Identification of a novel HLA‐B allele, HLA‐B*3580, with possible implication in transplantation and CTL response

Aureli, Anna; Liberatore, G.; Papola, F.; Adorno, D.; Piancatelli, D.

doi:10.1111/j.1399-0039.2007.00970.x

Cited by 11 publications

(3 citation statements)

References 5 publications

(4 reference statements)

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“…Human Leukocyte Antigen (HLA) locus on chromosome 6 encodes a number of highly polymorphic genes that play an essential role in immune system responses (de Bakker et al 2006;Lechler and Warrens 2000;Marsh et al 2010a, b;Aureli et al 2008;Orozco et al 2008;Sampaio-Barros et al 2008;Robinson et al 2003Robinson et al , 2009). In particular, Major Histocompatibility Complex (MHC) class I and class II genes, including HLA-A, -B, -C and DRB1, DQB1, DPB1, respectively, are well known components of the immune system.…”

Section: Prediction Of Hla Alleles From Snp Datamentioning

confidence: 99%

“…In particular, Major Histocompatibility Complex (MHC) class I and class II genes, including HLA-A, -B, -C and DRB1, DQB1, DPB1, respectively, are well known components of the immune system. Consequently, HLA loci and their haplotypes are an important component of disease association studies in the context of JIA and other autoimmune diseases (Aureli et al 2008;Orozco et al 2008;Sampaio-Barros et al 2008). The latter aspect of their function contributes to the role of MHC region as an important susceptibility locus in autoimmune diseases (de Bakker et al 2006;Lechler and Warrens 2000).…”

Section: Prediction Of Hla Alleles From Snp Datamentioning

confidence: 99%

“…19.3) concerns the prediction of Human Leukocyte Antigen (HLA) alleles (Lechler and Warrens 2000;Marsh et al 2010aMarsh et al , 2010bAureli et al 2008;Orozco et al 2008;Sampaio-Barros et al 2008;Robinson et al 2003Robinson et al , 2009 from SNP data. 19.3) concerns the prediction of Human Leukocyte Antigen (HLA) alleles (Lechler and Warrens 2000;Marsh et al 2010aMarsh et al , 2010bAureli et al 2008;Orozco et al 2008;Sampaio-Barros et al 2008;Robinson et al 2003Robinson et al , 2009 from SNP data.…”

Section: Introductionmentioning

confidence: 99%

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Pediatric Biomedical Informatics

Hutton¹

2012

Translational Bioinformatics

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Aims and ScopeThe Book Series in Translational Bioinformatics is a powerful and integrative resource for understanding and translating discoveries and advances of genomic, transcriptomic, proteomic and bioinformatic technologies into the study of human diseases. The Series represents leading global opinions on the translation of bioinformatics sciences into both the clinical setting and descriptions to medical informatics. It presents the critical evidence to further understand the molecular mechanisms underlying organ or cell dysfunctions in human diseases, the results of genomic, transcriptomic, proteomic and bioinformatic studies from human tissues dedicated to the discovery and validation of diagnostic and prognostic disease biomarkers, essential information on the identification and validation of novel drug targets and the application of tissue genomics, transcriptomics, proteomics and bioinformatics in drug efficacy and toxicity in clinical research.The Book Series in Translational Bioinformatics focuses on outstanding articles/chapters presenting significant recent works in genomic, transcriptomic, proteomic and bioinformatic profiles related to human organ or cell dysfunctions and clinical findings. The Series includes bioinformatics-driven molecular and cellular disease mechanisms, the understanding of human diseases and the improvement of patient prognoses. Additionally, it provides practical and useful study insights into and protocols of design and methodology. Series DescriptionTranslational bioinformatics is defined as the development of storage-related, analytic, and interpretive methods to optimize the transformation of increasingly voluminous biomedical data, and genomic data in particular, into proactive, predictive, preventive, and participatory health. Translational bioinformatics includes research on the development of novel techniques for the integration of biological and clinical data and the evolution of clinical informatics methodology to encompass biological observations. The end product of translational bioinformatics is the newly found knowledge from these integrative efforts that can be disseminated to a variety of stakeholders including biomedical scientists, clinicians, and patients. Issues related to database management, administration, or policy will be coordinated through the clinical research informatics domain. Analytic, storage-related, and interpretive methods should be used to improve predictions, early diagnostics, severity monitoring, therapeutic effects, and the prognosis of human diseases.

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