cghRA : a flexible workflow for CGH array analysis
Résumé
Although Next Generation Sequencing technologies are becoming the new reference in whole genome analysis, there is still a need for more affordable methods like CGH arrays to compare genomic alterations in large sample series. Despite this need and the large collection of freely available un-interfaced algorithms and commercial software, biologists unfamiliar with command line interfaces and scripting lack a simple and efficient tool to handle such data.
Here, we provide free cross-platform software, combining a user-friendly interface for pure biologists and an object-oriented command line interface for more advanced users. Its open-source R implementation offers a native interface to most of the published algorithms in the field, and a sharp learning curve to users familiar with this widespread scripting language. Aside from well-established algorithms, it includes original algorithms for copy number calling, recurring event definition and polymorphism filtering in a somatic context.
The performances of these algorithms have been assessed in a series of 108 CGH arrays of Diffuse Large B-Cell Lymphoma (DLBCL), for which conventional caryotyping and quantitative PCR had also been performed. Copy number estimations made by cghRA proved to be more accurate than concurrent algorithms (GLAD, CGHcall), as compared to wet-lab validation. Recurring events highlighted by cghRA matched and extended the current knowledge of DLBCL genomics, while offering more flexibility than concurrent software (GISTIC, SRA). Finally, polymorphism annotation sensitivity was assessed in a public dataset of control samples, and specificity in a subset of arrays hybridized against matching normal DNA rather than a DNA pool.