MLPAnalyzer: data analysis tool for reliable automated normalization of MLPA fragment data.
MLPAnalyzer: data analysis tool for reliable automated normalization of MLPA fragment data.
复制标题
DOI：
10.3233/clo-2008-0428
复制
发表时间：
2008
期刊：
影响因子：
--
通讯作者：
Meijer GA
中科院分区：
文献类型：
--
作者：
Coffa J;van de Wiel MA;Diosdado B;Carvalho B;Schouten J;Meijer GA
背景：Multiplex ligations dependent Probe Amplification （MLPA）是一种快速、简单、可靠和定制的高分辨率检测单个基因拷贝数变化的方法，可实现高通量分析。该技术通常用于研究大样本序列中的特定基因。数据量大，不同探针扩增产物之间的PCR效率差异，以及样品间的差异，给数据分析和解释带来了挑战。因此，我们着手开发一种易于使用的MLPA数据分析策略和工具，同时仍然考虑到上述变化的来源。材料和方法：MLPAnalyzer是在Visual Basic for Applications中开发的，可以直接接受来自毛细管序列系统的大量文件格式。所有MLPA探针信号的大小都被确定和过滤，质量控制步骤被执行，并且与大小相关的峰值强度变化被纠正。计算测试样本的DNA拷贝数比率，以表格视图的形式显示，并生成一组综合图形。为了验证这种方法，使用专用的MLPA混合物在6种不同的结直肠癌细胞系上进行了MLPA反应。使用我们的程序将生成的数据归一化，并使用统计方法和目视检查将结果与先前进行的阵列- cgh结果进行比较。结果和讨论：两种技术的柱状图和直比的目视检查显示非常相似的结果，而所有MLPA探针的平均Pearson矩相关性为0.42。因此，我们的研究结果表明，按照我们建议的策略进行自动MLPA数据处理可能会有很大的用处，特别是在处理大型MLPA数据集时，当样本质量不同时，或者MLPA电泳图的解释过于复杂时。然而，重要的是要认识到自动化MLPA数据处理可能只有在考虑专用实验设置时才会成功。
Background: Multiplex Ligation dependent Probe Amplification (MLPA) is a rapid, simple, reliable and customized method for detection of copy number changes of individual genes at a high resolution and allows for high throughput analysis. This technique is typically applied for studying specific genes in large sample series. The large amount of data, dissimilarities in PCR efficiency among the different probe amplification products, and sample-to-sample variation pose a challenge to data analysis and interpretation. We therefore set out to develop an MLPA data analysis strategy and tool that is simple to use, while still taking into account the above-mentioned sources of variation. Materials and Methods: MLPAnalyzer was developed in Visual Basic for Applications, and can accept a large number of file formats directly from capillary sequence systems. Sizes of all MLPA probe signals are determined and filtered, quality control steps are performed, and variation in peak intensity related to size is corrected for. DNA copy number ratios of test samples are computed, displayed in a table view and a set of comprehensive figures is generated. To validate this approach, MLPA reactions were performed using a dedicated MLPA mix on 6 different colorectal cancer cell lines. The generated data were normalized using our program and results were compared to previously performed array-CGH results using both statistical methods and visual examination. Results and Discussion: Visual examination of bar graphs and direct ratios for both techniques showed very similar results, while the average Pearson moment correlation over all MLPA probes was found to be 0.42. Our results thus show that automated MLPA data processing following our suggested strategy may be of significant use, especially when handling large MLPA data sets, when samples are of different quality, or interpretation of MLPA electropherograms is too complex. It remains, however, important to recognize that automated MLPA data processing may only be successful when a dedicated experimental setup is also considered.