Mercurial > repos > miller-lab > genome_diversity
annotate rank_pathways.xml @ 37:884ccb07885b
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author | Richard Burhans <burhans@bx.psu.edu> |
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date | Wed, 20 Nov 2013 16:43:43 -0500 |
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1 <tool id="gd_calc_freq" name="Rank Pathways" version="1.2.0"> |
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2 <description>: Assess the impact of a gene set on KEGG pathways</description> |
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3 |
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4 <command interpreter="python"> |
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5 #if $rank_by.choice == 'pct' |
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6 rank_pathways_pct.py |
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7 --input '$rank_by.input1' |
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8 --columnENSEMBLT '$rank_by.t_col1' |
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9 --inBckgrndfile '$rank_by.input2' |
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10 --columnENSEMBLTBckgrnd '$rank_by.t_col2' |
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11 --columnKEGGBckgrnd '$rank_by.k_col2' |
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12 --statsTest '$rank_by.stat' |
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13 --output '$output' |
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14 #else if $rank_by.choice == 'paths' |
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15 calclenchange.py |
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16 '--loc_file=${GALAXY_DATA_INDEX_DIR}/gd.rank.loc' |
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17 '--species=${rank_by.input.metadata.dbkey}' |
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18 '--input=${rank_by.input}' |
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19 '--output=${output}' |
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20 '--posKEGGclmn=${rank_by.kpath}' |
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21 '--KEGGgeneposcolmn=${rank_by.kgene}' |
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22 #end if |
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23 </command> |
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24 |
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25 <inputs> |
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26 <conditional name="rank_by"> |
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27 <param name="choice" type="select" label="Rank by"> |
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28 <option value="pct" selected="true">percentage of genes affected</option> |
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29 <option value="paths">change in length and number of paths</option> |
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30 </param> |
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31 <when value="pct"> |
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32 <!-- using fields similar to the Rank Terms tool --> |
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33 <param name="input1" type="data" format="tabular" label="Query dataset" /> |
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34 <param name="t_col1" type="data_column" data_ref="input1" label="Column with ENSEMBL transcript codes" /> |
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35 <param name="input2" type="data" format="tabular" label="Background dataset" /> |
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36 <param name="t_col2" type="data_column" data_ref="input2" label="Column with ENSEMBL transcript codes" /> |
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37 <param name="k_col2" type="data_column" data_ref="input2" label="Column with KEGG pathways" /> |
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38 <param name="stat" type="select" label="Statistic for determining enrichment/depletion"> |
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39 <option value="fisher" selected="true">two-tailed Fisher's exact test</option> |
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40 <option value="hypergeometric">hypergeometric test</option> |
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41 <option value="binomial">binomial probability</option> |
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42 </param> |
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43 </when> |
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44 <when value="paths"> |
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45 <param name="input" type="data" format="tabular" label="Dataset" /> |
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46 <param name="kgene" type="data_column" data_ref="input" label="Column with KEGG gene ID" /> |
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47 <param name="kpath" type="data_column" data_ref="input" numerical="false" label="Column with KEGG pathways" /> |
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48 </when> |
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49 </conditional> |
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50 </inputs> |
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51 |
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52 <outputs> |
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53 <data name="output" format="tabular" /> |
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54 </outputs> |
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55 |
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56 <requirements> |
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57 <requirement type="package" version="0.2.5">mechanize</requirement> |
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58 <requirement type="package" version="1.8.1">networkx</requirement> |
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59 <requirement type="package" version="0.1.4">fisher</requirement> |
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60 </requirements> |
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61 |
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62 |
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63 <tests> |
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64 <test> |
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65 </test> |
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66 </tests> |
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67 |
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68 <help> |
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69 |
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70 **Dataset formats** |
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71 |
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72 The query dataset has a column containing ENSEMBL transcript codes for |
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73 the gene set of interest, while the background dataset has one column |
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74 with ENSEMBL transcript codes and another with KEGG pathways, for some larger |
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75 universe of genes. |
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76 |
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77 All of the input and output datasets are in tabular_ format. The input |
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78 dataset (i.e. query) to rank by "percentage of genes affected" has a |
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79 column containing ENSEMBL transcript codes for the gene set of interest, |
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80 while the background dataset has one column with ENSEMBL transcript |
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81 codes and another with KEGG pathways, for some larger universe of genes. |
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82 The input dataset to rank by "change in length and number of paths" |
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83 must have columns with KEGG gene ID and pathways. The output datasets |
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84 are described below. (`Dataset missing?`_) |
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85 |
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86 .. _tabular: ./static/formatHelp.html#tab |
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87 .. _Dataset missing?: ./static/formatHelp.html |
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88 |
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89 ----- |
