Metaproteomics is an increasingly popular field involving the study of the full protein complement of entire (microbial) communities. These proteins are the ‘worker’ molecules in cells, carrying out the chemical reactions that sustain life, and that ensure communication with other cells in the community and with the host organism. Metaproteomics is frequently applied to gain insight into human gut microbiota, the microbial communities that live on and around plant roots, and the fermenting microbes in biogas reactors. Yet, despite its popularity, the ability of metaproteomics to provide detailed insight in the actual workings of the investigated communities is held back by a lack of computational tools to analyse and interpret the acquired data. This project therefore directly addresses this issue, by developing two crucial computational components. The first is a performant identification algorithm that translates the acquired data into protein identifications, and the second is a dedicated quantification algorithm that will provide the relative amounts of
proteins present across different samples (for instance, healthy compared to ill people, growing compared to dying crops, and performant compared to stalled biogas reactors). I will also implement these new algorithms in easy-to-use, free and open source software so that as many people as possible will be able to make use of our tools to analyse their own as well as other people’s data (which are available online).