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Where the Patterns Are: Repetition-Aware Compression for Colored de Bruijn Graphs.

Alessio Campanelli1, Giulio Ermanno Pibiri1,2, Jason Fan3

  • 1DAIS, Ca' Foscari University of Venice, Venice, Italy.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|October 9, 2024
PubMed
Summary
This summary is machine-generated.

We developed new compressed data structures for colored de Bruijn graphs (c-dBGs) to reduce memory usage in large-scale sequence indexing. These structures significantly improve space efficiency for k-mer color sets, enabling faster biological data analysis.

Keywords:
colored de Bruijn graphdata compressionhashingpseudoalignment

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Structures

Background:

  • Colored de Bruijn graphs (c-dBGs) are crucial for sequence indexing in computational biology.
  • Large memory footprints of c-dBGs hinder scalability for extensive genomic datasets.
  • Efficiently mapping k-mers to their color sets is essential for applications like read mapping and abundance estimation.

Purpose of the Study:

  • To introduce novel lossless compressed data structures for c-dBGs.
  • To address the significant memory challenges associated with large-scale sequence indexing.
  • To improve the space-effectiveness of indexing k-mer color sets.

Main Methods:

  • Leveraging the inherent repetitiveness of color sets in large, related genome collections.
  • Factorizing color sets into repeating patterns across the entire dataset.
  • Representing these patterns once to avoid redundant storage of atomic lists of integers.

Main Results:

  • Achieved substantial improvements in space efficiency compared to previous solutions, with some indexes being an order of magnitude smaller.
  • Demonstrated significant reductions in memory usage for c-dBG representations.
  • Maintained only moderate impacts on query efficiency despite dramatic space savings.

Conclusions:

  • The developed compressed data structures offer a highly space-effective solution for indexing colored de Bruijn graphs.
  • These methods significantly reduce memory requirements for large-scale genomic data analysis.
  • The approach balances substantial space reduction with acceptable query performance.