HIBAG is a state of the art software package for imputing HLA types using SNP data, and it uses the R statistical programming language. HIBAG is highly accurate, computationally tractable, and can be used by researchers with published parameter estimates (provided for subjects of European, Asian, Hispanic and African ancestries) instead of requiring access to large training sample datasets. It combines the concepts of attribute bagging, an ensemble classifier method, with haplotype inference for SNPs and HLA types. Attribute bagging is a technique which improves the accuracy and stability of classifier ensembles deduced using bootstrap aggregating and random subsets of variables.
HLAminer is a software for HLA class I predictions from next-generation shotgun (NGS) sequence read data that supports direct read alignment (HPRA) and targeted assembly of sequence reads (HPTASR).
HLA-IMPUTER implements the HIBAG algorithm for HLA allele imputation with different population specific reference panels, including a new Han Chinese reference panel derived from 10,689 samples.
DHLAS (database HLA system) is a user-friendly, web-based information system for the analysis of human leukocyte antigens (HLA) data from population studies.