The Rfam database has been a cornerstone resource for RNA sequence families for over two decades. When the team began planning a redesign, they saw an opportunity to put AI tools to the test. Philippa Muston, Full Stack Developer at EMBL-EBI shares her team’s experience and learnings from the process.
About Rfam
The Rfam database is a collection of RNA sequence families of structural RNAs including non-coding RNA genes and cis-regulatory elements. Each family is represented by a multiple sequence alignment, a consensus secondary structure, and a covariance model.
Rfam serves as a valuable resource for annotating and exploring the structure and distribution of ncRNAs, and as a data source for training AI models to predict functions, structure and interactions of ncRNAs. Users can browse data and genome annotations on the website, download the full dataset from the FTP site, or access it programmatically through the Rfam API.
Tell me a little bit about the Rfam website. Why and when was it set up? The Rfam website was set up over 20 years ago, written in a programming language that was very popular at the time called Perl, and using an old framework called Catalyst. The website was overdue for a refresh as it was getting harder to maintain and update. What are the main goals of the redesign? We wanted to move to Python and update to the EMBL-EBI visual framework, a shared design that aligns our look and feel with the larger ‘family’ of biodata resources at EMBL-EBI. This redesign vastly improves the website visually. The new layouts are much more modern, accessible, intuitive and user-friendly to navigate, while also allowing the website to be fully responsive across different browsers and devices. The front end got a big makeover. The challenge was the backend, as it has to maintain the legacy API URLs that people’s scripts interact with. The code has to be deployed to Kubernetes, a...