The group lasso is an extension of the popular lasso regression method which allows to select predefined groups of features jointly in the context of regression or supervised classification. I will discuss two extensions of the group lasso, motivated by applications in genomic data analysis. First, I will present a new fast method for multiple change-point detection in multidimensional signals, which boils down to a group Lasso regression problem and allows to detect frequent breakpoint location in DNA copy number profiles with millions of probes. Second, I will discuss the latent group lasso, an extension of the group lasso when groups can overlap, which enjoys interesting consistency properties and can be helpful for structured feature selection in high-dimensional gene expression data analysis for cancer prognosis. (Joint work with Kevin Bleakley, Guillaume Obozinski and Laurent Jacob)