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Machine learning approaches have become an established part of the mass spectrometry-based proteomics field in recent years. Several tools capable of predicting different aspects of peptide behavior have been developed and incorporated in data analysis workflows. These tools have proven to be beneficial in peptide and protein identification in proteomics experiments, and it is therefore […]

It has been a while since our official PROTrEIN launch. We’ve had monthly ESR meetings during this time, where the majority of us have been able to discuss more details about our projects and present them to the group. Several ESRs are working on developing new tools and data analysis pipelines to improve cross linking […]

The moment to begin exercising control over the rules and regulations that will bind us tomorrow is soon. The time to begin thinking and talking about them is now. In this month’s Journal Club, we have decided to step away from technical topics such the latest advances in proteomics methodologies. Instead, we will approach a more “philosophic” […]

Short description In the study of post-translational modifications from mass spectrometry experiments, proteins are almost always represented as a linear string of amino acids. However, it should be kept in mind that proteins’ function is conferred by their 3-dimensional structure and that understanding the modifications of proteins calls for studying them in their structural context. […]

The article Performance of machine learning classification models of autism using resting-state fMRI is contingent on sample heterogeneity (Maya A. Reiter, Afrooz Jahedi, A. R. Jac Fredo, Inna Fishman, Barbara Bailey, Ralph-Axel Müller, Springer Nature 2020) was chosen because it shows potential applications of machine learning in fMRI data analysis. Unsupervised machine learning has been […]

This month’s PROTrEIN journal club covers an article presenting a tool – pDeep2 – capable of predicting MS/MS spectra of modified peptides.1 pDeep2 is built using a machine learning technique that makes it possible to generate prediction even when there are only a few datasets available for training the model. But… What is this machine learning […]

Proteomics can be simply defined as the large-scale analysis of proteins – all the meaningful genome products – in a given sample [1,2]. Mass spectrometry (MS)-based proteomics is one of the most powerful approaches for identifying proteins from biological samples. Over years, there were many proteomics workflows generated by scientists in this field. The development […]