AFLAP

AFLAP constructs high-density genetic linkage maps without requiring a pre-existing genome assembly by using k-mer-based markers for linkage analysis.

Key Features:

• Assembly-free mapping: Performs linkage analysis without a reference genome assembly.
• k-mer markers: Uses k-mers (short DNA sequences of length k) as genetic markers for linkage analysis.
• Jellyfish hashing: Processes raw sequencing reads and utilizes Jellyfish hashes to compare parental genomes.
• Unique segregating k-mer identification: Identifies uniquely segregating k-mers that serve as reliable genetic markers.
• Genotype table generation: Directly generates a genotype table from k-mer markers, bypassing read alignment and variant calling.
• Data types supported: Works with whole genome sequencing and genotyping-by-sequencing (GBS) data, including low-coverage datasets.
• High-density maps: Produces high-density genetic maps (e.g., >130,000 markers from 100 F2 Arabidopsis thaliana individuals and >90,000 markers in Bremia lactucae).
• Mapping population support: Applicable to F2, F1, and recombinant inbred line mapping populations across species such as Arabidopsis thaliana, Lactuca spp., and Bremia lactucae.
• Assembly improvement: Enables fragmenting, ordering, orienting, and scaffolding of genome assemblies using genetic maps.
• Bias mitigation: Mitigates biases associated with read alignment and variant calling through an assembly-independent k-mer approach.
• Validation: Validated through simulations and empirical datasets.

Scientific Applications:

• High-density linkage mapping: Generate dense linkage maps, demonstrated by >130,000 markers from 100 F2 Arabidopsis thaliana individuals sequenced at low coverage.
• GBS-based mapping: Produce linkage maps from genotyping-by-sequencing data, demonstrated on 235 recombinant inbred lines of Lactuca spp.
• Genome assembly scaffolding: Facilitate fragmentation, ordering, orienting, and scaffolding of genome assemblies, as shown with Bremia lactucae where >90,000 markers across 19 linkage groups improved the reference assembly.
• Low-coverage data analysis: Enable linkage analysis using low-coverage whole-genome sequencing and moderate-coverage datasets (>5× coverage).
• Cross-species application: Apply to diverse organisms with available mapping populations to generate genetic maps and assist assembly improvement.

Methodology:

Processes raw sequencing reads, uses Jellyfish hashes to compare parental genomes, identifies uniquely segregating k-mers, generates a genotype table from k-mer markers, and performs linkage analysis to construct genetic maps.