%global _empty_manifest_terminate_build 0 Name: python-pyprophet Version: 2.2.5 Release: 1 Summary: PyProphet: Semi-supervised learning and scoring of OpenSWATH results. License: BSD URL: https://github.com/PyProphet/pyprophet Source0: https://mirrors.nju.edu.cn/pypi/web/packages/75/8d/f2a298f3ec9dff4703c1897a329911038ff656e4d33f9fb4efc457af837d/pyprophet-2.2.5.tar.gz BuildArch: noarch %description PyProphet: Semi-supervised learning and scoring of OpenSWATH results. PyProphet is a Python re-implementation of the mProphet algorithm [1] optimized for SWATH-MS data acquired by data-independent acquisition (DIA). The algorithm was originally published in [2] and has since been extended to support new data types and analysis modes [3,4]. Please consult the [OpenSWATH website](http://openswath.org) for usage instructions and help. 1. Reiter L, Rinner O, Picotti P, Hüttenhain R, Beck M, Brusniak MY, Hengartner MO, Aebersold R. *mProphet: automated data processing and statistical validation for large-scale SRM experiments.* **Nat Methods.** 2011 May;8(5):430-5. [doi: 10.1038/nmeth.1584.](http://dx.doi.org/10.1038/nmeth.1584) Epub 2011 Mar 20. 2. Teleman J, Röst HL, Rosenberger G, Schmitt U, Malmström L, Malmström J, Levander F. *DIANA--algorithmic improvements for analysis of data-independent acquisition MS data.* **Bioinformatics.** 2015 Feb 15;31(4):555-62. [doi: 10.1093/bioinformatics/btu686.](http://dx.doi.org/10.1093/bioinformatics/btu686) Epub 2014 Oct 27. 3. Rosenberger G, Liu Y, Röst HL, Ludwig C, Buil A, Bensimon A, Soste M, Spector TD, Dermitzakis ET, Collins BC, Malmström L, Aebersold R. *Inference and quantification of peptidoforms in large sample cohorts by SWATH-MS.* **Nat Biotechnol** 2017 Aug;35(8):781-788. [doi: 10.1038/nbt.3908.](http://dx.doi.org/10.1038/nbt.3908) Epub 2017 Jun 12. 4. Rosenberger G, Bludau I, Schmitt U, Heusel M, Hunter CL, Liu Y, MacCoss MJ, MacLean BX, Nesvizhskii AI, Pedrioli PGA, Reiter L, Röst HL, Tate S, Ting YS, Collins BC, Aebersold R. *Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses.* **Nat Methods.** 2017 Sep;14(9):921-927. [doi: 10.1038/nmeth.4398.](http://dx.doi.org/10.1038/nmeth.4398) Epub 2017 Aug 21. %package -n python3-pyprophet Summary: PyProphet: Semi-supervised learning and scoring of OpenSWATH results. Provides: python-pyprophet BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-pyprophet PyProphet: Semi-supervised learning and scoring of OpenSWATH results. PyProphet is a Python re-implementation of the mProphet algorithm [1] optimized for SWATH-MS data acquired by data-independent acquisition (DIA). The algorithm was originally published in [2] and has since been extended to support new data types and analysis modes [3,4]. Please consult the [OpenSWATH website](http://openswath.org) for usage instructions and help. 1. Reiter L, Rinner O, Picotti P, Hüttenhain R, Beck M, Brusniak MY, Hengartner MO, Aebersold R. *mProphet: automated data processing and statistical validation for large-scale SRM experiments.* **Nat Methods.** 2011 May;8(5):430-5. [doi: 10.1038/nmeth.1584.](http://dx.doi.org/10.1038/nmeth.1584) Epub 2011 Mar 20. 2. Teleman J, Röst HL, Rosenberger G, Schmitt U, Malmström L, Malmström J, Levander F. *DIANA--algorithmic improvements for analysis of data-independent acquisition MS data.* **Bioinformatics.** 2015 Feb 15;31(4):555-62. [doi: 10.1093/bioinformatics/btu686.](http://dx.doi.org/10.1093/bioinformatics/btu686) Epub 2014 Oct 27. 3. Rosenberger G, Liu Y, Röst HL, Ludwig C, Buil A, Bensimon A, Soste M, Spector TD, Dermitzakis ET, Collins BC, Malmström L, Aebersold R. *Inference and quantification of peptidoforms in large sample cohorts by SWATH-MS.* **Nat Biotechnol** 2017 Aug;35(8):781-788. [doi: 10.1038/nbt.3908.](http://dx.doi.org/10.1038/nbt.3908) Epub 2017 Jun 12. 4. Rosenberger G, Bludau I, Schmitt U, Heusel M, Hunter CL, Liu Y, MacCoss MJ, MacLean BX, Nesvizhskii AI, Pedrioli PGA, Reiter L, Röst HL, Tate S, Ting YS, Collins BC, Aebersold R. *Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses.* **Nat Methods.** 2017 Sep;14(9):921-927. [doi: 10.1038/nmeth.4398.](http://dx.doi.org/10.1038/nmeth.4398) Epub 2017 Aug 21. %package help Summary: Development documents and examples for pyprophet Provides: python3-pyprophet-doc %description help PyProphet: Semi-supervised learning and scoring of OpenSWATH results. PyProphet is a Python re-implementation of the mProphet algorithm [1] optimized for SWATH-MS data acquired by data-independent acquisition (DIA). The algorithm was originally published in [2] and has since been extended to support new data types and analysis modes [3,4]. Please consult the [OpenSWATH website](http://openswath.org) for usage instructions and help. 1. Reiter L, Rinner O, Picotti P, Hüttenhain R, Beck M, Brusniak MY, Hengartner MO, Aebersold R. *mProphet: automated data processing and statistical validation for large-scale SRM experiments.* **Nat Methods.** 2011 May;8(5):430-5. [doi: 10.1038/nmeth.1584.](http://dx.doi.org/10.1038/nmeth.1584) Epub 2011 Mar 20. 2. Teleman J, Röst HL, Rosenberger G, Schmitt U, Malmström L, Malmström J, Levander F. *DIANA--algorithmic improvements for analysis of data-independent acquisition MS data.* **Bioinformatics.** 2015 Feb 15;31(4):555-62. [doi: 10.1093/bioinformatics/btu686.](http://dx.doi.org/10.1093/bioinformatics/btu686) Epub 2014 Oct 27. 3. Rosenberger G, Liu Y, Röst HL, Ludwig C, Buil A, Bensimon A, Soste M, Spector TD, Dermitzakis ET, Collins BC, Malmström L, Aebersold R. *Inference and quantification of peptidoforms in large sample cohorts by SWATH-MS.* **Nat Biotechnol** 2017 Aug;35(8):781-788. [doi: 10.1038/nbt.3908.](http://dx.doi.org/10.1038/nbt.3908) Epub 2017 Jun 12. 4. Rosenberger G, Bludau I, Schmitt U, Heusel M, Hunter CL, Liu Y, MacCoss MJ, MacLean BX, Nesvizhskii AI, Pedrioli PGA, Reiter L, Röst HL, Tate S, Ting YS, Collins BC, Aebersold R. *Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses.* **Nat Methods.** 2017 Sep;14(9):921-927. [doi: 10.1038/nmeth.4398.](http://dx.doi.org/10.1038/nmeth.4398) Epub 2017 Aug 21. %prep %autosetup -n pyprophet-2.2.5 %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-pyprophet -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Wed May 17 2023 Python_Bot - 2.2.5-1 - Package Spec generated