ArticleBMC bioinformatics2011
MLPAstats: an R GUI package for the integrated analysis of copy number alterations using MLPA data.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Multiplex Ligation Probe Amplification and Sanger Sequencing: Light and Shade in the Diagnosis of Lysosomal Storage Disorders.Biomedicines · 2025Article
- Comparing nodal versus bony metastatic spread using tumour phylogenies.Scientific reports · 2016Article
- Systematic screening for PRKAR1A gene rearrangement in Carney complex: identification and functional characterization of a new in-frame deletion.European journal of endocrinology · 2014Article
- Use of the MLPA assay in the molecular diagnosis of gene copy number alterations in human genetic diseases.International journal of molecular sciences · 2012Review
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Authors and funding
4 authors.
Funding
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Abstract
backgroundMultiplex-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.
resultsWe 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.
conclusionsMLPAstats 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.
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Registered trials
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