kmdiff

kmdiff performs differential k-mer analysis between control and case populations using short-read sequencing data to identify k-mers with differential representation for genomic comparisons and disease-association studies.

Key Features:

• Efficiency in Analysis: Computational optimizations reduce time and memory requirements to enable large-scale cohort analyses.
• Differential Representation Identification: Identifies k-mers that are differentially represented between control and case populations.
• Application in GWAS: Uses k-mers as base signals to detect associations in genome-wide association studies that may be missed by single-nucleotide polymorphism (SNP)-based analyses.

Scientific Applications:

• Genomic Research: Enables detailed comparisons of genetic material across populations by analyzing k-mer frequency differences from short-read sequencing data.
• Disease Association Studies: Highlights differential k-mers linked to disease susceptibility or resistance for association analyses.
• Genome-wide association studies (GWAS): Provides k-mer–level association signals complementary to SNP-based approaches.

Methodology:

Analyzes short-read sequencing data from control and case groups by computing the frequency of each k-mer and identifying k-mers that are differentially represented between the two populations.