Benchmarking Genomic Variant Calling Tools in Inbred Mouse Strains: Recommendations and Considerations.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Whole-genome sequencing is increasingly affordable, making variant identification a common task.
• Existing variant calling tools often assume outbred genomes, which is unsuitable for inbred laboratory models.
• Optimizing variant calling in inbred organisms requires specialized approaches.

Purpose of the Study:

• To analyze the performance of widely used variant calling tools in simulated inbred mouse genomes.
• To identify optimal strategies for variant calling and filtering in laboratory mice and other inbred strains.
• To provide best practices for accurate genomic variant identification in inbred organisms.

Main Methods:

• Simulation of C57BL/6J and nine non-reference mouse strain genomes.
• Evaluation of multiple widely used variant calling software packages.
• Development of empirical filters to enhance variant calling performance.

Main Results:

• A tradeoff exists between recall and precision for different variant calling tools.
• An ensemble approach, using the intersection of variants from multiple callers, yields an optimal variant call set.
• Strain-specific recommendations and empirical filters improve variant discovery and polymorphism identification.

Conclusions:

• Variant calling in inbred organisms requires careful tool selection and filtering strategies.
• Ensemble methods offer a robust approach to balancing precision and recall.
• This study provides essential guidelines for accurate genomic variant analysis in laboratory mice and other inbred models.