I think you got the main applications covered.

It should be mentioned that fine-tuned protein LLMs can be used to predict almost any physical property of proteins (solubility) and many extraneous qualities (localization, optimal temperature for activity). There is also a generative side, where new proteins are created daily with improved properties.

https://github.com/RosettaCommons/RFdiffusion

After training and fine-tuning, DNA/RNA/protein sequences can be classified based on many different criteria. One of the questions that often gets asked here is identifying rRNAs in a mixture of sequences, and in my opinion the best tool for this task is GPU-dependent (it works without it, but very slow):

https://github.com/hzi-bifo/RiboDetector

Any field of biology/bioinformatics that deals with images is almost guaranteed to be heavily GPU-dependent. Same for many applications of machine learning that deal with large amounts of data, even if they are not images.

Using mapping programs to map reads is pretty helpful. I've put together a quick list; needs to be updated.

URL: https://github.com/cement-head/GPU-Bioinformatics/wiki

If anyone has any papers/links to contribute, please make a comment and I'll add to the list.

NVIDIA itself provides a number of GPU optimized tools (grouped under the name 'parabricks') :

https://docs.nvidia.com/clara/parabricks/latest/toolreference.html

Single-cell data analysis: https://github.com/scverse/rapids_singlecell

Single-cell and spatial RNA-seq analysis tool: https://github.com/Starlitnightly/omicverse