Minisymposium Presentation

The enormous data growth continuously shifts the life sciences from model-driven towards data-driven science. The need for efficient processing has led to the adoption of massively parallel accelerators such as GPUs. As a consequence, genomics and proteomics method development nowadays often heavily depends on the effective use of these powerful technologies. Furthermore, progress in both computational techniques and architectures continues to be highly dynamic including novel deep neural network models and AI accelerators. For example, contemporary groundbreaking AI-tools like AlphaFold can generate highly accurate 3D protein structure predictions. In this talk, I present two novel tools for accelerating large-scale protein homology search on modern GPU systems: CUDASW++4.0 and MMseqs2-GPU, which advance the state-of-the-art in this area. For example, MMSeqs2-GPU can be used to significantly accelerate the computation of multiple sequence alignments in the ColabFold server for protein structure prediction, which is one of the most frequently used bioinformatics tools worldwide.