2023-12-14 2m read
It still feels a bit unreal to write this but here it comes. After almost 6 incredible years at the Institute for Systems Biology in Seattle I will join the faculty of the Medical University of Graz early 2024 as an Assistant Professor in Computational Microbiome Science. My lab will be part of The Diagnostic and Research Institute of Hygiene, Microbiology and Environmental Medicine and will operate within the Austrian Cluster of Excellence “Microbiomes Drive Planetary Health”.
2017-09-03 13m read
In those days analyzing microbiome data often means dealing with read counts from high-throughput sequencing. Usually, we stratify those counts by some entity of interest: phyla, species, genes, sequence variants and so on. Three questions that may pop up when analyzing those read counts are the following: Does the sample contain enough reads to observe the majority of phyla, species, genes that are actually in the sample? How well do the reads represent their distribution in the sample?
2016-06-01 4m read
Once upon a time, I was a young Ph.D. student and was just collecting my first systematic teaching experiences giving short courses in Bioinformatics and Systems Biology for Biophysics students. I somehow got stuck with teaching a session on “Databases for Systems Biology and Bioinformatics”, a topic that did not really create a lot of enthusiasm for me, but I still decided to make the best of it. I had this great little story where you would accompany a young scientists on her way to the discovery of her lifetime by series of small steps, each of them using a particular database to come closer to understanding her results.
2015-11-27 2m read
I recently prepared a small Docker training for our research group in order to highlight our new CobraPy Docker container. I think that Docker is a pretty great technology for Computational and Systems Biology. Most researchers in those areas do a lot of programming, but are actually neither trained nor paid for the software development. Due to this, scientists normally do not suffer from the NIH syndrome (not related to the funding agency, see here), but rather combine a lot of different programs and programming languages into a single pipeline to achieve their goals.