This project is in the context of Structural Biology, where Distance Geometry (DG) has been proved to be a valid tool for the analysis and the determination of biological structures, such as proteins. The classical application of DG arises in the framework of Nuclear Magnetic Resonance (NMR) experiments, where distances between atom pairs are estimated by the experimental technique, and suitable three-dimensional conformations of the corresponding molecule need to be identified. This problem is NP-hard and was historically tackled via the use of heuristic and meta-heuristic methods; since some years, several partners of the present project are working on a discretization approach for DG which allows to employ a branch-and-prune (BP) algorithm for the identification of three-dimensional conformations. One strong point of this discretization approach is that the DG solution set can be potentially exhaustively enumerated, providing in this way all possible three-dimensional protein conformations fitting with the experimental data. The main idea in this project is to enhance the robustness of such an approach for efficiently dealing with uncertain data, and to extend its domain of applicability to genomics data and Hi-C data.