Gioele La Manno – Developer of the resource velocyto, which won the 2019 Bioinformatics Resource Innovation Award
Today, Gioele La Manno is a Group Leader at EPFL where he leads the Neurodevelopmental Systems Biology Group. His lab is mostly interested in the dynamics of nervous system development and heterogeneity of the redial-glia progenitor population. The La Manno Lab studies these biological processes using quantitative techniques and computational methods. To learn more about his research interests, have a look at his group’s webpage and follow @GioeleLaManno on Twitter.
Gioele was a PhD student in the group of Sten Linnarsson at the Karolinska Institute and one of the main developers behind the resource velocyto, which received the 2019 SIB Bioinformatics Resource Innovation Award. Velocyto estimates gene-specific transcriptional derivatives and visualizes single-cell RNA-seq (scRNA-seq) data.
The SIB Bioinformatics Awards will be presented for the 11th time, providing a great occasion to reach out to past laureates and ask them where they are now in their career. In this interview, we met with Gioele La Manno, Developer of the resource velocyto, which won the 2019 Bioinformatics Resource Innovation Award.
At which point of your career were you when Stenn Linnarsson received the SIB Award for the velocyto resource? How did it feel? What was the key interest of your research at this time point?
It felt like a fantastic recognition for our work. I had just finished my PhD, and started as a Group Leader. The prize motivated me even more towards a "think-outside-of-the-box" way of working. After all, the tool we won the resource award for, was the fruit of a new intuition rather than a method of great mathematical depth. While I enjoy a lot the elegance of doing cool derivations, and I recognize the value of a user-friendly tool, trying crazy-sounding ideas is why I am in science.
How has the resource developed since then? Do you have an example of a study in which your resource was used?
We do not see RNA velocity as just “our” tool. Sure, we started it, but I think that the broader involvement of the bioinformatics community is really the most satisfying development I could hope for. Since the early release of velocyto, we have been trying to have an open conversation with other scientists interested in developing on what we started. Nowadays, tools like ScVelo, Alevin Velocity, Seurat have the same capabilities of velocyto, and can do even better. This kind of legacy is really the best recognition we could have gotten.
Indeed, thanks to the SIB prize, we will be able to start a series of international meetings focused on velocity-based methods. The plan is to include hackathons with tool-integration in mind and with the spirit to improve the different tools as a community effort. Unfortunately, for now, the pandemic has been holding us back, but we have exciting plans to organize this in the upcoming future.
In your personal opinion, what is the single most fascinating discovery made possible by bioinformatics?
Two results enabled by bioinformatics come to my mind and never stop impressing me. First, the understanding of evolution that we can achieve based on sequence analysis: looking at the sequences of homologous genes feels like staring directly at the workings of evolution. Second, the possibility to capture phenotypic variation between cells with a single-cell transcriptomics approach. The fact that a single experiment can reveal more about cytology than what you can read from any textbook, still amazes me!
What do you like to do in your free time?
At this stage of my career, there is not a lot of free time. I spend most of it with my wife, we enjoy the beauty of the Swiss mountains, but also love to take a break to travel to Sicily, where I was born, to enjoy the sea in the company of our family and friends.
Any words for the future generation of bioinformaticians?
The pervasiveness of sequencing-based approaches is opening up new possibilities: sequencing is nowadays used to measure indirectly biological variables that are not necessarily DNA-related or encoded. I would encourage bioinformaticians to team up with tech-dev experts to co-design indirect measurement strategies rooted on theory. I would also advise the new generation of machine learning-oriented bioinformaticians to approach problem formulation by focussing their parametrizations on biological principles rather than on performance and algorithmic improvement.