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A novel and efficient feature extraction algorithm using kmer-derived mutation signal.

JingJing Zhang1, XinGong Zhang1, Jianwen Huang1

  • 1School of Mathematical Sciences, Chongqing Normal University, Chongqing, China.

Peerj
|March 16, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel algorithm for genomic feature extraction using kmer analysis, identifying biologically significant "anchors." This method improves phylogenetic tree construction and machine learning classification, aiding in mutation analysis.

Keywords:
KmerMutation signal simulationPhylogenetic analysisWeighted interval entropy

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Kmer-based genomic feature extraction is vital for bioinformatics.
  • The biological significance of kmer frequency and position requires further investigation.
  • Current methods face challenges in mining the biological significance of kmers.

Purpose of the Study:

  • To develop a novel feature extraction algorithm simulating genomic mutation behavior.
  • To identify biologically significant kmers, termed "anchors."
  • To define a weighted interval entropy for characterizing anchor information content.

Main Methods:

  • Simulated genomic accumulation mutation behavior using reverse, complementary, and reverse-complementary kmers.
  • Identified "anchors" by examining elastic length regions for specific kmer scenarios.
  • Defined weighted interval entropy based on anchor interval signals.

Main Results:

  • The proposed algorithm demonstrated superior effectiveness compared to position and frequency-based methods.
  • Achieved enhanced performance in constructing species phylogenetic trees.
  • Showed improved accuracy in machine learning classification tasks.
  • Enabled diagnosis of mutation types and accumulation directions in Ebola-Zaire virus (EBOV) and hepatitis C virus (HCV) genomes.

Conclusions:

  • The novel kmer-based algorithm effectively extracts genomic features with biological significance.
  • Identified "anchors" and weighted interval entropy provide valuable insights into genomic information.
  • The method shows potential for diagnosing viral mutation dynamics and evolutionary patterns.