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RAbHIT: R Antibody Haplotype Inference Tool.
Ayelet Peres1, Moriah Gidoni1, Pazit Polak1
1Faculty of Engineering, Bar Ilan University, Ramat Gan 5290002, Israel.
Bioinformatics (Oxford, England)
|June 8, 2019
Summary
We developed RAbHIT, a novel tool for antibody haplotype inference using a Bayesian framework. This method aids in identifying genetic predispositions to diseases by analyzing antibody gene sequences.
Area of Science:
- Genomics
- Immunology
- Bioinformatics
Background:
- Antibody haplotype inference is crucial for understanding genetic disease predispositions.
- Challenges in sequencing highly repetitive antibody gene loci hinder accurate inference.
- Analyzing rearranged V(D)J sequences offers a promising approach.
Purpose of the Study:
- To present RAbHIT (R Haplotype Antibody Inference Tool), a novel algorithm for V(D)J haplotype inference.
- To enable the inference of antibody gene haplotypes and detect gene deletions.
- To provide a tool applicable to diverse B-cell types and sequencing protocols.
Main Methods:
- Developed a novel algorithm adapting a Bayesian framework for V(D)J haplotype inference.
- Implemented the RAbHIT tool in R, available via CRAN.
- Algorithm designed to handle challenges of repetitive genomic loci.
Main Results:
- RAbHIT successfully infers V(D)J haplotypes.
- The tool can also identify gene deletions within antibody loci.
- Demonstrated applicability to both naïve and non-naïve B-cell sequences.
Conclusions:
- RAbHIT offers a powerful computational approach for antibody haplotype inference.
- This tool has potential clinical implications for identifying genetic disease predispositions.
- RAbHIT is a versatile and accessible tool for immunological and genomic research.
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