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

最近发现，人类基因组中的拷贝数变异(CNV)很常见，并且对表型有重要的影响。目前，大规模检测和表征CNV的主要平台是单核苷酸多态性(SNP)微阵列。目前从微阵列数据中计算识别CNV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CNV来训练他们的模型，它们在识别稀有CNV方面并不成功，而稀有CNV被认为在人类人口中占所有CNV的相当大比例。这个项目的目标是开发基于优化的CNV识别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CNV(稀有和常见)。该框架通过将CNV识别明确地描述为一系列包含多种因素的优化问题，包括对噪声的敏感性、CNV的稀有性/共性、基因特异性和简约性，开发了一种新的计算方法。这一公式使得高效算法的开发成为可能，这些算法将稀有和常见CNV的识别视为具有不同目标函数的不同问题。向社区提供所产生的软件将使在大样本中更有效和准确地识别CNV，促进在理解CNV在一系列复杂表型中的作用方面取得进展，包括艾滋病毒、自闭症、精神分裂症、精神发育迟滞和许多其他类型。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。

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.