RecombineX: A generalized computational framework for automatic high-throughput gamete genotyping and tetrad-based...

Area of Science:

• Genetics and Genomics
• Computational Biology
• Molecular Biology

Background:

• Meiotic recombination is crucial for accurate chromosome segregation and genetic diversity.
• Tetrad analysis offers a detailed view of meiotic products but often requires complex computational tools.

Purpose of the Study:

• To introduce RecombineX, a generalized computational framework for automating tetrad analysis.
• To enable comprehensive profiling of meiotic recombination landscapes and genetic variations.
• To provide a robust tool for analyzing tetrad sequencing data across diverse organisms.

Main Methods:

• Developed RecombineX, a computational framework with batch processing capabilities.
• Implemented reference-based and parental genome assembly-based analysis.
• Included modules for copy number variation profiling, missing genotype inference, and tetrad genome simulation.
• Validated RecombineX using tetrad sequencing data from Saccharomyces cerevisiae and Chlamydomonas reinhardtii.

Main Results:

• RecombineX automates marker identification, gamete genotyping, and recombination profiling.
• The framework demonstrates accuracy and robustness even with low sequencing depths (1-2X).
• Analysis of budding yeast data revealed two previously undetected structural rearrangement events.

Conclusions:

• RecombineX is a versatile, one-stop solution for tetrad analysis in diverse organisms.
• The tool enhances downstream analysis and facilitates hypothesis testing through simulation.
• RecombineX aids in understanding genome instability during meiosis.