Back

Deep FASTQ and BAM co-compression in Genozip 15

Lan, D. M.; Hughes, D. S. T.; Llamas, B.

2023-07-07 bioinformatics
10.1101/2023.07.07.548069 bioRxiv
Show abstract

We introduce Genozip Deep, a method for losslessly co-compressing FASTQ and BAM files. Benchmarking demonstrates improvements of 75% to 96% versus the already-compressed source files, translating to 2.3X to 6.8X better compression than current state-of-the-art algorithms that compress FASTQ and BAM separately. The Deep method is independent of the underlying FASTQ and BAM compressors, and here we present its implementation in Genozip, an established genomic data compression software.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.