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, posing challenges for inbred laboratory models.
• Performance of variant calling tools varies, necessitating evaluation for specific research contexts.

Purpose of the Study:

• To analyze the performance of variant calling tools in simulated inbred mouse genomes.
• To identify best practices for variant calling and filtering in inbred organisms.
• To provide strain-specific recommendations for variant detection.

Main Methods:

• Evaluation of five variant calling tools using simulated genomes from ten diverse inbred mouse strains.
• Analysis of tool performance in terms of recall and precision.
• Development and application of filtering strategies to improve variant call sets.

Main Results:

• Variant detection, recall, and precision differ significantly across tools and mouse strains.
• An ensemble approach combining multiple variant callers enhances confidence in mutation detection.
• Specific filters improve performance for both rare variant discovery and strain polymorphism identification.

Conclusions:

• Best practices for variant calling in inbred organisms, especially laboratory mice, have been established.
• Strain-specific considerations and analytical goals influence optimal variant calling tool selection.
• An ensemble strategy is recommended for obtaining an optimal variant call set in inbred genomes.