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Views of a pet bioinformatician

My research group has three bioinformaticians, but the team is mostly wet lab focused. So I consider myself and my colleagues as pet bioinformaticians. I could also be considered a clinical bioinformatician, and I want to share my (little) experience so far:


My lab is mostly wet lab but my boss has several bioinformaticians, despite not understanding much the mathematical/technical aspect of what we do. She is mostly interested in which genes are up-regulated, or which microorganisms are involved in the disease (inflammatory bowel disease). It is often difficult to explain my concerns with using some methods. For instance, she wants to know which microorganisms are related to which genes from the mucosal intestine. She frequently asks me for the correlations between them, without considering that this way we might have lots of noise from different sources like the diet that different patients follow. At the same time I also need to make is easy to understand the more complex methods like the models accounting for these factors.

Our lab is in a research center focused on translational research. That means that each lab is connected to a group of doctors of the hospital nearby (less than 100m). We often have meetings with the doctors to learn how are they trying to cure the disease, and what are they doing. For me it is usually difficult to understand, they focus on the number of samples on the follow-up of patients, or the effect of a new treatment on a cohort.
In this meetings we also present what we are doing, what research we are doing. I had to present a couple of times already, and I realized how different is what we do.


When you communicate you must build trust with whoever is listening. I tend to explain what, why and how but people usually want to know directly the conclusion. But explaining how you can build trust on the results. Usually explaining why you are doing each change on the analysis or choosing one tool over another is more important than explaining what are you doing, but at the end you must also provide some results.

Side projects

As a bioinformatician I set up my own experiments, I decide which tools should I use, or spend the time I need to be sure if I want to use them.
Make it easy to yourself to reproduce figures, (I use them in the informal meetings I have with my PI every twice weeks) and the experiments. This might involve writing a package for the analysis and saving all the "tests" which end up being the experiments used to decide the direction of further research.

Make it easy to build reports/redo the same analysis with a slightly variation. I've read that programmers bring value in companies by maintaining/building the source of the income. Here too, by maintaining/building methods to analyse the data you keep your lab on. Make a stand-alone program for the process you had to repeat for the third time, or that you see that it can come useful later.

Look for other's code. What new tools and methods have been developed or are being developed? How are people using existing tools?  Could any of these be used in your project or in future projects? You might need to use know how to find new tutorials or which research groups you should look for when the lab decides to give it a try to a new technique.

The lab mates

Understand other's projects, what are they looking for. Why are they looking or doing this experiment?

You don't need to design or add your value in everything they do. But you might need to help them when they are going to sequence their samples, or when they want to compare several measurements. Somehow you might be the person they ask to know which method they should apply or how they can represent some values or difference or simply how they can show better their history of the paper..

Also understand their struggles, you might be happy letting a computer run over night but they might need to come on Saturdays and Sundays to make sure their experiments are OK. Or they need to stay late because the sample of the hospital came late and they need it to process it the same day. Be sympathetic and help whenever you can.

Learn about the data

It is easy to forget where does the data come, but before being a big matrix or a big .fastq there were a lot of process. You need to learn what was going on. Why were those samples collected? What was the process before the data arrived to your computer?
In my case I have to know if the cirugy on patients is random, or not, or if those missing biopsies are due to some reason or there are several and cannot be foreseen. Knowing the origin of your data provides insight about how to analyse it.

Read the literature about the problems/how to analyse the data: is it composite, which methods are there to normalize, how should you do  the quality control?

Be helpful

Be sincere if you do a mistake, own it. Try to help people and do your best and nobody can ask you more. I hope that if you are in a similar position you find a good team and position to learn, grow and be happy.