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NYX: Format-aware, learned compression across omics file types

Patsakis, M.; Chronopoulos, T.; Mouratidis, I.; Georgakopoulos-Soares, I.

2026-03-17 bioinformatics
10.64898/2026.03.16.712193 bioRxiv
Show abstract

Genomic data repositories continue to grow as sequencing technologies improve, with the NCBI SRA alone exceeding 47 PB. General-purpose compressors treat bioinformatics files as unstructured byte streams and fail to exploit the structured nature of omics data. We present NYX, a format-aware compression system for FASTA, FASTQ, VCF, WIG, H5AD, and BED files. NYX combines lightweight, reversible preprocessing and is build upon the OpenZL framework to take advantage of inherent data structure, delivering high compression ratios while preserving fast and lossless compression. Across representative datasets in the target formats, NYX achieves substantially higher speed than format-specific compressors while maintaining or improving compression ratio.

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The top 2 journals account for 50% of the predicted probability mass.

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"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.