About the interviewer: I am Amir Kayvanjoo, a PhD student in the field of Molecular Biomedicine at LIMES institute, research group of Dr. Mass, University of Bonn. I have done my bachelor in Biology-Animal Sciences in Iran and later my Master in Life Sciences Informatics with the focus on data science and chemoinformatics at the University of Bonn. In a series of articles, I would like to address a topic that many biology students might have heard of; bioinformatics and transiting from traditional biological experiments to computational biology. In this article, I have done an interview with Nico Reusch, who has combined both, bioinformatics and wet-lab experiments, during his studies.
About the interviewee: Nico Reusch is a PhD student at LIMES institute in the research group of Prof. Schultze with a project that is focused on Bioinformatics and Immunoregulation. He has started his academic career in 2012 at the Saarland University and the University of Strasbourg in a binational Bachelor in Human and Molecular Biology. During his Bachelor thesis, he investigated the link between autophagy and the autoimmune disease systemic lupus erythematosus. He graduated in 2015 and later decided to enroll for the Master’s Degree of Immunobiology at the University of Bonn in 2016. For the past two years, he has focused on the field of immunoregulation, bioinformatics, and genomics and he managed to become specialized in doing single-cell RNA sequencing experiments in addition to the analysis of big data. In his current PhD project, he is focusing on the elucidation of intercellular communication in perturbed blood.
1. You have recently started your PhD with a major difference from your focus during your studies. How did you get familiar with bioinformatics?
My first time getting into contact with bioinformatics was already during my Bachelor studies. During a two-weeks practical course I got familiar with basic commands in Python. However, it took me another three years before I dived into the world of bioinformatics again.
2. So, did you have any idea about it when you started your bachelor? Or could you imagine you would end up in the field one day?
When I started my Bachelor, I expected my future job to be only based on working in a laboratory. However, the availability of high-throughput technologies in all biological fields brings along the necessity to use computational approaches to unravel complex biological systems. I thus soon became interested in getting into computational analysis.
3. What difficulties did you face during your transition from wet lab to dry lab?
The transition from pure wet lab work to computational analysis is more difficult than one would expect. First of all, it is a different kind of time management. Instead of planning an experiment from morning to late afternoon, you need to set yourself some aims that you want to accomplish during one day. Often, these aims need to be postponed when the solution is not as straight-forward as initially thought. Also, one needs a lot of coffee in the beginning to fight the tiredness caused by the immobility in front of the screen. 😉
4. Do you think it is easier for a biologist to learn informatics or vice versa? What is your point of view?
I think this question is very project specific. If you want to have a holistic understanding of a certain biological question and analyze it from a systems biological point of view, I would consider a background in biology more useful than pure informatics. The complexity of biological and chemical pathways that is taught in five years of studies is hard to catch up on, whereas understanding one out of many programming languages and applying a pre-formulated pipeline can easily be learned in little time. On the other hand, if you want to set up new analytical pipelines, a strong background in informatics may be pivotal as you have a better understanding of statistics and writing code.
5. Some people these days say that there is going to be no future for people who are focused on wet lab. What is your opinion about it?
Who am I to judge, as I have just started my career in the field?! I can only speak from my experience and certainly people with good practical skills will always be needed. Without experienced biologists and chemists designing new high-throughput technologies there would not be any ‘big data’ to analyze. Also, most results from computational analysis will still need to be confirmed in the wet-lab. What holds true to what you say is that the data that is generated in everyday laboratory experiments becomes more and more extensive. To analyze these data, it is more and more useful to be able to perform basic computational analysis, for instance to generate dimensionality reductions of flow-cytometry data or to analyze your own sequencing data.
6. How do you like bioinformatics in comparison to regular lab work? Can you give us some pros and cons?
I really like the world of bioinformatics as it paves the way towards a systematic understanding of biological pathways. As you usually analyze data for multiple projects in parallel you learn a lot about different projects from deciphering developmental trajectories in animal models to the elucidation of human diseases. However, one may get lost in the vast amount of different packages and comparisons to use. Often it is difficult to find a stopping point where an analysis is “finished”. Physically speaking, sitting in front of the screen all day may be very tiring, whereas lab work is usually more active and diverse.
7. What are your recommendations for people who are considering a shift to bioinformatics?
In general, I would recommend to first start with some tutorials back home. There are many free courses out there that nicely teach the basics of programming in R or Python. Moreover, I would recommend a lab rotation in a bioinformation group to get first insights into the field. Only after analyzing some data by yourself you will know if you really like working on a computer all day.
8. Any last words for our readers?
I would recommend everyone to try to get your hands on basic tutorials in bioinformatics and you will see that it is actually much easier than you think and even with the basics you can analyze much of your data a lot nicer than with most commercial visualization tools.