Computational RNA Biology
My research program spans several connected areas of computational RNA biology, linked by a common focus on how RNA structure, dynamics, evolution, and function relate.
What connects these areas is the observation that many RNA questions are no longer well served by sequence analysis alone. Structural inference, comparative evidence, chemical probing, molecular modeling, and selected machine-learning methods each capture part of the problem. My work is concerned with how these lines of evidence can be used in ways that remain scientifically defensible.
RNA structure prediction
RNA structure prediction has been a central theme throughout my work. I am interested in both secondary and tertiary structure, especially in settings where purely sequence-based inference reaches its limits. This includes the use of chemical probing data such as SHAPE, SHAPE-MaP, and DMS-based workflows, as well as comparative evidence in RNA families where structural conservation is clearer than primary-sequence conservation.
RNA folding kinetics
Folding kinetics is another long-standing focus. Many regulatory RNAs and many designed RNAs cannot be understood from an equilibrium structure alone. They depend on how folding proceeds in time, which alternatives remain accessible, and which metastable states persist long enough to matter. This is particularly relevant in co-transcriptional folding, ligand-controlled systems, and synthetic constructs whose behavior depends on local alternatives rather than a single dominant fold.
Functional RNA design
These questions lead naturally into functional RNA design. I am interested in design problems where structure has to be engineered with a specific use in mind, whether that means preserving accessibility, controlling kinetic behavior, or deciding which sequence constraints actually matter before experimental work begins.
Viral RNA biology
Comparative RNA virology has become a major application area. Much of this work deals with conserved structured elements in flaviviral and related genomes, including xrRNAs, untranslated regions, and long-range interactions. These systems are scientifically interesting in their own right, but they are also useful because they expose cases where structural constraints remain visible even when raw sequence similarity becomes weak.
RNA–protein recognition
I also work on RNA–protein interactions and structure-guided modeling in systems where RNA recognition cannot be reduced to motif matching alone. This includes molecular modeling and simulation approaches that help connect predicted structures with plausible interaction geometries.
Computational methods & reproducibility
Selected projects use machine learning where it provides a clear technical advantage, for example in kinetic approximation or in well-scoped inference problems. In those cases my interest is less in generic claims than in whether a method actually improves what can be said about a specific RNA system. Alongside this sits reproducible, open scientific software so that RNA analyses can be run, audited, and reused at scale.
Independent research & collaboration
The publication list gives the formal record of this work. Many of the papers also have a blog post that explains the methods and the biological context in more depth.
I pursue independently led research through RNA Forecast, the platform for my software development and collaborations. Current independently led projects, research themes, and RNA Forecast-affiliated publications are maintained there.
Much of this work is computational and I carry it out independently. Where a question needs experiments, I work with partners in RNA bioinformatics, structural biology, virology, and synthetic biology, above all the Theoretical Biochemistry Group (TBI) at the University of Vienna, with whom I have collaborated for many years. Research collaborations are listed on the collaborations page, and current supervision on the people and supervision page.
Current independently led projects, research themes, and affiliated publications are maintained on RNA Forecast.