Software Systems for Detecting Rare Muations

描述（由申请人提供）：2008年11月，《科学家》杂志在网上发表了一篇评论文章，其中写道：“经过数百亿美元的联邦资金（加上私人来源的数十亿美元）和近40年的积极研究，令人沮丧的是，与癌症的战争远未结束。癌症将很快超过心脏病，成为美国人死亡的首要原因。43%的公众在人生的某个阶段会患上某种形式的癌症。”虽然多年来取得了很大进展，但对许多形式的癌症仍然缺乏有效的治疗方法。在我们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）技术有望成为建立对癌症及其起源的新认识的工具。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。最终目标是在临床中使用NGS技术。在实现这一愿景之前，必须克服许多障碍。化验成本必须显著降低，样品通量必须相对于今天的能力大幅提高。要实现这一目标，我们需要简化样品制备和实验室流程，全面了解NGS系统、错误概况和分析动态，以及可靠的可验证软件系统，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误差的理解，以及如何完全描述NGS数据集，以及它们提供高质量信息的潜力，是不完整的。通过这项拟议的研究，Geospiza和梅奥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。在第一阶段项目中，我们将测试开发临床系统的可行性，通过对相对于对照序列的差异碱基进行编目，以表征有限数量的NGS数据集的真变异、假阳性和假阴性错误，并考虑序列背景、随机噪声、实验室步骤和仪器伪像。然后，目录将用于开发统计算法，该算法可以分析大量排列的读取，并为单个碱基分配不同的检测概率，以及计算汇总统计数据，该统计数据可用于为来自单个样本的数据集分配描述性值，并随后识别样本工件和与样本处理相关的问题。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，并提供更清晰的方法来将基于网络的基因分析结果可视化。此外，Geospiza将通过BioConductor提供许多核心算法来促进社区参与。

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.