README.md
In HIBAG: HLA Genotype Imputation with Attribute Bagging

HLA Genotype Imputation with Attribute Bagging

Kernel Version: 1.5

HIBAG is a state of the art software package for imputing HLA types using SNP data, and it relies on a training set of HLA and SNP genotypes. HIBAG can be used by researchers with published parameter estimates 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 using bootstrap aggregating and random variable selection.

Release Version: 1.26.1

http://www.bioconductor.org/packages/HIBAG/

Kernel Version: v1.5
The kernel v1.5 generates the same training model as v1.4, but 2-6x faster, by taking advantage of Intel AVX, AVX2 and AVX512 intrinsics if available

Kernel Version: v1.4
The kernel v1.4 outputs exactly the same model parameter estimates as v1.3, and the model training with v1.4 is 1.2 times faster than v1.3
Modify the kernel to support the GPU extension

Kernel Version: v1.3
Optimize the calculation of hamming distance using SSE2 and hardware POPCNT instructions if available
Hardware POPCNT: 2.4x speedup for large-scale data, compared to the implementation in v1.2.4
SSE2 popcount implementation without hardware POPCNT: 1.5x speedup for large-scale data, compared to the implementation in v1.2.4

Dr. Xiuwen Zheng

https://hibag.s3.amazonaws.com/index.html
Platform-specific HLARES models: https://hibag.s3.amazonaws.com/hlares_index.html
http://www.biostat.washington.edu/~bsweir/HIBAG/

Zheng, X. et al. HIBAG-HLA genotype imputation with attribute bagging. Pharmacogenomics Journal 14, 192-200 (2014). doi: 10.1038/tpj.2013.18

Zheng, X. (2018) Imputation-Based HLA Typing with SNPs in GWAS Studies. In: Boegel S. (eds) HLA Typing. Methods in Molecular Biology, Vol 1802. Humana Press, New York, NY. doi: 10.1007/978-1-4939-8546-3_11

Bioconductor repository:

source("http://bioconductor.org/biocLite.R")
biocLite("HIBAG")

Development version from Github (for developers/testers only):

library("devtools")
install_github("zhengxwen/HIBAG")

The install_github() approach requires that you build from source, i.e. make and compilers must be installed on your system -- see the R FAQ for your operating system; you may also need to install dependencies manually.

GCC (>= v6.0) is strongly recommended to compile the HIBAG package (Intel ICC is not suggested).
HIBAG::hlaSetKernelTarget("max") can be used to maximize the algorithm efficiency.