{"id":21976695,"url":"https://github.com/zhengxwen/snprelate","last_synced_at":"2025-04-08T10:33:09.498Z","repository":{"id":17756141,"uuid":"20609725","full_name":"zhengxwen/SNPRelate","owner":"zhengxwen","description":"R package: parallel computing toolset for relatedness and principal component analysis of SNP data (Development version only)","archived":false,"fork":false,"pushed_at":"2024-11-14T21:51:54.000Z","size":18868,"stargazers_count":105,"open_issues_count":42,"forks_count":25,"subscribers_count":12,"default_branch":"master","last_synced_at":"2025-04-01T09:31:23.575Z","etag":null,"topics":["bioinformatics","gds-format","pca","r","simd","snp"],"latest_commit_sha":null,"homepage":"http://www.bioconductor.org/packages/SNPRelate","language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/zhengxwen.png","metadata":{"files":{"readme":"README.md","changelog":"NEWS","contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2014-06-08T05:00:50.000Z","updated_at":"2025-03-21T09:58:48.000Z","dependencies_parsed_at":"2023-01-11T20:27:17.526Z","dependency_job_id":"b7a10b6c-e879-424d-9196-b69033d7f2ee","html_url":"https://github.com/zhengxwen/SNPRelate","commit_stats":{"total_commits":356,"total_committers":9,"mean_commits":39.55555555555556,"dds":0.1713483146067416,"last_synced_commit":"64f86936ca125ff82da60be011756f3d46dcd91e"},"previous_names":[],"tags_count":7,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zhengxwen%2FSNPRelate","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zhengxwen%2FSNPRelate/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zhengxwen%2FSNPRelate/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zhengxwen%2FSNPRelate/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/zhengxwen","download_url":"https://codeload.github.com/zhengxwen/SNPRelate/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247824136,"owners_count":21002212,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bioinformatics","gds-format","pca","r","simd","snp"],"created_at":"2024-11-29T16:11:46.218Z","updated_at":"2025-04-08T10:33:09.459Z","avatar_url":"https://github.com/zhengxwen.png","language":"C++","funding_links":[],"categories":[],"sub_categories":[],"readme":"SNPRelate: Parallel computing toolset for relatedness and principal component analysis of SNP data\n====\n\n![GPLv3](http://www.gnu.org/graphics/gplv3-88x31.png)\n[GNU General Public License, GPLv3](http://www.gnu.org/copyleft/gpl.html)\n\n[![Availability](http://www.bioconductor.org/shields/availability/release/SNPRelate.svg)](http://www.bioconductor.org/packages/release/bioc/html/SNPRelate.html)\n[![Years-in-BioC](http://www.bioconductor.org/shields/years-in-bioc/SNPRelate.svg)](http://www.bioconductor.org/packages/release/bioc/html/SNPRelate.html)\n[![R](https://github.com/zhengxwen/SNPRelate/actions/workflows/r.yml/badge.svg)](https://github.com/zhengxwen/SNPRelate/actions/workflows/r.yml)\n\n\n## Features\n\nGenome-wide association studies are widely used to investigate the genetic basis of diseases and traits, but they pose many computational challenges. We developed SNPRelate (R package for multi-core symmetric multiprocessing computer architectures) to accelerate two key computations on SNP data: principal component analysis (PCA) and relatedness analysis using identity-by-descent measures. The kernels of our algorithms are written in C/C++ and highly optimized.\n\nThe GDS format offers the efficient operations specifically designed for integers with two bits, since a SNP could occupy only two bits. The SNP GDS format in this package is also used by the [GWASTools](http://bioconductor.org/packages/GWASTools) package with the support of S4 classes and generic functions. The extended GDS format is implemented in the [SeqArray](https://github.com/zhengxwen/SeqArray) package to support the storage of single nucleotide variation (SNV), insertion/deletion polymorphism (indel) and structural variation calls. It is strongly suggested to use [SeqArray](https://github.com/zhengxwen/SeqArray) for large-scale whole-exome and whole-genome sequencing variant data instead of [SNPRelate](https://github.com/zhengxwen/SNPRelate).