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Ali Osman Berk Şapcı1, Siavash Mirarab2,3

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|August 29, 2024
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We introduce k-mer RANKer (KRANK), a novel algorithm for selecting k-mer subsets from large databases. KRANK reduces memory usage for metagenomic classification with minimal accuracy loss, outperforming existing methods on imbalanced datasets.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • K-mer-based sequence matching is vital for metagenomic classification.
  • Growing reference databases pose scalability challenges due to high memory requirements.
  • Existing k-mer subsampling methods struggle with taxonomically imbalanced datasets.

Purpose of the Study:

  • To develop a method for selecting a fixed-size k-mer subset from ultra-large datasets for efficient metagenomic classification.
  • To address the limitations of current subsampling strategies, particularly for imbalanced microbial libraries.
  • To minimize classification accuracy loss while significantly reducing memory consumption.

Main Methods:

  • Proposed k-mer RANKer (KRANK) algorithm, incorporating hierarchical selection, adaptive size restrictions, and equitable coverage.
  • Implemented KRANK with optimized code and integrated it with the CONSULT-II classifier.
  • Evaluated performance using established benchmarks, including the CAMI dataset.

Main Results:

  • KRANK significantly reduces memory footprint compared to existing k-mer selection approaches.
  • Minimal loss in classification accuracy was observed with KRANK.
  • KRANK demonstrated superior taxonomic profiling performance against k-mer alternatives.

Conclusions:

  • KRANK offers an effective solution for memory-efficient metagenomic classification.
  • The algorithm shows promise in handling taxonomically imbalanced reference libraries.
  • KRANK achieves accuracy comparable to marker-based methods while using significantly less memory.