Probabilistic modelling of protein sub-cellular localisation

In biology, localisation is function - understanding the sub-cellular localisation of proteins is paramount to comprehend the context of their functions. Mass spectrometry-based spatial proteomics and contemporary machine learning enable to build proteome-wide spatial maps, informing us on the location of thousands of proteins. Nevertheless, while some proteins can be found in a single location within a cell, up to half of proteins may reside in multiple locations, can dynamically re-localise, or reside within an unknown functional compartment, leading to considerable uncertainty in associating a protein to their sub-cellular location. Recent advances enable us to probabilistically model protein localisation as well as quantify the uncertainty in the location assignments, thus leading to better and more trustworthy biological interpretation of the data.