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Updated: Sep 15, 2025

Production of Double-stranded DNA Ministrings
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GreedyMini: generating low-density DNA minimizers.

Shay Golan1,2, Ido Tziony3, Matan Kraus3

  • 1Department of Computer Science, University of Haifa, Haifa 3498838, Israel.

Bioinformatics (Oxford, England)
|July 15, 2025
PubMed
Summary
This summary is machine-generated.

GreedyMini generates minimizers for high-throughput sequencing (HTS) data, achieving lower k-mer densities than existing methods. This toolkit improves HTS algorithm performance by efficiently selecting representative k-mers.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Minimizers are widely used k-mer selection schemes in high-throughput sequencing (HTS) data analysis.
  • Current minimizer schemes often result in redundant k-mer selections, increasing data processing burdens.
  • There is a need for methods to generate minimizers with lower k-mer densities for improved HTS analysis efficiency.

Purpose of the Study:

  • To develop a novel method for generating minimizers with minimized expected density.
  • To improve upon existing minimizer selection schemes for HTS data.
  • To provide a flexible toolkit for various k-mer selection scenarios.

Main Methods:

  • Developed GreedyMini, a toolkit for generating minimizers with controllable densities.
  • Extended minimizer generation to larger alphabets, k, and w values.
  • Implemented efficient methods for measuring the expected density of minimizers.

Main Results:

  • GreedyMini generates DNA minimizers with expected densities close to the theoretical lower bound.
  • Achieved significantly lower expected and particular densities compared to existing selection schemes.
  • Demonstrated comparable k-mer rank-retrieval time to common k-mer hash functions.

Conclusions:

  • GreedyMini offers a powerful new approach to k-mer selection in HTS.
  • The toolkit is expected to enhance the performance of numerous HTS algorithms and data structures.
  • This work advances the research field of k-mer selection schemes for genomic data analysis.