  Searching for similar genomic sequences is an essential and fundamental step
in biomedical research and an overwhelming majority of genomic analyses.
State-of-the-art computational methods performing such comparisons fail to cope
with the exponential growth of genomic sequencing data. We introduce the
concept of sparsified genomics where we systematically exclude a large number
of bases from genomic sequences and enable much faster and more
memory-efficient processing of the sparsified, shorter genomic sequences, while
providing similar or even higher accuracy compared to processing non-sparsified
sequences. Sparsified genomics provides significant benefits to many genomic
analyses and has broad applicability. We show that sparsifying genomic
sequences greatly accelerates the state-of-the-art read mapper (minimap2) by
2.57-5.38x, 1.13-2.78x, and 3.52-6.28x using real Illumina, HiFi, and ONT
reads, respectively, while providing up to 2.1x smaller memory footprint, 2x
smaller index size, and more truly detected small and structural variations
compared to minimap2. Sparsifying genomic sequences makes containment search
through very large genomes and large databases 72.7-75.88x faster and 723.3x
more storage-efficient than searching through non-sparsified genomic sequences
(with CMash and KMC3). Sparsifying genomic sequences enables robust microbiome
discovery by providing 54.15-61.88x faster and 720x more storage-efficient
taxonomic profiling of metagenomic samples over the state-of-the-art tool
(Metalign). We design and open-source a framework called Genome-on-Diet as an
example tool for sparsified genomics, which can be freely downloaded from
https://github.com/CMU-SAFARI/Genome-on-Diet.
