A non-parametric Bayesian factor model is proposed for joint analysis of multi-platform genomics data. The approach is based on factorizing the latent space (feature space) into a shared component and a data-specific component with the dimensionality of these components (spaces) inferred via a beta-Bernoulli process. The proposed approach is demonstrated by jointly analyzing gene expression/copy number variations and gene expression/methylation data for ovarian cancer patients, showing that the proposed model can potentially uncover key drivers related to cancer. Availability and implementation: The source code for this model is written in MATLAB and has been made publicly available at https://sites.google.com/site/jointgenomics/ Contact: catherine.ll.zheng@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.