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MBGC2: Boosting compression via efficient encoding of approximate matches in genome collections
1Institute of Applied Computer Science, Lodz University of Technology, ul. Stefanowskiego 18, 90-537 Lodz, Poland.
Gigascience
|January 21, 2026
Summary
The Multiple Bacteria Genome Compressor (MBGC2) significantly improves genomic data compression, offering an average 14% higher compression ratio and 40% faster decompression. This tool enhances storage and transfer efficiency for large biological datasets.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- FASTA format is standard for biological sequences, but existing compressors have limitations.
- Large genomic datasets pose storage and transfer challenges.
- Specialized FASTA compressors lack consistent performance.
Purpose of the Study:
- To present an enhanced genome collection compression algorithm, MBGC2.
- To improve compression ratios and decompression speeds for genomic data.
- To provide practical features for managing large sequence archives.
Main Methods:
- Developed an enhanced Multiple Bacteria Genome Compressor (MBGC2).
- Utilized information on maximum exact matches to identify approximate matches.
- Implemented multi-threaded decoding for faster decompression.
Main Results:
- Achieved an average 14% increase in compression ratio for bacterial datasets.
- Improved compression by 18% for H. sapiens and up to 55% for S. paradoxus.
- Increased decompression speed by approximately 40% compared to the predecessor.
Conclusions:
- MBGC2 overcomes limitations in genome compression with reliable performance and usability.
- Consistent efficiency across diverse datasets makes MBGC2 suitable for research and clinical use.
- Offers a practical balance of compression ratio and speed for large genomic data management.
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