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GABAC: an arithmetic coding solution for genomic data.

Jan Voges1, Tom Paridaens2, Fabian Müntefering1

  • 1Institut für Informationsverarbeitung (TNT), Leibniz University Hannover, 30167 Hannover, Germany.

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Summary
This summary is machine-generated.

Genomic data compression is improved by GABAC, a new entropy codec for the MPEG-G standard. This solution enhances existing genomic compression tools, offering better performance for sequencing data management.

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Area of Science:

  • Bioinformatics
  • Genomic data compression
  • Data management

Background:

  • The rapid growth of genomic data necessitates efficient compression and management solutions.
  • The International Organization for Standardization (ISO) is developing the Moving Picture Experts Group (MPEG)-G standard for genomic sequencing data.
  • Existing compression methods require optimization to handle the increasing volume of genomic information.

Purpose of the Study:

  • To introduce GABAC, the first entropy codec implementation compliant with the MPEG-G standard.
  • To evaluate the performance of GABAC in compressing genomic sequencing data.
  • To present GABAC as a potential extension for current genomic compression tools.

Main Methods:

  • Implementation of GABAC using C++.
  • Integration of established coding technologies: context-adaptive binary arithmetic coding, binarization schemes, and transformations.
  • Development of a command-line application to demonstrate GABAC's functionalities.

Main Results:

  • GABAC demonstrates superior performance compared to established entropy codecs in various scenarios.
  • The GABAC codec effectively compresses genomic sequencing data.
  • GABAC can be integrated with existing solutions like CRAM for enhanced genomic data compression.

Conclusions:

  • GABAC offers a significant advancement in genomic data compression technology.
  • The MPEG-G standard, with implementations like GABAC, is crucial for managing large-scale genomic datasets.
  • GABAC provides a robust and efficient solution for the challenges posed by big genomic data.