Hannes Röst and his Plans to Tackle the Diversity Gap in Computational Mass Spectrometry
Medical Genomics
Dr. Hannes Röst. Image by Jovana Drinjakovic.
By
Vanessa Rokoszak
Dr. Hannes Röst is a bioinformatics researcher, an assistant professor of molecular genetics and computer science at the Donnelly Centre for Cellular and Biomolecular Research in UofT’s Temerty Faculty of Medicine, and one of the professors teaching MMG3002Y: Biological Statistics in the M.H.Sc in Medical Genomics Program. His lab is located in the Donnelly Centre, where his team uses next-generation mass spectroscopy to analyze proteomic and metabolomic data to gain insights in molecular biology. His lab has recently taken on a project aiming to develop a program that will increase diversity in computational mass spectrometry.
Dr. Röst completed his Bachelor’s, Master’s and Ph.D. studies at the Swiss Federal School of Technology (ETH) Zurich. While his B.Sc. is in biology, Dr. Röst was interested in switching career paths to computational studies, and completed his M.Sc. in computational biology and bioinformatics. He went on to complete his Ph.D. in Dr. Ruedi Aebersold’s lab, where his project focused on quantitative proteomics; measuring protein expression level using mass spectrometry. Dr. Röst then moved to California, completing his research fellowship at Stanford University in Michael Snyder Lab, where he took on a project using computational methods to apply proteomic data to personalized medicine. Finally, Dr. Röst took his position at the Donnelly Centre at UofT, where his lab uses mass spectrometry to analyze proteomic and metabolomic data to study a variety of diseases.
When asked how he would explain mass spectrometry (MS) to non-specialists, Dr. Röst said that it is used to “weigh the world”, allowing researchers to weigh each molecule by measuring its mass to charge ratio. He goes on to explain that the possibilities of the technology are endless since the technology is not limited to a particular type of molecular class and that “by measuring the weight of molecules, we can understand a lot about their function, their role in the cell, which molecules are present in diseased cell lines compared to healthy ones.” He states that while the concept behind the technique is relatively simple, in high throughput can lead to sophisticated information about what is happening on a molecular level in a cell. Dr. Röst’s lab is currently interested in using next-generation MS to investigate adverse outcomes in pregnancy (including gestational diabetes and growth delays in infants), as well as glioblastomas. In addition, his lab focuses on expanding computational tools (such as software algorithms) used to efficiently analyze large amounts of MS data.
Dr. Röst has recently undertaken a project aiming to tackle the diversity gap in computational mass spectrometry. He explains that MS is used in many areas in both research and society, including toxicology, forensics, environmental analysis, and healthcare. While the applications of MS are varied and affect people across society, the people doing computational work in MS tend to be mostly white men, and Dr. Röst has noticed a significant lack of diversity in the field. His new project hopes to bridge the gap between the people affected by the applications of the technology and the people who work in research and provide data interpretation, as he believes it is deeply problematic that a limited group of people decide what data is relevant.
Dr. Röst explains that it is crucial that everyone has access to the tools and knowledge used in MS data collection and analysis. All of his lab’s tools are open sourced, so that in principle anyone can recreate the group’s analyses and understand how the software works. However, Dr. Röst realizes that this understanding requires a lot of training and knowledge. He is therefore committed to making this knowledge available to a wider range of people through this diversity project.
The project is structured in a hierarchical manner, where the first step aims to remove barriers to understanding MS data by improving online tutorials and public documentation. These improvements will hopefully encourage people to experiment with MS data and gain computational skills. The next step is developing in-person workshops, where researchers at any career level can come in and be taught how to use software and computational tools, which they can apply to their own research endeavors. Next, the project aims to develop internship positions for under-represented groups, where people of diverse backgrounds can work in world class research labs to gain experience and networks to expand their careers in computational biology. Finally, an annual research award will be developed for researchers from underrepresented populations in academia. Representation matters, and the hope is that these award-winning early career researchers can serve as role models to other young people in their community, who might then consider pursuing computational biology themselves.
We wish Dr. Röst many successes in all his research endeavors, and we are thankful to have him as part of the MedGen faculty.
