Integrating CNV analysis into a NextGen sequencing clinical analytics platform

拷贝数变异（CNV）是人类疾病的常见原因。到今天为止，CNV最常见的是 使用微阵列检测。然而，微阵列是昂贵的，并且不能有效地检测CNVs 低于1K bp范围。该项目旨在在一次临床试验中检测点突变和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， 研究人员进行这种类型的高级分析，而不需要生物信息学和脚本技能。的 由此产生的好处包括： 成本节约：通过消除对额外微阵列测试的需求，实验室将能够简化其 利用NextGen测序（NGS）数据的分析工作流程。 临床产量：所提出的解决方案将能够检测低于1K bp范围内的较小CNV事件 而这些都是微阵列无法检测到的。这对临床医生来说至关重要，因为他们正在评估基因组， 诊断目的。 易于用途：建议的解决方案将嵌入Golden Hacking VarSeq产品中， 旨在实现复杂的分析工作流，而无需编写脚本或编程。简化 先进的工作流程，如CNV分析是至关重要的，因为精准医疗越来越多， 更主流。该解决方案的简单性还将简化医疗保健培训 正在进入这个领域的专业人士。 为了我们的目的，我们可以将CNV定义为相对于人参照的任何DNA缺失或插入 长度≥ 50 bps的序列。短于50 bp的缺失和插入是常见的，但一般来说， 通过NGS数据分析中使用的常规变异识别算法检测。CNV的大小可能在 高达几兆字节。我们同样感兴趣的是检测CNV，它们是数十个或更多的DNA片段， 尺寸，因为我们热衷于尽可能大地扩展检测范围。 从NGS数据中检测CNV是目前人类遗传学中的一个关键课题。不同的解决方案，大多 发展了以研究为导向的小组。我们将在现有最佳解决方案的基础上再接再厉， 并使其可商购获得。大多数当前的解决方案使用的模型仅 能够合并单一证据度量。我们的CNV检测方法将使用 能够合并从参考集合导出的多个证据度量的概率模型 样品这是一个关键的改进，它将我们的方法与 文学此外，这项工作将导致更先进的能力，如检测杂合性丢失 (LOH)、拷贝中性洛缺失和单亲二体性（UPD）事件。

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.