REDHORSE

REDHORSE detects recombination events in haploid organisms from next-generation sequencing (NGS) genomic alignments by transforming alignments for compatibility with recombination detection approaches and identifying crossovers and double crossovers.

Key Features:

• Genomic alignment processing: Processes genomic alignments to extract genetic markers and generate multiple sequence alignments compatible with recombination detection algorithms.
• Custom recombination detection algorithm: Implements a bespoke algorithm that leverages sequence information and genomic positions to identify conventional crossovers and double crossovers associated with gene conversion.
• Artifact filtering: Filters sequencing and alignment artifacts to reduce false-positive recombination calls.
• Performance and validation: Validated on simulated recombination datasets and real NGS data, including a genetic cross of Toxoplasma gondii, against known breakpoints.
• Comparative advantage: Outperforms commonly used recombination detection algorithms in identifying conventional crossovers and uniquely detects double crossovers.
• Java-based implementation: Provides a Java-based bridge that transforms genomic alignments into formats compatible with existing recombination detection tools.

Scientific Applications:

• Genetic mapping in haploids: Detects crossovers and double crossovers for mapping genetic crosses of haploid organisms.
• Evolutionary and population genetics: Studies mechanisms of genetic exchange and recombination using NGS-derived genomic data.
• Pathogen genetics: Analyzes recombination in experimental crosses such as the Toxoplasma gondii genetic cross included in validation.

Methodology:

Processes genomic alignments to extract genetic markers and generate multiple sequence alignments; applies a bespoke algorithm using sequence information and genomic positions to detect conventional crossovers and double crossovers (including gene conversions); filters sequencing and alignment artifacts; validated using simulated and real NGS datasets including a Toxoplasma gondii genetic cross.