Comparative Analysis of CNV Calling Algorithms: Literature Survey and a Case Study Using Bovine High-Density SNP Data.

Microarrays Pub Date : 2013-06-25 DOI:10.3390/microarrays2030171

Lingyang Xu, Yali Hou, Derek M Bickhart, Jiuzhou Song, George E Liu

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引用次数: 52

Abstract

Copy number variations (CNVs) are gains and losses of genomic sequence between two individuals of a species when compared to a reference genome. The data from single nucleotide polymorphism (SNP) microarrays are now routinely used for genotyping, but they also can be utilized for copy number detection. Substantial progress has been made in array design and CNV calling algorithms and at least 10 comparison studies in humans have been published to assess them. In this review, we first survey the literature on existing microarray platforms and CNV calling algorithms. We then examine a number of CNV calling tools to evaluate their impacts using bovine high-density SNP data. Large incongruities in the results from different CNV calling tools highlight the need for standardizing array data collection, quality assessment and experimental validation. Only after careful experimental design and rigorous data filtering can the impacts of CNVs on both normal phenotypic variability and disease susceptibility be fully revealed.

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CNV调用算法的比较分析:文献综述和牛高密度SNP数据的案例研究。

拷贝数变异(拷贝数变异)是指与参考基因组相比，一个物种的两个个体之间基因组序列的增益和损失。来自单核苷酸多态性(SNP)微阵列的数据现在通常用于基因分型，但它们也可用于拷贝数检测。在阵列设计和CNV调用算法方面已经取得了实质性进展，并且已经发表了至少10项人体比较研究来评估它们。在这篇综述中，我们首先调查了现有的微阵列平台和CNV调用算法的文献。然后，我们研究了一些CNV调用工具，利用牛高密度SNP数据评估它们的影响。不同CNV调用工具的结果存在较大的不一致性，这突出了标准化阵列数据收集、质量评估和实验验证的必要性。只有经过精心的实验设计和严格的数据过滤，才能充分揭示CNVs对正常表型变异性和疾病易感性的影响。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Microarrays

自引率

0.00%

发文量

审稿时长

11 weeks

期刊介绍： High-Throughput (formerly Microarrays, ISSN 2076-3905) is a multidisciplinary peer-reviewed scientific journal that provides an advanced forum for the publication of studies reporting high-dimensional approaches and developments in Life Sciences, Chemistry and related fields. Our aim is to encourage scientists to publish their experimental and theoretical results based on high-throughput techniques as well as computational and statistical tools for data analysis and interpretation. The full experimental or methodological details must be provided so that the results can be reproduced. There is no restriction on the length of the papers. High-Throughput invites submissions covering several topics, including, but not limited to: Microarrays, DNA Sequencing, RNA Sequencing, Protein Identification and Quantification, Cell-based Approaches, Omics Technologies, Imaging, Bioinformatics, Computational Biology/Chemistry, Statistics, Integrative Omics, Drug Discovery and Development, Microfluidics, Lab-on-a-chip, Data Mining, Databases, Multiplex Assays.