Tumor Genetics Blog

By: Sinja Taavitsainen

Suomennos: Opettaminen – tutkijat kouluttavat tulevia sukupolvia

Every day, researchers focus their energy on answering key questions in biology, such as how cancer develops and how we can treat it. However, there is another integral part of research – training future researchers. As in every field, it’s necessary to learn the core knowledge and skills in order to become a competent and independent researcher. Enter senior researchers who take on a large part of this teaching and training themselves. Here, we’ve asked Dr. Sinja Taavitsainen to tell us about her experiences in training the next generation in her role as University Instructor.

Tell us a little bit about yourself.

I’m Sinja Taavitsainen, bioinformatics researcher and University Instructor at Tampere University. I initially studied microbiology during my Bachelor’s studies at the University of California at Davis, but I switched to bioinformatics for my Master’s and PhD at Tampere University, so my background is a mix of biology and computing. In my PhD, I focused on trying to understand treatment resistance mechanisms and tumor evolution in prostate cancer using various computational methods. I’ve since done a postdoc at Karolinska Institutet, where I worked on liquid biopsies and circulating tumor DNA dynamics in prostate cancer. Now, I’m really excited to be back at Tampere University in my new role as University Instructor, and this semester I’m teaching Bachelor’s level students, providing them with an overview of the field of bioinformatics in preparation for more advanced studies.

Was it difficult to transition from research to research and teaching?

I had already been participating in teaching alongside my research during my Master’s, PhD, and postdoc years, so the transition didn’t feel like a huge leap. What has changed a bit is the balance of my time, since teaching is now a more central part of my role rather than something I fit in around research. That shift has actually felt very natural, because I’ve always really enjoyed teaching. The main challenge has been the practical side, like preparing new lectures and learning how to organize a whole course, but even that has been fun, because it pushes me to think carefully about what really matters for students to learn at a particular stage in their degrees.

What are some challenges in teaching that you don’t face in research?

In research, you’re often working with colleagues who share at least some of your background and vocabulary, but in teaching you have to remember what it’s like to encounter a concept for the very first time. Breaking down complex ideas without oversimplifying them can definitely be challenging sometimes. Another difference is the immediate responsibility you feel as a teacher: in teaching you see right away in real-time whether students are following or if you need to adjust your approach to explain something in another way. 

Do you think your research influences your teaching or vice versa?

Definitely! My research gives me plenty of real-life examples that I can bring into teaching. For example, when I give lectures about sequencing technologies and their data analysis methods, I can show how they’re actually applied in cancer research. I think this helps students see that bioinformatics isn’t just theory, it’s also a set of tools that can answer important biomedical questions. On the other hand, students ask questions that make me think about familiar problems in a new way. Having to explain methods clearly to beginners forces me to revisit and rethink some of the fundamentals, which is really helpful for growing as a researcher.

Do you notice any differences between what is taught now versus when you were in school?

Bioinformatics has moved forward quite a lot since I was a student. Back then, at least in my view, the focus was very much on mastering individual tools and running analyses step by step. Now, there’s much more emphasis on automation, reproducibility, and coding skills. Things like using workflows, version control, and cloud computing have become standard.

Also, new sequencing technologies like single-cell methods are a bigger part of the field today. So while the foundations are the same, the skills students need to succeed have definitely shifted toward more flexible and computationally robust approaches. And of course, the rise of large language models like ChatGPT has made it so that students can now generate code or explanations for concepts in seconds. That can be a huge help, but it also means we need to teach them how to critically evaluate and understand what these tools produce, rather than just copy-pasting results.

What kinds of students do you teach? Will most of them go the research route?

At the moment, I mostly teach students at the Bachelor’s level in the Biotechnology and Biomedical Engineering program. They have strong biology knowledge, and many are also comfortable with math and some programming. Many of them will indeed decide to go into research, but not necessarily only in academia. Many will also aim at industry positions and one goal of the degree program is to provide students with a broad view of the different career options that are available.

What kind of opportunities would be good to provide for those that are interested in becoming researchers?

Opportunities like summer internships in labs, collaborative projects, or participation in ongoing research give students a real sense of what it’s like to work on a scientific question from start to finish. I was very lucky to get to start as a research assistant in Matti Nykter’s group when I was a bioinformatics student, and it really solidified my interest in research and gave me the skills to actually work as a bioinformatician. Providing students with hands-on experience with real research projects, where they can learn to analyze data, interpret results, and contribute to ongoing studies is really valuable and something I think Tampere University and its research groups do really well.

What do you think are some of the important skills and knowledge that should be passed on to future generations?

I think the technical skills such as coding, statistics, and specialized data analysis are important, but they’re only part of the picture. The real expertise to pass on is how to think about data. That means being critical about its quality, mindful of its limitations, and creative about how to extract meaningful insights.

Another crucial skill is interdisciplinarity. Bioinformatics is a mix of biology, computer science, and math, so the ability to “speak the language” of multiple fields and to collaborate across disciplines is something that will always be relevant. And finally, I think adaptability is also key. Tools, technologies, and research questions evolve rapidly, so being able to learn and adjust continuously is one of the most valuable skills a student can develop.

Do you have any advice for new teachers in this area, especially coming from a research background?

Since I’m just starting out as a teacher myself, it’s maybe a bit early for me to give anyone advice 🙂. However, I have gotten some great advice from other teachers over the years that I think have really improved my own teaching. Through their advice, I’ve for example learned the importance of clearly defining learning objectives, motivating lectures so that students know why they should be interested in a particular topic, breaking complex topics into manageable steps, and being flexible in responding to students’ questions and needs. 

Beyond this, I would encourage anyone interested in going into teaching to take some courses in pedagogy. I didn’t complete very many pedagogical studies during my Master’s and PhD days, but I wish I had! You can learn a lot by getting teaching experience, but studying pedagogy is also a great way to get new ideas and methods for your teaching.

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