web-based interfaces. In addition, Geospiza will promote community involvement by making many of the core algorithms available through BioConductor. PUBLIC HEALTH RELEVANCE: The SBIR project "Software Systems for Detecting Rare Mutations" will deliver new software technologies to further advance the applications for deep DNA sequencing in personalized medicine by improving methods for detecting rare mutations that define cancer types and determine how a cancer cell may grow and respond to, or resist, treatment. In addition to improving cancer research and diagnostics, the software developed will have general use for any application where DNA sequencing is used to understand the genetic basis of human health, disease, and response to drug therapies.

描述（由申请人提供）：2008年11月，《科学家》在网上发表了一篇评论文章，其中引用了以下内容：“经过数百亿美元的联邦资金（加上来自私人来源的数十亿美元）和近40年的积极研究，癌症战争还远未结束。癌症很快将超过心脏病，成为美国的头号死因。在他们生命中的某个时刻，43%的公众会患上某种形式的癌症。“虽然多年来取得了很大进展，但仍然缺乏对许多形式癌症的有效治疗。在更好地了解多种形式的癌症之前，治疗方案将继续落后。下一代DNA测序（NGS）技术作为建立对癌症及其起源的新理解的工具具有很大的前景。深度测序提供了更灵敏的方法来检测导致不同类型癌症的种系和体细胞突变，以及识别肿瘤细胞小亚群中的新突变，这些突变可以作为肿瘤生长或耐药性的预后指标。最终目标是在临床上使用NGS技术。在实现这一愿景之前，必须克服许多障碍。分析成本必须显著降低，样品通量必须相对于当今的能力大幅增加。要实现这一目标，我们需要简化样品制备和实验室流程的程序，全面了解NGS系统、误差分布和分析动态，以及强大的可验证软件系统，以支持临床企业的诊断测试。Geospiza的FinchLab软件平台解决了大量与在临床环境中操作NGS仪器和实验室流程相关的问题。然而，我们对NGS错误的理解以及如何完全表征NGS数据集，以及它们提供高质量信息的潜力，是不完整的。通过拟议的研究，Geospiza和马约诊所的合作者将消除许多阻碍癌症诊断愿景成为现实的障碍。在I期项目中，我们将测试开发临床系统的可行性，方法是通过对相对于对照序列的差异碱基进行编目来表征有限数量的NGS数据集的真变异、假阳性和假阴性错误，以及序列背景、随机噪声、实验室步骤和仪器伪影。然后，目录将用于开发统计算法，该算法可以分析大量对齐的读数并将变异检测概率分配给各个碱基，以及计算汇总统计量，该汇总统计量可用于将描述性值分配给来自各个样本的数据集，并随后识别样本伪影和与样本处理相关的问题。Geospiza将联合收割机将获得的见解和开发的新软件工具结合到FinchLab系统中，为研究人员提供更好的方法来处理NGS数据，并提供更清晰的方法来可视化基于网络的界面中呈现的遗传分析结果。此外，Geospiza还将通过BioConductor提供许多核心算法来促进社区参与。 公共卫生关系：SBIR项目“检测罕见突变的软件系统”将提供新的软件技术，通过改进检测罕见突变的方法，进一步推进深度DNA测序在个性化医疗中的应用，这些突变定义了癌症类型，并确定癌细胞如何生长和对治疗作出反应或抵抗。除了改进癌症研究和诊断外，开发的软件还将广泛用于任何应用，其中DNA测序用于了解人类健康，疾病和药物治疗反应的遗传基础。