The steady-state localisation of proteins provides vital insight into their function. These localisations are context speci c with proteins translocating between di erent subcellular niches upon perturbation of the subcellular environment. Di erential localisation, that is a change in the steady-state subcellular location of a protein, provides a step towards mechanistic in- sight of subcellular protein dynamics. High-accuracy high-throughput mass spectrometry-based methods now exist to map the steady-state localisation and re-localisation of proteins. Here, we describe a principled Bayesian approach, BANDLE, that uses these data to compute the probability that a protein di erentially localises upon cellular perturbation. Extensive simula- tion studies demonstrate that BANDLE reduces the number of both type I and type II errors compared to existing approaches. Application of BANDLE to several datasets recovers well- studied translocations. In an application to cytomegalovirus infection, we obtain insights into the rewiring of the host proteome. Integration of other high-throughput datasets allows us to provide the functional context of these data.