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.
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.
Authors: Jakob Albrethsen; Jaco C Knol; Sander R Piersma; Thang V Pham; Meike de Wit; Sandra Mongera; Beatriz Carvalho; Henk M W Verheul; Remond J A Fijneman; Gerrit A Meijer; Connie R Jimenez Journal: Mol Cell Proteomics Date: 2010-01-20 Impact factor: 5.911
Authors: Anoek H J Verschuur-Maes; Cathy B Moelans; Peter C de Bruin; Paul J van Diest Journal: Cell Oncol (Dordr) Date: 2014-04-02 Impact factor: 6.730
Authors: Ronald van Eijk; Paul H C Eilers; Remco Natté; Anne-Marie Cleton-Jansen; Hans Morreau; Tom van Wezel; Jan Oosting Journal: BMC Bioinformatics Date: 2010-01-29 Impact factor: 3.169
Authors: Cathy B Moelans; Hanneke N Monsuur; Johannes H de Pinth; Remco D Radersma; Roel A de Weger; Paul J van Diest Journal: Cell Oncol (Dordr) Date: 2011-05-04 Impact factor: 6.730
Authors: Cathy B Moelans; Roel A de Wegers; Hanneke N Monsuurs; Anoek H J Maess; Paul J van Diest Journal: Cell Oncol (Dordr) Date: 2011-05-06 Impact factor: 6.730
Authors: Tineke E Buffart; Nicole C T van Grieken; Marianne Tijssen; Jordy Coffa; Bauke Ylstra; Heike I Grabsch; Cornelis J H van de Velde; Beatriz Carvalho; Gerrit A Meijer Journal: Virchows Arch Date: 2009-08-21 Impact factor: 4.064