HLA Genotype Imputation with Attribute Bagging

Kernel Version: v1.4

Features

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.

Bioconductor Package

Release Version: http://www.bioconductor.org/packages/release/bioc/html/HIBAG.html

Development Version: http://www.bioconductor.org/packages/devel/bioc/html/HIBAG.html

Changes in Bioconductor Version (since v1.14.0):

Kernel Version: v1.4
modify the kernel to support the GPU extension
develop a complementary R package (HIBAG.gpu) for GPU computing
The kernel v1.4 outputs exactly the same parameter estimates as v1.3, and the model training with v1.4 is 1.2 times faster than v1.3

Changes in Bioconductor Version (since v1.3.0):

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

Package Maintainer

Dr. Xiuwen Zheng (zhengx@u.washington.edu)

Pre-fit Model Download

http://zhengxwen.github.io/HIBAG/hibag_index.html
platform-specific models: http://zhengxwen.github.io/HIBAG/platforms.html

Citation

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