Software Systems for Detecting Rare Muations

描述(申请人提供)：2008年11月，《科学家》杂志在网上发表了一篇评论文章，引用了以下几句话：经过数百亿美元的联邦资金(外加来自私人渠道的数十亿美元)和近40年的积极研究，抗癌战争远未结束。癌症很快将成为美国的头号死因，仅次于心脏病。在他们一生中的某个时候，43%的公众将患上某种形式的癌症。尽管多年来已经取得了很大进展，但许多形式的癌症仍然缺乏有效的治疗方法。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前景广阔。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，并在肿瘤细胞的小亚群中识别新的突变，这些突变可以作为肿瘤生长或耐药性的预后指标。最终目标是将NGS技术应用于临床。在实现这一愿景之前，必须克服许多障碍。检测成本必须大幅降低，样品产量必须相对于目前的能力大幅提高。要实现这一目标，我们将需要简化样品准备和实验室流程的程序，全面了解NGS系统、错误概况和分析动力学，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软件平台解决了与在临床环境中操作NGS仪器和实验室流程相关的大量问题。然而，我们对NGS错误以及如何完全描述NGS数据集提供高质量信息的潜力的理解是不完整的。通过这项拟议的研究，Geospiza和梅奥诊所的合作者将消除阻碍癌症诊断这一愿景成为现实的许多障碍。在第一阶段项目中，我们将测试开发临床系统的可行性，方法是通过分类相对于对照序列的差异碱基，针对序列上下文、随机噪声、实验室步骤和仪器人工制品，表征有限数量的NGS数据集的真变异、假阳性和假阴性错误。然后，这些目录将被用来开发统计算法，该算法可以分析大量的比对读数，将不同的检测概率分配给单个碱基，以及计算汇总统计数据，这些统计数据可用于为来自单个样本的数据集分配描述性值，并随后识别与样本处理相关的样本人工制品和问题。Geospiza将把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，并为在基于网络的界面中呈现的遗传分析结果提供更清晰的方法。此外，Geospiza将通过BioConductor提供许多核心算法，从而促进社区参与。 与公共卫生相关：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，进一步推动深度DNA测序在个性化医学中的应用。罕见突变定义癌症类型，并确定癌细胞如何生长和对治疗做出反应或抵抗治疗。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使用DNA测序来了解人类健康、疾病和药物治疗反应的遗传基础的应用程序。

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.