People working in a collaboration space at the Crick

Science date: an experimentalist meets a computational biologist

  • Date created: 9 April 2019
  • News Type:
  • Feature

About the Data Challenge

The Data Challenge is the brainchild of three Crick researchers: Aylin Cakiroglu, Nikita Desai and Martin Jones. It brought experimental and computational researchers together from across the institute with experts from our Bioinformatics and Biostatistics and Scientific Computing STPs in a hackathon-style event. 

Connecting people from different disciplines is at the heart of the Crick's mission, but even in such an open place it can be difficult for researchers to find colleagues with complementary skills. 

With this in mind, we recently held the Crick’s first Data Challenge, an opportunity for experimental and computational researchers to get together and take projects in a new direction.

So what happens when ‘wet lab’ scientists meet their ‘dry lab’ counterparts? What can they learn from each other, and do scientific sparks fly?

Setting the scene

Lorea Blazquez Garcia is a postdoc in the RNA Regulatory Networks Lab working on how RNA splicing is regulated by RNA binding proteins. It's thought that up to a quarter of genetic mutations modify RNA splicing. 

Lorea is interested in developing therapeutic strategies for these genetic mutations by working out which RNA sequences to target to restore correct RNA splicing patterns.

To do this effectively she needs to analyse existing experimental data – in this case where RNA binding proteins interact with the RNA – to make predictions which she can then test in the lab. 

Anna Laddach is a postdoc in the Development and Homeostasis of the Nervous System Laboratory. A bioinformatician, Anna analyses the single-cell sequencing data generated by her lab and has a background in handling large data sets. 

Q&A

Lorea Blazquez Garcia

Lorea Blazquez Garcia

 

Tell us a bit about your (scientific) self

I’m purely an experimentalist, carrying out experiments in the lab and then analysing the data. The analysis I wanted to do was for hundreds of high-throughput RNA-sequencing experiments generated in my lab or published elsewhere, so I had to find computational partners for this.


Why did you get involved in the Data Challenge?

Before the challenge I knew what to do to analyse all the data to help us better design therapeutic drugs. But I simply didn’t know how to do it – I needed expert help with the computational heavy lifting.  

I was very lucky to have Charlotte Capitanchik involved in the project from the outset. She understands the biology and also the computational side. But without the Data Challenge it would have have taken us months to do meaningful work.


What were you hoping for?

I was hoping for what happened! To find a very talented group of computational people to help me with my research question. 

We got a prototype modelling pipeline together in two days. So we now have a program where I can tell it the outcome I’d like, run it and it will say “if you want to do that, based on all the experimental data, you should target that [sequence]”. 

Then I can go to the lab and do the experiments and see if the program is right or not. And in the longer term, we’ll be able to refine the model using validated experimental data.


What did you bring to the table?

Knowing what I wanted to achieve. To be honest, during the challenge itself I saw the team typing away … and I couldn’t really help. But there were of course points where my biology expertise was necessary too.


Were there any difficulties or challenges?

I have no idea about the coding or computational side of things, so it could have been difficult to communicate. So I was really glad that Charlotte was there, as she could act as the ‘translator’, as well as helping to design the program and coding. We wouldn’t have progressed so much in the two days without her.


Best thing about the experience?

To see that at the end of the two days that the prototype was working. And it was great to meet so many new people from other labs and hear their ideas. I learned about a different way of thinking about things.


What was the most surprising thing?

Maybe not a surprise … but it confirmed to me the amazing things that you can do if you put the right people together, particularly from different disciplines. 


Do you think you’ll work together in the future?

Definitely. We’re moving forward with the project and hoping to refine it.  And now I know Anna and the skills she has, I may turn to her in the future for other things.

Anna Laddach

Anna Laddach

 

Tell us a bit about your (scientific) self

I'm a postdoc bioinformatician. My group studies the enteric nervous system which is an entirely new, but fascinating, field for me - I’m slowly soaking up all the biology associated with it!


Why did you get involved in the Data Challenge?

It’s nice to have an opportunity to collaborate with other scientists who enjoy analysing data. It’s also interesting to learn about the biological problems and to find out about the expertise available here. I’d never worked with this specific type of data before either, so I was able to learn something new.

I also really liked that this project had a clear goal and a specific desired outcome, which made it feasible in the small amount of time we had.

 

What were you hoping for?

I am the only purely computational person in my group so I was hoping to meet people in a similar field to discuss ideas and troubleshoot problems with.

I was also hoping to learn something new through the challenge; collaborating with new people provides an opportunity to see different ways to work and to be able to learn from people with different areas of expertise.

 
What did you bring to the table?

I was involved a lot in the design of the code – how to go from Lorea’s idea to producing a useful prototype and the step by step implementation of this. And then, along with the other team members, I wrote a lot of the code to make that happen. 


Any difficulties or challenges?

At the very beginning we had technical problems with getting everyone access to folders etc. And things went slower on the first day because it took a while to work out where everyone’s skills lay and the role they could play in the project. 


Best thing about the experience?

We often work on our own, computationally, so having lots of people in the same room is really fun – all sitting in the room typing away and working towards a common goal. It was great to meet all the people and make these connections. 


What was the most surprising thing?

I was a bit apprehensive about how much could be done in two days. I was surprised by how smoothly it went – other than a few hiccups. 


Do you think you’ll work together in the future?

Yes, I am really looking forward to refining our prototype and, hopefully, making it into something which will speed up research for the wet lab biologists.

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