The imgt/HLA database

Anthony Nolan Research Institute, Royal Free Hospital, Pond Street, Hampstead, London NW3 2QG, UK.
Nucleic Acids Research (Impact Factor: 9.11). 11/2010; 39(Database issue):D1171-6. DOI: 10.1093/nar/gkq998
Source: PubMed


It is 12 years since the IMGT/HLA database was first released, providing the HLA community with a searchable repository of
highly curated HLA sequences. The HLA complex is located within the 6p21.3 region of human chromosome 6 and contains more
than 220 genes of diverse function. Many of the genes encode proteins of the immune system and are highly polymorphic. The
naming of these HLA genes and alleles and their quality control is the responsibility of the WHO Nomenclature Committee for
Factors of the HLA System. Through the work of the HLA Informatics Group and in collaboration with the European Bioinformatics
Institute, we are able to provide public access to this data through the web site Regular updates to the web site ensure that new and confirmatory sequences are dispersed to the HLA community, and the wider
research and clinical communities.

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Available from: James Robinson
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    • "II. Athlates for in silico HLA-typing using exome sequence data: We diverged from the recommended Athlates protocol at two points: (1) We performed[27], using BWA with zero mismatches (params : bwa aln -e 0 -o 0 -n 0) instead of NovoAlign[28]After alignments (and optional HLA typing) were completed , somatic mutation detection was performed using the following series of steps (Additional file 1: Figure S1): (1) Samtools[30,31]mpileup v0.1.16 was run with parameters '-A -B' with default setting for the other parameters. "
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    ABSTRACT: Cancer immunotherapy has gained significant momentum from recent clinical successes of checkpoint blockade inhibition. Massively parallel sequence analysis suggests a connection between mutational load and response to this class of therapy. Methods to identify which tumor-specific mutant peptides (neoantigens) can elicit anti-tumor T cell immunity are needed to improve predictions of checkpoint therapy response and to identify targets for vaccines and adoptive T cell therapies. Here, we present a flexible, streamlined computational workflow for identification of personalized Variant Antigens by Cancer Sequencing (pVAC-Seq) that integrates tumor mutation and expression data (DNA- and RNA-Seq). pVAC-Seq is available at
    Preview · Article · Dec 2016 · Genome Medicine
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    • "Alignments for HLA-A, HLA-B, and HLA-C were obtained from the IMGT/HLA Database (Robinson et al. 2013). All dN / dS estimates and related analyses were implemented in CODEML (PAML package, Yang 2007). "
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    ABSTRACT: The classical class I HLA loci of humans show an excess of nonsynonymous with respect to synonymous substitutions at codons of the antigen recognition site (ARS), a hallmark of adaptive evolution. Additionally, high polymporphism, linkage disequilibrium, and disease associations suggest that one or more balancing selection regimes have acted upon these genes. However, several questions about these selective regimes remain open. First, it is unclear if stronger evidence for selection on deep timescales is due to changes in the intensity of selection over time or to a lack of power of most methods to detect selection on recent timescales. Another question concerns the functional entities which define the selected phenotype. While most analyses focus on selection acting on individual alleles, it is also plausible that phylogenetically defined groups of alleles ("lineages") are targets of selection. To address these questions, we analyzed how [Formula: see text] ([Formula: see text]) varies with respect to divergence times between alleles and phylogenetic placement (position of branches). We find that [Formula: see text] for ARS codons of class I HLA genes increases with divergence time and is higher for inter-lineage branches. Throughout our analyses, we used non-selected codons to control for possible effects of inflation of [Formula: see text] associated to intra-specific analysis, and showed that our results are not artifactual. Our findings indicate the importance of considering the timescale effect when analysing [Formula: see text] over a wide spectrum of divergences. Finally, our results support the divergent allele advantage model, whereby heterozygotes with more divergent alleles have higher fitness than those carrying similar alleles.
    Full-text · Article · Jan 2016 · Journal of Molecular Evolution
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    • "( imgt/hla/probe.html) [28] [29] . Luminex 100 flow analyzer identified HLA alleles via HLA visual 1.0 software by referring to HLA typing template data for DRB1 and DQB1 provided by manufacturer (OneLambda, Inc. "
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    DESCRIPTION: Protective effect of HLA-DQB1 alleles against alloimmunization in patients with sickle cell disease
    Full-text · Research · Nov 2015
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