Monitoring of changes at the DNA level enables the characterization of the underlying structure of genetic diseases. In particular, copy number alterations (CNAs) are increasingly being recognized as an important component of genetic variations in cancer: oncogenes may be enhanced by DNA amplification and tumor suppressor genes may be inactivated by physical deletion. Encouraged by the advent of array comparative genomic hybridization technology, several biological studies have been designed to look for chromosomal aberrations involved in cancer. Hence, the development of algorithms aimed at the identification of CNAs is a current challenge in bioinformatics. Despite the amount of proposed approaches, identification of CNAs is yet an open problem. Here we propose a new approach for
detection of CNAs that extends a previously published algorithm where a popular image segmentation variational model was used. The proposed algorithm, called Vega Multi-Channel (VegaMC), starts from the assumption that copy number profiles are piecewise constant and finds the optimal segmentation by minimizing a functional energy that represents a compromise between accuracy and parsimony of
the boundaries. We applied VegaMC on a published gastrointestinal stromal tumor aCGH dataset, showing the ability of the proposed approach in the identification of
well-known cytogenetic mutations, and eventually discover new ones