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理解的工具前景广阔。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，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。然而，要完成从主要应用的证据到实际应用的过渡，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不断的技术发展和激烈的供应商竞争正在使数据收集成本商品化，使系统更易于评估。然而，采用NGS技术的最大障碍是缺乏能够轻松访问处理数据所需的巨大生物信息学和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、罕见和从头开始的变异水平。遗传变异必须在注释丰富的生物学背景下提出，以确定临床效用、频率和可能的生物学影响。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特定应用数据集再到注释的各种资源整合在一起，所有这些都编织到具有交互用户界面(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将创建综合方法，以便对遗传变异进行强有力的检测和丰富的背景信息。以癌症基因组学中的变异分析为模型系统，我们将进行研究，通过深入表征现有和新兴的NGS平台的数据、质量值(QV)重新校准工具和比对算法来提高分析灵敏度，以了解在数据中造成错误的系统伪像。为了改善研究人员了解变体的生物学背景、功能和潜在临床用途的方式，我们将开发方法，将来自许多样本的分析结果与RNA-Seq和现有数据(如GEO和SRA中的数据)以及来自数据库SNP、癌症基因组数据库和ENCODE的信息资源等新的NGS数据集相结合。最后，我们将开发必要的可扩展计算基础设施和新颖的用户界面，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，我们将生产集成的敏感分析系统，利用NGS识别DNA序列之间的极低水平(1：1000)变化，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跟踪病毒流行和了解自身免疫性疾病的分析。 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): 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.