We are a group of researchers applying computational approaches involving the analysis of high-throughput genomic and transcriptomic data aiming to understand how changes at the RNA-level increase proneness to diseases, namely cancer, neurodegenerative disorders and other ageing-related pathologies. Along the way, we develop some tools for assisting non-computational scientists in their analyses of transcriptomic data.
We develop applications for assisting non-computational scientists in their analyses of transcriptomic data. We aim to contribute towards bridging the conceptual and knowledge gaps between wet- and dry-lab researchers, by transparently sharing our approaches to data analysis in ways that make them understandable and therefore open for scrutiny by fellow scientists, also facilitating their reproducibility. Our bioinformatics tools empower colleagues to intelligibly perform similar analyses to ours, by visually assisting their decision-making process, avoiding “black boxes”. We make our applications open source, user-friendly and freely available on our webserver, to democratise their use by the entire scientific community. We further increase their usability through the creation of accompanying tutorials and other training materials.
Paper [1] [2] R package GitHub
Alternative splicing quantification, visualisation and analysis
Impact (examples): Neurobiology Splicing Regulation Cancer
Reference-free identification of open reading frames and encoded proteins from nanopore transcriptomic long reads
At iMM, we have the opportunity of using Lobo, a multi-hundred node computer cluster. Lobo makes extensive use of Docker images, each a read-only templates that carries the instructions to build a Docker container. Containers are packaged applications that carry the necessary dependencies to allow running the program in every computer environment. The following Docker images were developed by people in the Disease Transcriptomics team (in green, those under active maintenance).
scStudio
betAS