Integration of OMICS datasets

Development of tools for aberrant expression, splicing and differential usage analyses

This project is being carried out by PhD student Ir. Alexandre Segers and supervised by Prof. Dr. Elfride De Baere, Prof. Dr. Ir. Lieven Clement and Dr. Ir. Mattias Van Heetvelde.

RNA-seq data analysis relies on many different tools, each tailored to specific applicatio​ns and coming with unique assumptions and restrictions. Indeed, tools for differential transcript usage, or diagnosing patients with rare diseases through splicing and expression outliers, either lack in performance, discard information, or do not scale to massive data compendia. We argue that replacing the normalisation offsets unlocks bulk RNA-seq workflows for scalable differential usage, aberrant splicing and expression analyses. Further, by adapting the estimation equations of the Newton-Raphson algorithm, we will enhance scalability towards large design matrices. This work will provide a single workflow for various short- and long-read RNA-seq application, enhancing power by using all information available and ensuring a computational efficient workflow by changing the normalization offsets and the estimation equations.