III: Small: Computational Infrastructure for the Identification of Copy Number Variations from SNP Microarrays

III：小型：用于识别 SNP 微阵列拷贝数变异的计算基础设施

• 批准号: 0916102
• 负责人: Mehmet Koyuturk
• 金额: $ 49.76万
• 依托单位: Case Western Reserve University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916102&HistoricalAwards=false
• 关键词: III; Small; Computational; Infrastructure; Identification

It was recently discovered that copy number variations (CNVs) in human genome are quite common, and have important implications on phenotype. Currently, the primary platforms for large-scale detection and characterization of CNVs are SNP (single nucleotide polymorphism) microarrays. The current state-of-the-art in computational identification of CNVs from microarray data relies mostly on model-based approaches (e.g., Hidden Markov Models). However, such methods require extensive training data, which may not be always available. Furthermore, since these methods use common CNVs to train their models, they are not as successful in identifying rare CNVs, which are believed to make up a substantial proportion of all CNVs in the human population. The objective of this project is to develop optimization based algorithms and software for the identification and genotyping of CNVs, with a view to enabling fast and accurate identification of different types of CNVs (rare and common), without the requirement of training data.The proposed framework develops a novel computational approach by explicitly formulating CNV identification as a series of optimization problems that incorporate multiple factors, including sensitivity to noise, rarity/commonality of CNVs, genotypic specificity, and parsimony. This formulation enables development of efficient algorithms that treat identification of rare and common CNVs as different problems with different objective functions. Availability of the resulting software to the community will enable more efficient and accurate identification of CNVs in large samples, facilitating advances in understanding the role of CNVs in a range of complex phenotypes, including HIV, autism, schizophrenia, mental retardation, and many others. Furthermore, the computational innovations introduced by this project are likely to find applications in next generation sequencing.

拷贝数变异（拷贝数变异，copy number variation, CNVs）在人类基因组中非常普遍，并且对表型具有重要影响。目前，大规模检测和表征CNVs的主要平台是SNP（单核苷酸多态性）微阵列。目前从微阵列数据中计算识别CNVs的最新技术主要依赖于基于模型的方法（例如，隐马尔可夫模型）。然而，这种方法需要大量的训练数据，而这些数据可能并不总是可用的。此外，由于这些方法使用常见的CNVs来训练它们的模型，它们在识别罕见的CNVs方面并不成功，而罕见的CNVs被认为占人类所有CNVs的很大比例。该项目的目标是开发基于优化的CNVs鉴定和基因分型算法和软件，以期在不需要训练数据的情况下快速准确地识别不同类型的CNVs（稀有和常见）。该框架开发了一种新的计算方法，通过将CNV识别明确地表述为一系列优化问题，包括对噪声的敏感性、CNV的稀罕性/共性、基因型特异性和简约性。该公式能够开发有效的算法，将稀有和常见CNVs的识别视为具有不同目标函数的不同问题。由此产生的软件对社区的可用性将使在大样本中更有效和准确地识别CNVs，促进对CNVs在一系列复杂表型（包括HIV，自闭症，精神分裂症，智力迟钝等）中的作用的理解。此外，该项目引入的计算创新可能会在下一代测序中找到应用。