REDHORSE-REcombination and Double crossover detection in Haploid Organisms using next-geneRation SEquencing data

Area of Science:

• Genomics
• Bioinformatics

Background:

• Next-generation sequencing (NGS) enables high-resolution genetic exchange studies.
• Existing recombination detection algorithms require multiple sequence alignments, posing challenges for NGS data.
• A novel software suite, REDHORSE, was developed to address these limitations.

Purpose of the Study:

• To develop a software suite (REDHORSE) for analyzing genetic crosses using NGS data.
• To bridge the gap between genomic alignments and existing recombination detection algorithms.
• To implement a custom algorithm for accurate crossover detection in NGS data.

Main Methods:

• REDHORSE processes genomic alignments to extract genetic markers.
• It generates multiple sequence alignments suitable for existing algorithms.
• A custom recombination detection algorithm within REDHORSE utilizes sequence information and genomic positions.

Main Results:

• REDHORSE was validated using simulated and real NGS data from a Toxoplasma gondii genetic cross.
• The software successfully transformed NGS read alignments into multiple sequence alignments.
• REDHORSE accurately detected conventional and double crossovers, outperforming other algorithms and filtering sequencing artifacts.

Conclusions:

• REDHORSE is an efficient, portable, and platform-independent Java-based pipeline for analyzing genetic crosses with NGS data.
• It serves as a crucial link between genomic alignments and recombination detection tools.
• REDHORSE includes a specialized algorithm for detecting crossovers in NGS data.