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Summary

We introduce a refined minimizer operator that improves k-mer repetitiveness in DNA sequences. This efficient method enhances sequence analysis tools like read mapping and binning.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Minimizer concept is crucial for sequence sketching and widely used in bioinformatics.
  • Standard canonical minimizers face trade-offs between k-mer density, repetitiveness, and computational efficiency.
  • High-performance minimizer algorithms require generic, effective, and efficient solutions.

Purpose of the Study:

  • To propose a refined minimizer operator that enhances k-mer repetitiveness.
  • To develop a computationally efficient minimizer applicable to various selection schemes.
  • To improve sequence analysis applications such as read mapping and binning.

Main Methods:

  • A simple minimizer operator is proposed as a refinement of the standard canonical minimizer.
  • The operator is designed for computational efficiency and minimal operational overhead.
  • The method is evaluated for its impact on k-mer repetitiveness and density.

Main Results:

  • The refined minimizer operator significantly improves k-mer repetitiveness, particularly for lexicographic ordering.
  • It demonstrates computational efficiency comparable to standard minimizers.
  • K-mer density remains close to that of the standard minimizer.
  • The operator is applicable to diverse selection schemes, including random orders.

Conclusions:

  • The proposed minimizer operator offers an effective and efficient refinement for sequence sketching.
  • It addresses the trade-offs in existing minimizer variants, enhancing performance.
  • This method holds potential for improving high-throughput sequencing data analysis.