November 7, 2017
Meet Sarah Carl and Takashi Miki
"One of my fav wet lab-dry lab collabs so far." This is how Sarah Carl shared the news on her recent paper in Genes & Development on Twitter. In a very fruitful collaboration, she and Takashi Miki found that there are two distinct transcription termination modes and that the promoter of a gene determines the mechanism for termination of its transcription. We wanted to learn from the two young scientists why this collaboration worked so well.
Q: Why was this collaboration so fruitful and pleasant?
Sarah: Essentially, we were both highly interested and invested in the project, which led to a really nice ongoing dialogue. From my perspective, I think it helped a lot that Takashi was very open to new and unexpected results from the computational analysis, and I felt that he and Helge both valued my scientific as well as technical contributions.
Takashi: Yes, we were both excited about our observations and shared the passion for understanding the underlying mechanisms.
Q: How did you set it up? What did you put in place that you were able to work together nicely?
Takashi: This project started from RNA-seq data indicating that there existed two distinct transcription termination modes. Due to the lack of a global quantification method, I had to examine individual genes one by one and felt I was not getting the big picture. Development of an analysis method for such a particular phenomenon requires a long-standing commitment of a computational biologist, for which I couldn’t ask the Computational Biology platform. But Sarah, who joined the group as a computational biologist at that time, expressed a strong interest and right away gave me some ideas about how to analyze the data.
Sarah: We didn't set up any formal structure, but we ended up with a nicely productive cycle where we would discuss the results of each analysis together and use our insights to inform the next round of experiments. Even when things went wrong and experiments didn't work, I think it was useful to have multiple perspectives to come up with a solution.
Takashi: Yes, whenever we got new data, we discussed the reasonable interpretations and hypotheses in a mutually critical manner and designed the next experiments.
Q: What are the challenges that wet-lab and computer biologists face when they work together?
Sarah: I think communication can be a challenge, as well as setting clear expectations. Sometimes as a computational biologist, I feel like I don't get the whole story from the beginning of a project, probably because the wet-lab biologist thinks it isn't important for me to know all the details - but that can make it hard for me to know what the most useful analyses would be!
Takashi: I sometimes see a gap in what computer biologists expect for the quality of data and what we wet-lab biologists can get within a reasonable amount of time. In these situations, it can be difficult to reach a middle ground. However, this was not the case in our collaboration, probably because Sarah had wet-lab experience and knew what was realistic and unrealistic.
Q: What are your recommendations based on your experience?
Takashi: This may not be limited to this type of collaboration, but I think that a strong interest in the project from both sides, frequent discussions, being critical with each other and most importantly mutual respect, make the collaboration powerful and comfortable, and steer the project in the right direction.
Sarah: For a computational biologist and wet-lab biologist starting to work together, I would say to discuss the evolution of the project frequently and as openly as possible. That way each person will understand better what the other is doing, so you are less likely to have misunderstandings and more likely to come up with interesting and new insights. In the end, it is a scientific collaboration, so you should treat each other as fellow scientists, each with a unique perspective.
Q: What’s next for you? And has this shared good experience an influence on what you are doing next?
Takashi: Regarding this project, our findings open many interesting questions, particularly about the mechanistic aspects of the two different transcription termination modes, and I would like to address those. This will surely require collaboration with a computational biologist, and this good experience makes me optimistic about it.
Sarah: Currently, I have several more ongoing projects with both the Grosshans and Bühler labs, and I am trying to approach them in a similar way. In the long run, I'm not yet sure what I'll be doing! But I'm pretty sure it will involve collaborations, and this experience has helped me develop a good framework that I can use in many different situations in the future.