ONYX: an alignment-free biological sex inference from high-throughput sequencing data.

Area of Science:

• Genomics
• Bioinformatics
• Population Genetics

Background:

• Biological sex inference is crucial for population genomics, conservation, and forensics.
• Existing methods often rely on sequence alignment or species-specific markers, limiting cross-system applicability.
• A universal, alignment-free approach is needed for scalable sex determination across diverse sex chromosome systems.

Purpose of the Study:

• To introduce ONYX, an alignment-free computational framework for biological sex inference.
• To demonstrate the framework's ability to work across different sex chromosome systems (e.g., XY and ZW).
• To provide a unified and efficient tool for sex determination from genomic data.

Main Methods:

• Developed ONYX, a framework utilizing sex chromosome-derived k-mer collections.
• Constructed homogametic- and heterogametic-specific k-mer collections from reference genomes.
• Inferred sex using a unified heterogametic signal score ($K{R}_{het}$) applied to sequencing reads.

Main Results:

• ONYX successfully distinguished heterogametic from homogametic individuals in human (XY) and chicken (ZW) whole-genome sequencing data.
• The $K{R}_{het}$ score showed consistent interpretation across different sex chromosome systems.
• Validation in Atlantic cod confirmed ONYX's applicability even with limited heterogametic-specific sequences.

Conclusions:

• ONYX provides a robust, alignment-free method for biological sex inference applicable across diverse sex chromosome systems.
• The unified $K{R}_{het}$ score offers a transferable metric for sex determination.
• The framework is computationally efficient, scalable, and suitable for time-sensitive genomic analyses.