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An Automated Method To Predict Mouse Gene and Protein Sequences Using Variant Data
Peter Dornbos1,2, Anooj A Arkatkar1, John J LaPres3,2
1Department of Biochemistry and Molecular Biology and.
G3 (Bethesda, Md.)
|January 9, 2020
Summary
This study introduces a new tool that predicts gene and protein sequences for diverse mouse strains using genetic variant data. This software aids researchers in understanding complex traits by providing essential genomic information for under-sequenced mouse models.
Area of Science:
- Genomics and Bioinformatics
- Comparative Genomics
- Model Organism Research
Background:
- Advances in sequencing technologies enable genetic basis exploration for complex phenotypes.
- Genomes of many genetically distinct mouse strains (Mus musculus) remain incompletely sequenced.
- Understanding genetic variation across mouse strains is crucial for biomedical research.
Purpose of the Study:
- To develop a computational tool for predicting gene, mRNA, and protein sequences in mouse strains.
- To leverage single-nucleotide polymorphism (SNP) and insertion-deletion (indel) data for sequence prediction.
- To facilitate genomic analysis for up to 36 genetically distinct mouse strains.
Main Methods:
- Developed a software tool with a graphical interface for automated database querying.
- Utilized single-nucleotide polymorphism (SNP) and insertion-deletion (indel) data as input.
- Predicted gene and amino acid sequences for the aryl hydrocarbon receptor (Ahr) in mouse strains.
Main Results:
- The tool successfully predicted gene and protein sequences for multiple mouse strains.
- Predictions were validated by comparison with fully sequenced genomes, demonstrating high accuracy.
- The software requires minimal computational expertise and no prior data input.
Conclusions:
- The developed tool is effective for predicting gene and protein sequences in genetically diverse mouse strains.
- This resource can significantly aid researchers studying complex phenotypes and genetic variation in mice.
- The software lowers the barrier to entry for utilizing genomic data in mouse model research.
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