In modelling many bio applications, it is extremely necessary to jointly model multiple uncertain quantities to present more accurate, near future probabilistic predictions. Informed decision-making about some disease would certainly benefit from such predictions. Bayesian networks (BNs) and copulas are widely used for modelling various uncertain scenarios in bioinformatics. Copulas, in particular, have attracted more interest due to their nice property of approximating the probability distribution of the bio data with non-symmetric and heavy tail such that this attributes frequently observed in genetic data. The standard multivariate copula suffer from serious limitations, which made them unsuitable for modelling gene networks. An alternative copula model called the pair-copula construction (PCC) model is more flexible and efficient for modelling the complex dependence of the data recorded for the gene expression data. The only restriction of PCC model is the challenge of selecting the best model structure such that they cannot produce sparse gene networks. This issue can be tackled by capturing conditional independence using the Bayesian network PCC (BN-PCC). The flexible structure of this model can be derived from conditional independences statements learned from genomic data. Additionally, the difficulty of computing conditional distributions in gene networks for non-Gaussian distributions can be eased using pair-copulas. In this talk, we review the latest developments of using BN-PCC for modelling non-Gaussian genomic data. We illustrate the approach by constructing gene regulatory networks from microarray data, and probabilistic modelling of protein networks.