A k-mer-Based Estimator of the Substitution Rate Between Repetitive Sequences

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• K-mer-based genomic analysis is widely used but struggles with accuracy due to repetitive sequences.
• Existing tools like Mash are inaccurate on repetitive genomic regions (e.g., centromeres).
• Previous theoretical models for mutation rate estimation rely on non-repetitive sequence assumptions.

Purpose of the Study:

• To develop a novel k-mer-based estimator for mutation rate that accounts for repetitive sequences.
• To provide theoretical bounds on the bias of the proposed estimator.
• To demonstrate the estimator's accuracy and robustness across diverse genomic datasets.

Main Methods:

• Relaxed the non-repetitive sequence assumption inherent in previous k-mer analysis methods.
• Derived a novel estimator for substitution rate incorporating repeat-aware calculations.
• Utilized sequence sketching to manage large k-mer sets efficiently while maintaining accuracy.

Main Results:

• The novel estimator accurately calculates mutation rates in repetitive genomic sequences, including centromeric satellite repeats.
• Theoretical bias bounds were derived for the new estimator.
• Demonstrated robustness across various substitution rates, k-mer sizes, and diverse datasets.

Conclusions:

• The proposed repeat-aware substitution rate estimator overcomes limitations of existing methods on repetitive genomic data.
• This work provides a robust theoretical and practical framework for analyzing mutation rates in complex genomes.
• Sequence sketching offers an effective strategy for scalable and accurate k-mer-based genomic analysis.