Software Systems for Detecting Rare Mutations

描述（由申请人提供）：下一代DNA测序（NGS）技术作为建立对健康和疾病的新认识的工具具有很大的前景。在了解癌症的情况下，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。然而，完成从主要应用证明到实际应用的过渡，需要许多基础和临床研究小组能够有效地利用NGS。NGS平台和服务提供商之间持续的技术发展和激烈的供应商竞争正在商品化数据收集成本，使系统更具可评估性。然而，采用NGS技术的唯一最大障碍是缺乏能够轻松访问数据所需的大量生物信息学和IT基础设施的系统。在变异分析的情况下，这样的系统将需要处理非常大的数据集，并准确地预测常见的、罕见的和新生的变异水平。遗传变异必须在注释丰富的生物学背景下呈现，以确定临床效用、频率和假定的生物学影响。用于这项工作的软件系统必须将来自许多样本的数据与从核心分析算法到应用特定数据集到注释的资源集成在一起，所有这些都编织到具有交互式用户界面（ui）的计算系统中。这样的端到端系统目前还不存在。在这个项目中，Geospiza将为遗传变异的鲁棒检测和丰富情境化创建集成方法。利用癌症基因组学中的变异分析作为模型系统，我们将通过深入表征现有和新兴的NGS平台、质量价值（QV）再校准工具和校准算法的数据来开展研究，以提高分析灵敏度，以了解导致数据错误的系统人为因素。为了提高研究人员对变异的生物学背景、功能和潜在临床应用的理解，我们将开发方法，将来自许多样本的分析结果与新的NGS数据集（如RNA-Seq）和现有数据（如GEO和SRA）以及来自dbSNP、癌症基因组数据库和ENCODE的信息资源相结合。最后，我们将开发必要的可扩展计算基础设施和新颖的UI，以组织和处理数据，并探索和注释结果。通过这项工作以及后续的产品开发，我们将生产出综合灵敏的检测系统，利用NGS识别DNA序列之间非常低（1:1000）的变化水平，以检测癌症突变和新出现的耐药性。我们的工具和基础设施以后可以应用于旨在跟踪病毒流行和了解自身免疫性疾病的分析。

DESCRIPTION (provided by applicant): Next generation DNA sequencing (NGS) technologies hold great promise as tools for building a new understanding of health and disease. In the case of understanding cancer, 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. Completing the transition from proof of principal applications to practical applications, however, requires that many basic and clinical research groups to be able to effectively utilize NGS. Ongoing technical developments and intense vendor competition amongst NGS platform and service providers are commoditizing data collection costs making systems more assessable. However, the single greatest impediment to the adoption of NGS technology is the lack of systems that create easy access to the immense bioinformatics and IT infrastructures needed to work with the data. In the case of variant analysis, such systems will need to process very large datasets, and accurately predict common, rare, and de novo levels of variation. Genetic variation must be presented in an annotation-rich, biological context to determine the clinical utility, frequency, and putative biological impact. Software systems used for this work must integrate data from many samples together with resources ranging from core analysis algorithms to application specific datasets to annotations, all woven into computational systems with interactive user interfaces (UIs). Such end-to-end systems currently do not exist. In this project, Geospiza will create integrated methods for robust detection and rich contextualization of genetic variants. Using variation analysis in cancer genomics as a model system, we will conduct research to improve assay sensitivity by deeply characterizing data from existing and emerging NGS platforms, quality value (QV) recalibration tools, and alignment algorithms, to understand the systematic artifacts that create errors in the data. To improve how researchers understand a variant's biological context, function and potential clinical utility, we will develop methods to combine assay results from many samples with de novo NGS datasets for assays like RNA-Seq and existing data such as those in GEO and SRA, and information resources from dbSNP, cancer genome databases, and ENCODE. Finally, we will develop the necessary scalable computing infrastructure and novel UI's needed to organize and process the data and explore and annotate the results. Through this work, and follow on product development, we will produce integrated sensitive assay systems that harness NGS for identifying very low (1:1000) levels of changes between DNA sequences to detect cancerous mutations and emerging drug resistance. Our tools and infrastructure can be later applied in assays designed to follow viral epidemics, and understand autoimmune disorders. 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.