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Computational analyses of high-throughput spatial proteomics data

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In biology, localisation is function. Knowledge of the localisation of proteins within the cell is of paramount importance to assess and study their function. Generation of high quality data is an essential and challenging task, and multiple research groups have described various efforts and technical procedures to obtain such data. However, data analysis is as critical as data production for insightful biological interpretation, and no consistent and robust solutions have been offered to the community so far. I will first present classical state-of-the-art data analysis methodologies, centred around supervised machine learning approaches, describe their inherent problems and recent breakthroughs that permit new insights into the data. The second part will compare sequence- and experiment-based approaches, and illustrate how these complementary data sources can be utilised to improve mining of the data. Finally, I will highlight some avenues on how computational understanding of the data can be used to optimise the experimental design and thus improve our understanding of the underlying biology.

This talk is part of the Computational and Systems Biology series.

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