comparison tools/networkAnalysis/LoadPoint/LoadPoint.xml @ 9:0976a6257300 draft

planemo upload for repository https://forgemia.inra.fr/metexplore/met4j-galaxy commit 05db35f63cadb9d56dafff594a3507c59cd85273
author metexplore
date Fri, 31 Jan 2025 18:28:53 +0000
parents 7a6f2380fc1d
children 6a112eaf8f38
comparison
equal deleted inserted replaced
8:1274e2a62479 9:0976a6257300
1 <?xml version="1.0" encoding="UTF-8"?> 1 <?xml version="1.0" encoding="UTF-8" standalone="no"?>
2 <tool id="met4j_LoadPoint" name="LoadPoint" version="MET4J_VERSION_TEST"> 2 <tool id="met4j_LoadPoint" name="LoadPoint" version="develop">
3 <description>Compute the Load points of a metabolic network. Load points constitute an indicator of lethality and can help identifying drug target.</description> 3 <description>Compute the Load points of a metabolic network. Load points constitute an indicator of lethality and can help identifying drug targets.</description>
4 <xrefs> 4 <xrefs>
5 <xref type="bio.tools">met4j</xref> 5 <xref type="bio.tools">met4j</xref>
6 </xrefs> 6 </xrefs>
7 <requirements> 7 <requirements>
8 <container type="singularity">oras://registry.forgemia.inra.fr/metexplore/met4j/met4j-singularity:MET4J_VERSION_TEST</container> 8 <container type="singularity">oras://registry.forgemia.inra.fr/metexplore/met4j/met4j-singularity:develop</container>
9 </requirements> 9 </requirements>
10 <command detect_errors="exit_code"><![CDATA[sh /usr/bin/met4j.sh networkAnalysis.LoadPoint -i "$inputPath" 10 <command detect_errors="exit_code"><![CDATA[sh /usr/bin/met4j.sh networkAnalysis.LoadPoint -i "$inputPath"
11 #if str($sideCompoundFile) != 'None': 11 #if str($sideCompoundFile) != 'None':
12 -s "$sideCompoundFile" 12 -s "$sideCompoundFile"
13 #end if 13 #end if
28 <outputs> 28 <outputs>
29 <data format="tsv" name="outputPath"/> 29 <data format="tsv" name="outputPath"/>
30 </outputs> 30 </outputs>
31 <tests> 31 <tests>
32 <test> 32 <test>
33
34
35
36
37
38
39
40
41
42
43
33 <param name="inputPath" value="toy_model.xml"/> 44 <param name="inputPath" value="toy_model.xml"/>
45
46
47
48
49
50
51
52
53
54
55
34 <output ftype="tsv" name="outputPath"> 56 <output ftype="tsv" name="outputPath">
57
58
59
60
61
62
63
64
65
66
67
35 <assert_contents> 68 <assert_contents>
69
70
71
72
73
74
75
76
77
78
79
36 <has_n_columns n="3"/> 80 <has_n_columns n="3"/>
81
82
83
84
85
86
87
88
89
90
91
37 <has_n_lines n="8"/> 92 <has_n_lines n="8"/>
93
94
95
96
97
98
99
100
101
102
103
38 </assert_contents> 104 </assert_contents>
105
106
107
108
109
110
111
112
113
114
115
39 </output> 116 </output>
117
118
119
120
121
122
123
124
125
126
127
40 </test> 128 </test>
41 </tests> 129 </tests>
42 <help><![CDATA[Compute the Load points of a metabolic network. Load points constitute an indicator of lethality and can help identifying drug target. 130 <help><![CDATA[Compute the Load points of a metabolic network. Load points constitute an indicator of lethality and can help identifying drug targets.
43 From Rahman et al. Observing local and global properties of metabolic pathways: ‘load points’ and ‘choke points’ in the metabolic networks. Bioinf. (2006): 131 From Rahman et al. Observing local and global properties of metabolic pathways: ‘load points’ and ‘choke points’ in the metabolic networks. Bioinf. (2006):
44 For a given metabolic network, the load L on metabolite m can be defined as : 132 For a given metabolic network, the load L on metabolite m can be defined as :
45 ln [(pm/km)/(∑Mi=1Pi)/(∑Mi=1Ki)] 133 ln [(pm/km)/(∑Mi=1Pi)/(∑Mi=1Ki)]
46 p is the number of shortest paths passing through a metabolite m; 134 p is the number of shortest paths passing through a metabolite m;
47 k is the number of nearest neighbour links for m in the network; 135 k is the number of nearest neighbour links for m in the network;
48 P is the total number of shortest paths; 136 P is the total number of shortest paths;
49 K is the sum of links in the metabolic network of M metabolites (where M is the number of metabolites in the network). 137 K is the sum of links in the metabolic network of M metabolites (where M is the number of metabolites in the network).
50 Use of the logarithm makes the relevant values more distinguishable.]]></help> 138 Use of the logarithm makes the relevant values more distinguishable.]]></help>
51 <citations/> 139 <citations>
140 <citation type="doi">10.1093/bioinformatics/btl181</citation>
141 </citations>
52 </tool> 142 </tool>