%global _empty_manifest_terminate_build 0 Name: python-fastlmm Version: 0.6.5 Release: 1 Summary: Fast GWAS License: Apache 2.0 URL: https://fastlmm.github.io/ Source0: https://mirrors.nju.edu.cn/pypi/web/packages/80/42/5a96824c1175f63eddc2b8651ec362992002d0aaf2e72038981421b7309a/fastlmm-0.6.5.tar.gz BuildArch: noarch %description FaST-LMM, which stands for Factored Spectrally Transformed Linear Mixed Models, is a program for performing genome-wide association studies (GWAS) on datasets of all sizes, up to one millions samples. This release contains the following features, each illustrated with an IPython notebook. * Core FaST-LMM ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/FaST-LMM.ipynb)) -- [Lippert *et al.*, *Nature Methods* 2011](http://www.nature.com/nmeth/journal/v8/n10/abs/nmeth.1681.html) Improvements: * New features for single_snp (including effect size and multiple phenotype support) and epistasis (including reporting beta and using pre-computed eigenvalue decompositions) ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/fastlmm2021.ipynb)) -- [Lippert *et al.*, *Nature Methods* 2011](http://www.nature.com/nmeth/journal/v8/n10/abs/nmeth.1681.html) * Ludicrous-Speed GWAS ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/SingleSnpScale.ipynb)) -- [Kadie and Heckerman, *bioRxiv* 2018](https://www.biorxiv.org/content/10.1101/154682v2) * Heritability with Spatial Correction ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/heritability_si.ipynb)), [Heckerman *et al.*, *PNAS* 2016](http://www.pnas.org/content/113/27/7377.abstract) * Two Kernels ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/FaST-LMM.ipynb)) -- [Widmer *et al.*, *Scientific Reports* 2014](http://www.nature.com/srep/2014/141112/srep06874/full/srep06874.html) * Set Analysis ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/FaST-LMM.ipynb)) -- [Lippert *et al.*, *Bioinformatics* 2014](http://bioinformatics.oxfordjournals.org/content/early/2014/09/07/bioinformatics.btu504) * Epistasis ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/FaST-LMM.ipynb)) -- [Lippert *et al.*, *Scientific Reports,* 2013](http://www.nature.com/srep/2013/130122/srep01099/full/srep01099.html) * Prediction ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/FaST-LMM.ipynb)) -- [Lippert *et al.*, *Nature Methods* 2011](http://www.nature.com/nmeth/journal/v8/n10/abs/nmeth.1681.html) *A C++ version, which is generally less functional, is available. See http://fastlmm.github.io/.* %package -n python3-fastlmm Summary: Fast GWAS Provides: python-fastlmm BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-fastlmm FaST-LMM, which stands for Factored Spectrally Transformed Linear Mixed Models, is a program for performing genome-wide association studies (GWAS) on datasets of all sizes, up to one millions samples. This release contains the following features, each illustrated with an IPython notebook. * Core FaST-LMM ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/FaST-LMM.ipynb)) -- [Lippert *et al.*, *Nature Methods* 2011](http://www.nature.com/nmeth/journal/v8/n10/abs/nmeth.1681.html) Improvements: * New features for single_snp (including effect size and multiple phenotype support) and epistasis (including reporting beta and using pre-computed eigenvalue decompositions) ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/fastlmm2021.ipynb)) -- [Lippert *et al.*, *Nature Methods* 2011](http://www.nature.com/nmeth/journal/v8/n10/abs/nmeth.1681.html) * Ludicrous-Speed GWAS ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/SingleSnpScale.ipynb)) -- [Kadie and Heckerman, *bioRxiv* 2018](https://www.biorxiv.org/content/10.1101/154682v2) * Heritability with Spatial Correction ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/heritability_si.ipynb)), [Heckerman *et al.*, *PNAS* 2016](http://www.pnas.org/content/113/27/7377.abstract) * Two Kernels ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/FaST-LMM.ipynb)) -- [Widmer *et al.*, *Scientific Reports* 2014](http://www.nature.com/srep/2014/141112/srep06874/full/srep06874.html) * Set Analysis ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/FaST-LMM.ipynb)) -- [Lippert *et al.*, *Bioinformatics* 2014](http://bioinformatics.oxfordjournals.org/content/early/2014/09/07/bioinformatics.btu504) * Epistasis ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/FaST-LMM.ipynb)) -- [Lippert *et al.*, *Scientific Reports,* 2013](http://www.nature.com/srep/2013/130122/srep01099/full/srep01099.html) * Prediction ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/FaST-LMM.ipynb)) -- [Lippert *et al.*, *Nature Methods* 2011](http://www.nature.com/nmeth/journal/v8/n10/abs/nmeth.1681.html) *A C++ version, which is generally less functional, is available. See http://fastlmm.github.io/.* %package help Summary: Development documents and examples for fastlmm Provides: python3-fastlmm-doc %description help FaST-LMM, which stands for Factored Spectrally Transformed Linear Mixed Models, is a program for performing genome-wide association studies (GWAS) on datasets of all sizes, up to one millions samples. This release contains the following features, each illustrated with an IPython notebook. * Core FaST-LMM ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/FaST-LMM.ipynb)) -- [Lippert *et al.*, *Nature Methods* 2011](http://www.nature.com/nmeth/journal/v8/n10/abs/nmeth.1681.html) Improvements: * New features for single_snp (including effect size and multiple phenotype support) and epistasis (including reporting beta and using pre-computed eigenvalue decompositions) ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/fastlmm2021.ipynb)) -- [Lippert *et al.*, *Nature Methods* 2011](http://www.nature.com/nmeth/journal/v8/n10/abs/nmeth.1681.html) * Ludicrous-Speed GWAS ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/SingleSnpScale.ipynb)) -- [Kadie and Heckerman, *bioRxiv* 2018](https://www.biorxiv.org/content/10.1101/154682v2) * Heritability with Spatial Correction ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/heritability_si.ipynb)), [Heckerman *et al.*, *PNAS* 2016](http://www.pnas.org/content/113/27/7377.abstract) * Two Kernels ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/FaST-LMM.ipynb)) -- [Widmer *et al.*, *Scientific Reports* 2014](http://www.nature.com/srep/2014/141112/srep06874/full/srep06874.html) * Set Analysis ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/FaST-LMM.ipynb)) -- [Lippert *et al.*, *Bioinformatics* 2014](http://bioinformatics.oxfordjournals.org/content/early/2014/09/07/bioinformatics.btu504) * Epistasis ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/FaST-LMM.ipynb)) -- [Lippert *et al.*, *Scientific Reports,* 2013](http://www.nature.com/srep/2013/130122/srep01099/full/srep01099.html) * Prediction ([notebook](https://nbviewer.jupyter.org/github/fastlmm/FaST-LMM/blob/master/doc/ipynb/FaST-LMM.ipynb)) -- [Lippert *et al.*, *Nature Methods* 2011](http://www.nature.com/nmeth/journal/v8/n10/abs/nmeth.1681.html) *A C++ version, which is generally less functional, is available. See http://fastlmm.github.io/.* %prep %autosetup -n fastlmm-0.6.5 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-fastlmm -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Thu May 18 2023 Python_Bot - 0.6.5-1 - Package Spec generated