HIBAG
R package – HLA Genotype Imputation with Attribute Bagging (development version only)
Science Score: 26.0%
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○CITATION.cff file
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○.zenodo.json file
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✓DOI references
Found 7 DOI reference(s) in README -
○Academic publication links
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○Scientific vocabulary similarity
Low similarity (10.6%) to scientific vocabulary
Keywords
Repository
R package – HLA Genotype Imputation with Attribute Bagging (development version only)
Basic Info
- Host: GitHub
- Owner: zhengxwen
- Language: C++
- Default Branch: master
- Homepage: https://hibag.s3.amazonaws.com/index.html
- Size: 39.9 MB
Statistics
- Stars: 30
- Watchers: 5
- Forks: 7
- Open Issues: 14
- Releases: 7
Topics
Metadata Files
README.md
HLA Genotype Imputation with Attribute Bagging
Kernel Version: 1.5
GNU General Public License, GPLv3
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: 1.42.0
http://www.bioconductor.org/packages/HIBAG/
Changes in Bioconductor Version (since v1.26.0, Y2020):
- 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
Changes in Bioconductor Version (since v1.14.0, Y2017):
- 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
Changes in Bioconductor Version (since v1.3.0, Y2013):
- 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 Author & Maintainer
Dr. Xiuwen Zheng
Pre-fit Model Download
- https://hibag.s3.amazonaws.com/index.html
- Platform-specific HLARES models: https://hibag.s3.amazonaws.com/hlares_index.html
- A backup webpage: https://zhengxwen.github.io/HIBAG/
Citation
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
Installation
Bioconductor repository:
R source("http://bioconductor.org/biocLite.R") biocLite("HIBAG")Development version from Github (for developers/testers only):
R library("devtools") install_github("zhengxwen/HIBAG")Theinstall_github()approach requires that you build from source, i.e.makeand compilers must be installed on your system -- see the R FAQ for your operating system; you may also need to install dependencies manually.
Acceleration
CPU with Intel Intrinsics
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.
GPU with OpenCL
- HIBAG.gpu, requiring HIBAG (>= v1.28.0).
Archive
Owner
- Name: Xiuwen Zheng
- Login: zhengxwen
- Kind: user
- Location: Chicago
- Repositories: 13
- Profile: https://github.com/zhengxwen
GitHub Events
Total
- Watch event: 2
- Push event: 3
Last Year
- Watch event: 2
- Push event: 3
Committers
Last synced: 9 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Xiuwen Zheng | z****n@g****m | 472 |
| Bioconductor Git-SVN Bridge | b****c@b****g | 2 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 9 months ago
All Time
- Total issues: 27
- Total pull requests: 2
- Average time to close issues: 3 months
- Average time to close pull requests: 16 minutes
- Total issue authors: 23
- Total pull request authors: 1
- Average comments per issue: 1.81
- Average comments per pull request: 0.0
- Merged pull requests: 2
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 3
- Pull requests: 0
- Average time to close issues: 27 days
- Average time to close pull requests: N/A
- Issue authors: 2
- Pull request authors: 0
- Average comments per issue: 0.67
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- JingjingBai2021 (3)
- oalavijeh (2)
- adiamb (2)
- SunYidan2021 (1)
- longbao738 (1)
- mukhtarsadykov (1)
- xiw588 (1)
- shubhamsaini (1)
- karpat90 (1)
- seagullOnABrick (1)
- mcjmigdal (1)
- cbrunoels (1)
- sidtjn (1)
- rcanovas (1)
- rwanwork (1)
Pull Request Authors
- zhengxwen (2)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- bioconductor 27,507 total
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 7
- Total maintainers: 1
bioconductor.org: HIBAG
HLA Genotype Imputation with Attribute Bagging
- Homepage: https://github.com/zhengxwen/HIBAG https://hibag.s3.amazonaws.com/index.html
- Documentation: https://bioconductor.org/packages/release/bioc/vignettes/HIBAG/inst/doc/HIBAG.pdf
- License: GPL-3
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Latest release: 1.44.0
published 10 months ago