Categoría: Journal Club

Proteomics research relies heavily on mass spectrometry, which has emerged as the most prominent and widely utilized approach for the identification of proteins. However, progress is being made in other analytical methods for peptides and proteins. In this month PROTrEIN ITN’s Journal Club, we are taking a glance at the characterization of proteins with nanopores […]

The fields of science and technology have been the engines of progress for many years, driving innovation and shaping the world we live in. Contributing to this scientific knowledge and progress is one of the main aspirations most of us researchers have. However, there is a growing number of studies suggesting that scientific progress is […]

Data-independent acquisition (DIA) proteomics increasingly becomes the method of choice for researchers since it provides better reproducibility, identification rates, and accuracy compared to data-dependent acquisition (DDA). More and more tools are developed for DIA analysis and even established proteomics data processing software now can analyze DIA data. However, analysis of multiplexed spectra, characteristic of DIA, […]

Life as we know it has been shaped by the constraints of the environment. The aerodynamic shape of leaves to prevent the tree from toppling at high winds, or gravity that affects heights of organisms (extraterrestrial humanoids on the fictional pandora in Avatar), or the color of a polar bear. The environment is a critical […]

In the last Journal club, we present a paper by Yilmaz et al. called «De novo mass spectrometry peptide sequencing with a transformer model» [1] introducing a deep learning model for de novo peptide sequencing. What? You do not know exactly what is de novo peptide sequencing? Let me explain it. Imagine that you do […]

In our last Journal Club blog post, we presented ProteomicsML a web platform with tutorials for machine learning in the field of proteomics. Today, we stay on the spot with machine learning, however, on this occasion, we are presenting a new approach and model from Tom Altenburg et al. based on their article “Ad hoc learning […]

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 […]

In this Journal Club, our ESRs Zahra and Arslan present the article «Optimization of Search Engines and Postprocessing Approaches to Maximize Peptide and Protein Identification for High-Resolution Mass Data» by C. Tu et al. from Sep 2015

In this Journal Club, our ESRs Louise and Shamil present the article «Strategies to enable large-scale proteomics for reproducible research» by R. Poulos et al. from July 2020