Integrating CNV analysis into a NextGen sequencing clinical analytics platform

将 CNV 分析集成到 NextGen 测序临床分析平台中

• 批准号: 9408437
• 负责人: Andreas Scherer
• 金额: $ 15万
• 依托单位: GOLDEN HELIX, INC.
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2018-03-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9408437
• 关键词: Adopted; Adoption; Algorithms; Autistic Disorder; Automation; Base Pairing; Bioinformatics; Cancer Prognosis; Classification; Clinical; Collection; Complex; Copy Number Polymorphism; Cost Savings; Crohn' s disease; DNA; Data; Detection; Diagnosis; Diagnostic; Disease; Event; Gene Frequency; Genes; Genome; Genomics; Goals; Grant; HIV Infections; Health Professional; Hospitals; Human; Human Genetics; Light; Literature; Loss of Heterozygosity; Lupus; Mainstreaming; Measures; Medicine; Methods; Mission; Modeling; Nucleotides; Organ; Pancreatitis; Parkinson Disease; Pathologic; Phase; Phenotype; Point Mutation; Predictive Value; Predisposition; Process; Reporting; Research; Research Personnel; Sampling; Savings; Schizophrenia; Small Business Innovation Research Grant; Statistical Models; Testing; Time; Tissues; Training; Uniparental Disomy; Variant; Work; Work Simplification; clinical sequencing; design; exome; improved; interest; next generation sequencing; precision medicine; programs; research clinical testing; skills; software development; statistics; trait; tumor; whole genome

Copy Number Variations (CNVs) are a common cause of disease in humans. As of today, CNVs are most often detected using microarrays. However, microarrays are expensive and are not able to effectively detect CNVs below 1K bp range. This project is designed to detect both point mutations and CNVs in one clinical test without having to utilize microarrays. It is our goal to develop an easy to use solution that allows clinicians and researchers to conduct this type of advanced analysis without requiring bioinformatics and scripting skills. The resulting benefits include:  Cost savings: By eliminating the need for additional microarray tests, labs will be able to streamline their analytics workflows utilizing NextGen Sequencing (NGS) data.  Clinical yield: The proposed solution will be able to detect smaller CNV events in the sub 1K bp range that remain undetected by microarrays. This is crucial for clinicians as they are evaluating a genome for diagnostic purposes.  Ease of Use: The proposed solution will be embedded in the Golden Helix VarSeq product that is designed to enable complex analytics workflows without the need to script or program. The simplification of advanced workflows such as the CNV analysis is crucial as precision medicine is becoming more and more mainstream. The simplicity of the solution will also streamline the training of healthcare professionals who are entering into this field. For our purpose, we may define CNVs as any deletion or insertion of DNA with respect to the human reference sequence of size ≥ 50 bps. Deletions and insertions shorter than 50 bp are common, but in general can be detected through routine variant calling algorithms used in the analysis of NGS data. CNVs may range in size up to several megabases. We are equally interested in detecting CNVs that are tens of kilobases or greater in size, as we are keen to extend the detection range as large as possible. CNV detection from NGS data is currently a key topic in human genetics. Different solutions from mostly research oriented groups have been developed. We will build upon the best of the solutions that have been described to date and make them commercially available. Most current solutions use models that are only capable of incorporating a single evidence metric. Our approach to CNV detection will instead make use of a probabilistic model capable of incorporating multiple evidence metrics derived from a collection of reference samples. This is a key improvement that differentiates our approach from existing methods described in the literature. Also, this work will lead to more advanced capabilities such as the detection of loss of heterozygosity (LOH), copy-neutral LOH and uniparental disomy (UPD) events.

拷贝数变化（CNV）是人类疾病的常见原因。截至今天，CNV通常是 使用微阵列检测。但是，微阵列很昂贵，无法有效检测CNV 低于1K BP范围。该项目旨在在一个临床测试中检测点突变和CNV 必须使用微阵列。我们的目标是开发一种易于使用的解决方案，该解决方案允许临床医生和 研究人员在不需要生物信息学和脚本技巧的情况下进行这种高级分析。 最终的好处包括： 节省成本：通过消除对其他微阵列测试的需求，实验室将能够简化其 分析使用NextGen测序（NGS）数据。 临床产量：拟议的解决方案将能够检测到以下1K bp范围内的较小的CNV事件 这仍然没有被微阵列发现。这对临床医生至关重要，因为他们正在评估一个基因组 诊断目的。 易用性：提议的解决方案将嵌入到金螺旋的Varseq产品中 旨在实现复杂的分析工作流，而无需脚本或程序。简化 CNV分析等高级工作流程至关重要，因为精确药物变得越来越多， 更多主流。解决方案的简单性还将简化医疗保健培训 进入该领域的专业人士。 出于我们的目的，我们可以将CNV定义为DNA相对于人类参考的任何删除或插入 大小≥50bps的顺序。删除和插入短于50 bp的较短，但通常可以是 通过用于NGS数据分析的常规变体调用算法检测。 CNV的大小可能范围 多达几个巨蛋。我们同样有兴趣检测数十千酶或更大的CNV 大小，因为我们渴望将检测范围延长尽可能大。 NGS数据的CNV检测目前是人类遗传学的关键主题。与主要的解决方案不同 已经开发了以研究为导向的群体。我们将基于最好的解决方案 迄今为止所描述的并使它们在商业上可用。大多数当前解决方案都使用仅 能够转换单个证据指标。我们的CNV检测方法将使用 概率模型，能够合并来自参考集合的多个证据指标 样品。这是一个关键改进，将我们的方法与现有方法区分开 文学。此外，这项工作将导致更高级的功能，例如检测杂合性的丧失 （LOH），复制中性LOH和单亲疾病（UPD）事件。