Evaluation of computational programs to predict HLA genotypes from genomic sequencing data | Zendy

Denis C. Bauer | Zendy; Armella Zadoorian | Zendy; Laurence O.W. Wilson | Zendy; Natalie Thorne | Zendy

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Evaluation of computational programs to predict HLA genotypes from genomic sequencing data

Author(s) -

Denis C. Bauer,

Armella Zadoorian,

Laurence O.W. Wilson,

Natalie Thorne

Publication year - 2016

Publication title -

briefings in bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.204

H-Index - 113

eISSN - 1477-4054

pISSN - 1467-5463

DOI - 10.1093/bib/bbw097

Subject(s) - human leukocyte antigen , typing , computer science , in silico , computational biology , exome , dna sequencing , exome sequencing , data mining , biology , genetics , gene , antigen , mutation , speech recognition

Despite being essential for numerous clinical and research applications, high-resolution human leukocyte antigen (HLA) typing remains challenging and laboratory tests are also time-consuming and labour intensive. With next-generation sequencing data becoming widely accessible, on-demand in silico HLA typing offers an economical and efficient alternative.

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