Post provided by Daniel Edler
Each year Methods in Ecology and Evolution awards the Robert May Prize to the best paper published in the journal by an author at the start of their career. Ten Early Career Researchers made the shortlist for this year’s prize, including Daniel Edler who is a PhD student at Umeå University in Sweden. In this interview, Daniel shares insights on his paper ‘raxmlGUI2.0: a graphical interface and toolkit for phylogenetic analyses using RAxML’.
Tell us about your career stage, research, your hobbies and interests
I’m currently in my last year as a PhD student at the Integrated Science lab (IceLab) in Umeå University, Sweden. I develop methods and applications to efficiently map and explore complex systems, using ideas from information theory and network science. Although my background is in physics and computational science, biodiversity informatics has been my main application area, supported by the Antonelli Lab and Gothenburg Global Biodiversity Centre. Other than science and being with my family, I’m interested in philosophy, playing the piano, and working in our new small-scale apple orchard with a focus on making juice.
How would you pitch your article to someone if you had just 30 seconds in an elevator?
A fundamental scientific question in biology and medicine is to understand how species or genes are related to each other. There are powerful methods that can take DNA and other observed sequence data that characterize different genes or species, and generate trees of evolutionary relationships that best explains the observed data. One of the most popular and widely used software for this task is called RAxML. However, in order to use it you need to be comfortable using the command-line on your computer and put together complex data pipelines. This is a hurdle for most biologists, and in our article we present our solution to overcome that.
Where did the idea to develop this method come from?
The idea came from Daniele Silvestro, one of my co-authors. He actually developed a first version of raxmlGUI that became widely used. However, the program has since become obsolete, lacking support for newer computers and recent advances in phylogenetic inference such as those necessary for analysing increasingly large genomic datasets. It became clear that a new desktop GUI was needed, and I proposed to build a new program from the ground up using modern web technologies.
What were the major challenges in developing this method? How did you overcome this?
As with all software, a major challenge is to make them as simple and intuitive as possible to use for a broad audience, while still not limiting the use case for advanced users. We solved this trade-off by focusing on simplicity but exposing the underlying command-line calls for RAxML so that an advanced user can modify them manually.
How do you plan to apply the method you published/what have you been working on since its publication?
I used raxmlGUI 2.0 immediately after its release, in a course in advanced phylogeny, and my co-authors have been using the program for teaching at universities in Sweden and Switzerland at bachelor and masters level and for several research projects.
Since the publication of raxmlGUI I have worked mainly on two projects, both related to our network community detection algorithm called Infomap. In the first project, led by Jelena in IceLab, I implemented an efficient Bayesian regularization for more reliable community detection in networks with missing links. In the second project, I developed a method to incorporate phylogenetic data into Infomap Bioregions, our network-based method to map biogeographical regions from species distributions. In parallel I keep developing raxmlGUI adding new features and fixes with the help of a growing community of users worldwide.
Who will benefit from your method?
As our application lowers the barrier to use the latest, state-of-the-art methods to build robust phylogenetic hypotheses, it will benefit students, researchers and professionals in many fields, from evolutionary biology to taxonomy, drug discovery and epidemiology.
It can be applied on data from a single to thousands of species, and has for example been used to better understand the evolution of the SARS-Cov-2 coronavirus.
If you could travel back in time, would you add to or change anything about your method?
There are always improvements and fixes to do but I’m glad we implemented automatic updates from the start so we don’t have to travel back in time to make sure users are getting the updates they should.
You can read Daniel’s full paper here
and discover more about the Robert May Prize 2022 shortlist here.