Genome-on-Diet is a fast and memory-frugal framework for exemplifying sparsified genomics for read mapping, containment search, and metagenomic profiling. It is much faster & more memory-efficient than minimap2 for Illumina, HiFi, and ONT reads. Described by Alser et al. (preliminary version: https://arxiv.org/abs/2211.08157).
Very interesting work, and some nice results in terms of performance and sensitivity! However, the false positive rate is pretty startling--I suspect it is unacceptably high for many applications.
In section 3.6.3 I noticed this concept of "rescue mapping" when there is no hit above the user-provided threshold. I was curious: do you see any enrichment for false positives among these rescued reads? You mention that it improves sensitivity, but I am curious what the cost is.