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Inbred Strain Variant Database (ISVdb): A Repository for Probabilistically Informed Sequence Differences Among the
Daniel Oreper1,2, Yanwei Cai1,2, Lisa M Tarantino2,3
1Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, North Carolina 27599-7265.
The Inbred Strain Variant Database (ISVdb) provides crucial genetic information for Collaborative Cross (CC) mouse strains. This resource simplifies accessing and analyzing complex genetic data for researchers, improving experimental design.
Area of Science:
- Genetics
- Bioinformatics
- Mouse Models
Background:
- The Collaborative Cross (CC) is a vital multiparental recombinant inbred mouse strain panel.
- Understanding genetic variation between CC strains is essential for experimental design and analysis.
- Publicly available whole-genome sequencing data for individual CC strains was previously lacking.
Purpose of the Study:
- To develop a standardized and accessible resource for CC strain genetic information.
- To provide imputed sequence data for CC strains by combining founder sequencing and genotyping array data.
- To reduce the analytical burden on researchers utilizing the CC resource.
Main Methods:
- Developed the Inbred Strain Variant Database (ISVdb) using an SQL database.
- Integrated exonic variants from the Sanger Institute mouse sequencing dataset.
- Combined probabilistic haplotype estimates from a custom genotyping array with founder variant calls for imputation.
Main Results:
- ISVdb offers direct sequence information for CC founders and imputed sequence data for CC strains.
- The database includes predicted variant consequence metadata.
- ISVdb supports rapid simulation of F1 populations and preserves imputation uncertainty for future refinement.
Conclusions:
- ISVdb provides a standardized, easily accessible online interface (http://isvdb.unc.edu) for CC genetic data.
- The database significantly reduces the complexity and potential for error in analyzing CC strain genetic differences.
- ISVdb facilitates more effective experimental design and analysis using the Collaborative Cross mouse model.
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