Hello everyone, I am working on a multi-omics project where I have to deal with RNA-seq, ChIP-seq, and metabolomics datasets that are several terabytes in size. My goal is to integrate them into Galaxy as smoothly as possible while keeping the processes reproducible and running fast. I am looking for: Best practices on how to organize and store large datasets efficiently in Galaxy. Advice on pipeline optimization in such a way that the runtime and memory usage are at a minimum. Information on traversing and documenting workflows when mixing different omics. Has anyone within the community ever managed a similar Galaxy-integrated multi-omics? Workflows, scripts or simple strategies would help a lot. I appreciate your experiences and advice! https://stealbrainrotgame.com