Proteomics: A Peptide’s Journey to Emergence
Dr. Maarten Dhaenens
Head of Proteomics Dept, Lab of Pharmaceutical Biotechnology
If we have learned anything from the dozens of collaborations at our lab, it is that the term Proteomics is actually very confusing to the outside world. Indeed, we measure peptides but what we actually report on, the proteins, is merely inferred. In a time where productivity is key, automating this inference as much as possible has become a goal in its own right. Yet, only human intervention can assure that the most correct or least ambiguous outcome is reported. Thus, here we will argue that proteomics is an “emergent” – not “emerging” – field. And, that facilitating human inspection of the visualized data is required to fill the gap between what is measured and what can be concluded in terms of potential biomarkers or biology. To illustrate this point, we will look at histones, five complexly modified low molecular weight proteins that are often used to normalize entire proteomes.
Studying histone modifications is an intrinsically peptide-centric approach. This is how we got to realize that inferring protein abundance is extremely hard and in some cases impossible. In this webinar we will follow a few of those peptides on their journey through the Progenesis QI for proteomics workflow including peak reviewing, QC metrics, conflict resolving and spectral library matching. Illustrating that no automation process to date is able to anticipate the complexity of protein abundance. In short, the final list of potential biomarkers should always be manually inspected and visualized in order to save time and money in the downstream validation process.
During this webinar attendees will:
• Learn how visualization of the data is essential to obtain intuitive insight into what was measured by the mass spectrometer and what can and cannot be translated into biology.
• Understand how all the different levels of complexity in proteomics data can be unraveled by the different functionalities of Progenesis QI.
• Realize how histone proteins surpass the standard level of complexity of general proteomics approaches and thus warrant the development of a dedicated workflow when being studied.
• Hear how Progenesis QI for proteomics is a powerful tool for resolving the many challenges in automated data analysis and protein inference.
• Learn why proteomics researchers that typically come up with a final shortlist of potential biomarkers, should subject their candidates to this workflow, in order to assure the validity of their candidates for future validation.
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