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MBGC: Multiple Bacteria Genome Compressor.

Szymon Grabowski1, Tomasz M Kowalski1

  • 1Institute of Applied Computer Science, Lodz University of Technology, ul. Stefanowskiego 18, 90-537 Lodz, Poland.

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|January 27, 2022
PubMed
Summary
This summary is machine-generated.

We developed MBGC, a new bacterial genome compressor that significantly improves compression ratios and speeds. This tool efficiently handles bacterial genome data, outperforming existing methods for faster and more effective genomic analysis.

Keywords:
FASTAalgorithmsdata compressionmultiple genome compressionpathogens

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Genomes within the same species exhibit high similarity, which can be exploited for data compression.
  • Existing multiple genome compressors are optimized for large genomes (e.g., mammalian) and show moderate performance on bacterial strains.

Purpose of the Study:

  • To develop a specialized genome compressor, MBGC (Multi-threaded Bacterial Genome Compressor), tailored for the unique redundancies found in bacterial genomes.
  • To enhance both the compression efficiency and speed for bacterial genome data.

Main Methods:

  • MBGC identifies direct and reverse-complemented LZ-matches specific to bacterial genomes.
  • A multi-threaded implementation with careful reference buffer management is employed.
  • The algorithm was tested on a large dataset of 168,311 bacterial genomes (587 GB).

Main Results:

  • MBGC achieved a compression ratio of approximately 1,265.
  • Compression and decompression speeds reached approximately 1,580 MB/s and 780 MB/s, respectively, using 8 hardware threads.
  • Compared to the next best competitor, MBGC demonstrated nearly 3x better compression and >6x faster compression speeds.

Conclusions:

  • MBGC offers a highly efficient and fast solution for bacterial genome compression.
  • The specialized approach significantly outperforms existing general-purpose genome compressors for bacterial data.
  • This advancement facilitates more effective storage and analysis of large bacterial genomic datasets.