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.

迄今为止，拷贝数变异 (CNV) 是人类疾病的常见原因。 然而，微阵列价格昂贵并且不能有效地检测CNV。 该项目旨在在一项临床测试中检测点突变和 CNV，而无需进行任何检测。 我们的目标是开发一种易于使用的解决方案，允许和精英。 研究人员无需生物信息学和脚本技能即可进行此类高级分析。 由此带来的好处包括：  节省成本：通过消除额外的微阵列测试的需要，实验室将能够简化他们的工作 分析工作流程利用下一代测序 (NGS) 数据。  临床产量：提议的解决方案将能够检测 1K bp 范围内的较小 CNV 事件 这对于上级来说至关重要，因为他们正在评估基因组。 诊断目的。  易于使用：建议的解决方案将嵌入 Golden Helix VarSeq 产品中，该产品 旨在支持复杂的分析工作流程，而无需编写脚本或编程。 随着精准医疗变得越来越重要，CNV 分析等先进工作流程变得至关重要 解决方案的简单性也将简化医疗保健培训。 正在进入该领域的专业人士。 出于我们的目的，我们可以将 CNV 定义为相对于人类参考的 DNA 的任何删除或插入 长度 ≥ 50 bp 的序列缺失和插入短于 50 bp 很常见，但一般来说可以。 通过 NGS 数据分析中使用的常规变异检出算法检测到的 CNV 的大小可能有所不同。 我们同样对检测数十千碱基或更大的 CNV 感兴趣。 尺寸，因为我们热衷于尽可能扩大检测范围。 NGS 数据的 CNV 检测目前是人类遗传学的一个关键主题，其解决方案与大多数不同。 我们将建立以研究为导向的小组。 迄今为止所描述的并使它们商业化的大多数当前解决方案仅使用模型。 我们的 CNV 检测方法将采用单一证据指标。 能够合并源自参考集合的多个证据指标的概率模型 这是一个关键的改进，使我们的方法与现有方法中描述的方法不同。 此外，这项工作还将带来更先进的功能，例如杂合性丢失的检测。 (LOH)、拷贝中性 LOH 和单亲二倍体 (UPD) 事件。