I'd love to see some benchmarks for this on some common genomic formats (fa, fq, sam, vcf). Will be doubly interesting to see its applicability to nanopore data - lots of useful data is lost because storing FAST5/POD5 is a pain.
I would not expect much improvement in compressing nanopore data. If you have a useful model of the data, creating a custom compressor is not that difficult. It takes some effort, but those formats are popular enough that compressors using the known models should already exist.
Naively and knowing little about CRAM, I would expect that OpenZL would beat Zstd handily out of the box, but need additional capabilities to match the performance of CRAM, since genomics hasn't been a focus as of yet. But it would be interesting to see how much we need to add is generic to all compression (but useful for genomics), vs. techniques that are specific only to genomics.
We're planning on setting up a blog on our website to highlight use cases of OpenZL. I'd love to make a post about this.
Happy to discuss further
I will take a look as soon as I get a chance. Looking at the BAM format, it looks like the tokenization portion will be easy. Which means I can focus on the compression side, which is more interesting.
Not today. However, we are considering this as we are continuing to evolve the frame format, and it is likely we will add this feature in the future.