\n\n\n## Bioconductor\n\nRelease Version: v1.40.0\n\n[http://www.bioconductor.org/packages/SNPRelate](http://www.bioconductor.org/packages/SNPRelate)\n\n\n## News\n\n* See [package news](NEWS).\n\n\n## Tutorials\n\n[http://www.bioconductor.org/packages/release/bioc/vignettes/SNPRelate/inst/doc/SNPRelate.html](http://www.bioconductor.org/packages/release/bioc/vignettes/SNPRelate/inst/doc/SNPRelate.html)\n\n\n## Citations\n\nZheng X, Levine D, Shen J, Gogarten SM, Laurie C, Weir BS (2012). A High-performance Computing Toolset for Relatedness and Principal Component Analysis of SNP Data. *Bioinformatics*. [DOI: 10.1093/bioinformatics/bts606](http://dx.doi.org/10.1093/bioinformatics/bts606).\n\nZheng X, Gogarten S, Lawrence M, Stilp A, Conomos M, Weir BS, Laurie C, Levine D (2017). SeqArray -- A storage-efficient high-performance data format for WGS variant calls. *Bioinformatics*. [DOI: 10.1093/bioinformatics/btx145](http://dx.doi.org/10.1093/bioinformatics/btx145).\n\n\n## Installation\n\n* Bioconductor repository:\n```R\nif (!requireNamespace(\"BiocManager\", quietly=TRUE))\n    install.packages(\"BiocManager\")\nBiocManager::install(\"SNPRelate\")\n```\n\n* Development version from Github (for developers/testers only):\n```R\nlibrary(\"devtools\")\ninstall_github(\"zhengxwen/gdsfmt\")\ninstall_github(\"zhengxwen/SNPRelate\")\n```\nThe `install_github()` approach requires that you build from source, i.e. `make` and compilers must be installed on your system -- see the [R FAQ](https://cran.r-project.org/faqs.html) for your operating system; you may also need to install dependencies manually.\n\n\n\n## Implementation with Intel Intrinsics\n\n| Functions             | No SIMD | SSE2 | AVX | AVX2 | AVX-512 |\n|:----------------------|:-------:|:----:|:---:|:----:|:-------:|\n| snpgdsDiss [»](https://rdrr.io/bioc/SNPRelate/man/snpgdsDiss.html)                    | X |\n| snpgdsEIGMIX [»](https://rdrr.io/bioc/SNPRelate/man/snpgdsEIGMIX.html)                 | X | X | X |\n| snpgdsGRM [»](https://rdrr.io/bioc/SNPRelate/man/snpgdsGRM.html)                       | X | X | X | . |\n| snpgdsIBDKING [»](https://rdrr.io/bioc/SNPRelate/man/snpgdsIBDKING.html)               | X | X |   | X |\n| snpgdsIBDMoM [»](https://rdrr.io/bioc/SNPRelate/man/snpgdsIBDMoM.html)                 | X |\n| snpgdsIBS [»](https://rdrr.io/bioc/SNPRelate/man/snpgdsIBS.html)                       | X | X |\n| snpgdsIBSNum [»](https://rdrr.io/bioc/SNPRelate/man/snpgdsIBSNum.html)                 | X | X |\n| snpgdsIndivBeta [»](https://rdrr.io/bioc/SNPRelate/man/snpgdsIndivBeta.html)           | X | X | P | X |\n| snpgdsPCA [»](https://rdrr.io/bioc/SNPRelate/man/snpgdsPCA.html)                       | X | X | X |\n| snpgdsPCACorr [»](https://rdrr.io/bioc/SNPRelate/man/snpgdsPCACorr.html)               | X |\n| snpgdsPCASampLoading [»](https://rdrr.io/bioc/SNPRelate/man/snpgdsPCASampLoading.html) | X |\n| snpgdsPCASNPLoading [»](https://rdrr.io/bioc/SNPRelate/man/snpgdsPCASNPLoading.html)   | X |\n| [...](http://rdrr.io/bioc/SNPRelate/man) |\n\n`X: fully supported;  .: partially supported;  P: POPCNT instruction.`\n\n\n### Install the package from the source code with the support of Intel SIMD Intrinsics:\n\nYou have to customize the package compilation, see: [CRAN: Customizing-package-compilation](https://cran.r-project.org/doc/manuals/r-release/R-admin.html#Customizing-package-compilation)\n\nChange `~/.R/Makevars` to, assuming GNU Compilers (gcc/g++) or Clang compiler (clang++) are installed:\n```sh\n## for C code\nCFLAGS=-g -O3 -march=native -mtune=native\n## for C++ code\nCXXFLAGS=-g -O3 -march=native -mtune=native\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzhengxwen%2Fsnprelate","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fzhengxwen%2Fsnprelate","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzhengxwen%2Fsnprelate/lists"}