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  1. Home
  3. Normalized Compression Distance For Dna Classification.
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  3. Normalized Compression Distance For Dna Classification.

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Normalized compression distance for DNA classification.

Gavin Hearne1, Mohammadsaleh S Refahi1, Haozhe Neil Duan1

  • 1Department of Electrical Engineering, Drexel University, Philadelphia, PA, United States of America.

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|February 11, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Normalized compression distance (NCD) offers a low-resource method for genomic sequence analysis. This approach shows promise in gene labeling and taxonomic classification, especially in low-data scenarios.

Keywords:
Alignment-free methodsBioinformaticsCompression distanceGenomic classificationGenomic sequence analysisGzip compressionMetagenomics

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Genomic sequence analysis is crucial for understanding biological systems, including the human microbiome.
  • Normalized Compression Distance (NCD) is a low-resource technique that approximates Kolmogorov complexity, showing potential beyond text classification.
  • Existing tools like Many-against-Many sequence searching (MMseqs) and Kraken2 are effective but can be resource-intensive.

Purpose of the Study:

  • To explore the application of a gzip-based NCD for gene labeling of open reading frames (ORFs) and taxonomic classification of short genomic reads.
  • To evaluate NCD's performance as an alternative to deep neural networks (DNNs) and traditional sequence analysis tools in genomics.
  • To assess NCD's effectiveness in both in-distribution and out-of-distribution classification tasks.

Main Methods:

  • Implementation of a gzip-based Normalized Compression Distance (NCD) algorithm.
  • Application of NCD for gene labeling of open reading frames (ORFs).
  • Utilizing NCD for taxonomic classification of short sequencing reads.

Main Results:

  • Achieved 0.89 accuracy and 0.88 macro-F1 for human gene classification, outperforming other NCD methods.
  • Demonstrated superior classification accuracy compared to alignment and exact-match tools in out-of-distribution prokaryotic gene labeling.
  • Outperformed sequence-embedding methods in in-distribution prokaryotic gene labeling tasks.

Conclusions:

  • Compression-based approaches, specifically NCD, offer an effective alternative for genomic sequence classification.
  • NCD shows particular strength in low-data environments and out-of-distribution scenarios.
  • Computational complexity (O(MN)) is a limitation for extremely large datasets, but NCD remains a viable low-resource strategy.