Dear-OMG: An Omics-General Compression Method for Genomics, Proteomics and Metabolomics Data

As high-throughput omics technologies continue to advance, researchers are facing the challenge of a rapid surge in proteomics, metabolomics, and genomics data. This growth not only necessitates more disk space and network bandwidth, but also complicates data sharing and subsequent analysis. To address this challenge and enhance the analytical efficiency of downstream software, we propose Dear-OMG, a unified, compact, flexible, and high-performance metadata storage solution. Dear-OMG introduces a novel file storage structure and utilizes the Elias-Fano encoding algorithm to compress and store proteomics, genomics, and metabolomics metadata into the unified OMG format. The OMG format not only demonstrates remarkably high compression and decompression speeds, but also enables parallel random access to any data block. Test results reveal that, compared to the commonly used proteomics formats of mzXML and mzML, the OMG format achieves an 80% reduction in storage space, a 90% decrease in conversion time, and approximately a tenfold speed improvement with support for parallel random access. Dear-OMG is freely available at https://github.com/jianweishuai/Dear-OMG .