1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
|
%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
* Tue May 30 2023 Python_Bot <Python_Bot@openeuler.org> - 2.2.5-1
- Package Spec generated
|
