RecombineX

RecombineX automates high-throughput gamete genotyping and tetrad-based recombination analysis to profile meiotic recombination landscapes across diverse organisms and genetic backgrounds.

Key Features:

• High-throughput gamete genotyping: Performs automated genotyping of gametes at scale for tetrad analysis.
• Marker identification: Identifies genetic markers for downstream recombination and genotype calling.
• Tetrad-based recombination profiling: Profiles meiotic crossover and noncrossover events from tetrad data.
• Reference-based and parental-assembly analyses: Supports analyses using a conventional reference genome or parental genome assemblies to preserve native genomic context.
• Copy number variation (CNV) profiling: Detects and profiles CNVs from gamete sequencing data.
• Missing genotype inference: Infers missing genotypes to improve genotype matrices for tetrad analysis.
• Simulation module: Generates genomes and reads for recombinant tetrads for hypothesis testing and benchmarking.
• Low-depth sequencing support: Operates on low sequencing depths (e.g., 1–2X coverage) for genotyping and simulation studies.
• Batch processing: Processes multiple tetrads or samples in batch for high-throughput studies.
• Structural rearrangement detection with long reads: Enables identification of structural rearrangements when integrated with Oxford Nanopore sequencing data.

Scientific Applications:

• Meiotic recombination mapping: Mapping crossover and gene conversion landscapes from tetrad sequencing data.
• Hypothesis testing and power analysis: Using simulated genomes and reads for recombinant tetrads to evaluate experimental designs and analysis methods.
• Genome instability and structural variation discovery: Detecting structural rearrangements associated with meiosis via integration with Oxford Nanopore sequencing.
• Tetrad sequencing validation: Applied and validated on tetrad sequencing data from Saccharomyces cerevisiae and Chlamydomonas reinhardtii.
• CNV and genotype completeness studies: Characterizing copy number variation and improving genotype completeness in genetic studies of meiosis.

Methodology:

Computational steps explicitly include marker identification, gamete genotyping, profiling meiotic recombination landscapes, copy number variation profiling, missing genotype inference, simulation of genomes and reads for recombinant tetrads, support for reference-based or parental genome assembly analyses, and batch processing; the simulation module and analyses are demonstrated to operate at low sequencing depths (1–2X).