A simple refined DNA minimizer operator enables 2-fold faster computation.

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.