OGRDB
OGRDB curates inferred germline receptor gene sequences from AIRR-seq datasets to expand and improve germline gene reference sets for analysis of adaptive immune receptor repertoires.
Key Features:
- Comprehensive Germline Gene Reference Sets: Provides an extensive repository capturing diversity and polymorphism in human and animal germline receptor genes to supplement existing reference sets.
- Community-Driven Expert Review: Implements a community approach for expert review and publication of inferred germline sequences with supporting evidence.
- Support for V- and J- Sequence Inferences: Accepts sequence inferences from human B-cell receptor (BCR) light and heavy chains with a focus on variable (V) and joining (J) segments.
- Integration with AIRR-seq Data Analysis: Leverages tools and methods that infer gene sequences from AIRR-seq datasets to improve interpretation of complex genomic regions affected by multiple repeats, insertions, and deletions.
Scientific Applications:
- Immune response research: Enables analysis of adaptive immune receptor repertoires from AIRR-seq to study immune responses to diseases and the development of immune disorders.
- Immunogenetics: Provides more complete and representative germline gene sets for studying germline variability across populations.
- Vaccine development: Improves inference of receptor repertoires relevant to vaccine antigenicity and immune correlates.
- Personalized medicine: Supports individualized repertoire analysis and interpretation by supplying expanded germline references.
Methodology:
Inference of germline V and J gene sequences from AIRR-seq datasets using advanced bioinformatics tools that address challenges from repeats, insertions, and deletions.
Topics
Details
- Tool Type:
- web application
- Added:
- 11/14/2019
- Last Updated:
- 1/4/2021
Operations
Publications
Lees W, Busse CE, Corcoran M, Ohlin M, Scheepers C, Matsen FA, Yaari G, Watson CT, Collins A, Shepherd AJ. OGRDB: a reference database of inferred immune receptor genes. Nucleic Acids Research. 2019;48(D1):D964-D970. doi:10.1093/nar/gkz822. PMID:31566225. PMCID:PMC6943078.
DOI: 10.1093/NAR/GKZ822
PMID: 31566225
Funding: - National Institutes of Health: R21AI142590, R24AI138963, U01AI136677
- Swedish Research Council: 2016-01720