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MLPAstats:一个用于使用 MLPA 数据进行拷贝数改变综合分析的 R GUI 包。

MLPAstats: an R GUI package for the integrated analysis of copy number alterations using MLPA data.

机构信息

Center for Research in Environmental Epidemiology, Doctor Aiguader 88, Barcelona 08003, Spain.

出版信息

BMC Bioinformatics. 2011 May 11;12:147. doi: 10.1186/1471-2105-12-147.

Abstract

BACKGROUND

Multiplex-Dependent Probe Amplification (MLPA) is a cost-effective experimental method for candidate gene studies, aimed at the identification of copy number alterations. The analysis of such genetic variants, from electropherogram peak intensities, involves two main stages. First, peak normalization for each probe is required to remove the contribution of probe size to peak intensity. Second, the statistical significance of peak alteration between case and control samples is estimated. A number of methods have been proposed in each step with varying levels of complexity and precision. However, there is no single framework from which the results of each method and possible combinations at each step can be assessed.

RESULTS

We present MLPAstats, an R package designed to integrate the methods for exploring different analysis scenarios in a reliable way. A GUI has been developed to allow researchers to find their optimal analysis strategy.

CONCLUSIONS

MLPAstats is an analysis tool that promotes the use of cost-effective MLPA suitable for candidate gene studies. Its R implementation allows future methods to be easily incorporated, while its GUI will facilitate its use by non-expert analysts. A vignette describing a set-by-step tutorial is also available with the package.

摘要

背景

多重依赖探针扩增(MLPA)是一种针对候选基因研究的具有成本效益的实验方法,旨在识别拷贝数改变。通过分析电泳图谱峰强度来分析此类遗传变异,涉及两个主要阶段。首先,需要对每个探针进行峰归一化,以去除探针大小对峰强度的贡献。其次,估计病例和对照样本之间峰改变的统计显著性。在每个步骤中,已经提出了许多方法,具有不同的复杂性和精度水平。然而,没有一个单一的框架可以评估每个方法的结果以及每个步骤的可能组合。

结果

我们提出了 MLPAstats,这是一个专为以可靠的方式集成探索不同分析场景的方法而设计的 R 包。开发了一个图形用户界面(GUI),以允许研究人员找到最佳的分析策略。

结论

MLPAstats 是一种分析工具,可促进使用具有成本效益的 MLPA 进行候选基因研究。其 R 实现允许轻松合并未来的方法,而其 GUI 将便于非专家分析师使用。该软件包还提供了一个描述逐步教程的示例。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cac1/3118163/f69bbe14ef5f/1471-2105-12-147-1.jpg

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