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90 |
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91 **What it does** |
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92 |
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93 Given a query set of genes from a larger background dataset, this tool |
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94 evaluates the over- or under-representation of KEGG pathways in the query |
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95 set, using the specified statistical test. Alternatively, the tool ranks |
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96 the pathways based on the change in length and number of paths connecting |
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97 sources and sinks. This change is calculated between graphs representing |
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98 pathways with and without excluding the nodes that represent the genes |
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99 in an input list. Sources are all the nodes representing the initial |
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100 reactants/products in the pathway. Sinks are all the nodes representing |
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101 the final reactants/products in the pathway. |
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102 |
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103 If pathways are ranked by percentage of genes affected, the output |
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104 contains a row for each KEGG pathway, with the following columns: |
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106 1. count: the number of genes in the query set that are in this pathway |
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107 2. representation: the percentage of this pathway's genes (from the background dataset) that appear in the query set |
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108 3. ranking of this pathway, based on its representation ("1" is highest) |
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109 4. probability of depletion of this pathway in the query dataset |
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110 5. probability of enrichment of this pathway in the query dataset |
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111 6. name of the pathway |
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112 |
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113 If pathways are ranked by change in length and number of paths, the |
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114 output is a tabular dataset with the following columns: |
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115 |
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116 1. change in the mean length of paths between sources and sinks |
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117 2. mean length of paths between sources and sinks in the pathway including the genes in the input dataset. If the pathway do not have sources/sinks, the length is assumed to be infinite (I) |
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118 3. mean length of paths between sources and sinks in the pathway excluding the genes in the input dataset. If the pathway do not have sources/sinks, the length is assumed to be infinite (I) |
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119 4. rank of the change in the mean length of paths between sources and sinks (from high change to low change) |
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120 5. change in the number of paths between sources and sinks |
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121 6. number of paths between sources and sinks in the pathway including the genes in the input dataset. If the pathway do not have sources/sinks, it is assumed to be a circuit (C) |
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122 7. number of paths between sources and sinks in the pathway excluding the genes in the input dataset. If the pathway do not have sources/sinks, it is assumed to be a circuit (C) |
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123 8. rank of the change in the number of paths between sources and sinks (from high change to low change) |
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124 9. name of the pathway |
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125 |
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126 ----- |
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127 |
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128 **Examples** |
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129 |
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130 Rank by percentage of genes affected: |
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131 |
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132 - input background dataset (column 5 for ENSEMBL transcript, column 12 for KEGG pathways, two-tailed Fisher's exact test for statistic):: |
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133 |
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134 Contig39_chr1_3261104_3261850 414 chr1 3261546 ENSCAFT00000000001 ENSCAFP00000000001 S 667 F 476153 probably damaging cfa00230=Purine metabolism.cfa00500=Starch and sucrose metabolism.cfa00740=Riboflavin metabolism.cfa00760=Nicotinate and nicotinamide metabolism.cfa00770=Pantothenate and CoA biosynthesis.cfa01100=Metabolic pathways |
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135 Contig62_chr1_19011969_19012646 265 chr1 19012240 ENSCAFT00000000144 ENSCAFP00000000125 * 161 R 483960 probably damaging N |
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136 etc. |
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137 |
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138 - input query dataset (column 5 for ENSEMBL transcript):: |
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139 |
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140 Contig12_chr20_101969_112646 265 chr20 9822141 ENSCAFT00000001234 ENSCAFP00000021123 T 101 R 476153 probably damaging |
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141 Contig39_chr1_3261104_3261850 414 chr1 3261546 ENSCAFT00000000001 ENSCAFP00000000001 S 667 F 476153 probably damaging |
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142 etc. |
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143 |
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144 - output:: |
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145 |
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146 3 0.20 1 1.0 0.0065 cfa03450=Non-homologous end-joining |
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147 1 0.067 2 1.0 0.019 cfa00750=Vitamin B6 metabolism |
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148 2 0.062 3 1.0 0.021 cfa00290=Valine, leucine and isoleucine biosynthesis |
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149 1 0.037 4 1.0 0.035 cfa00770=Pantothenate and CoA biosynthesis |
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150 etc. |
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151 |
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152 Rank by change in length and number of paths: |
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153 |
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154 - input (column 10 for KEGG gene ID, column 12 for KEGG pathways):: |
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155 |
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156 Contig39_chr1_3261104_3261850 414 chr1 3261546 ENSCAFT00000000001 ENSCAFP00000000001 S 667 F 476153 probably damaging cfa00230=Purine metabolism.cfa00500=Starch and sucrose metabolism.cfa00740=Riboflavin metabolism.cfa00760=Nicotinate and nicotinamide metabolism.cfa00770=Pantothenate and CoA biosynthesis.cfa01100=Metabolic pathways |
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157 Contig62_chr1_19011969_19012646 265 chr1 19012240 ENSCAFT00000000144 ENSCAFP00000000125 * 161 R 483960 probably damaging N |
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158 etc. |
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159 |
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160 - output:: |
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161 |
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162 3.64 8.44 4.8 2 4 9 5 1 cfa00260=Glycine, serine and threonine metabolism |
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163 7.6 9.6 2 1 3 5 2 2 cfa00240=Pyrimidine metabolism |
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164 0.05 2.67 2.62 6 1 30 29 3 cfa00982=Drug metabolism - cytochrome P450 |
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165 -0.08 8.33 8.41 84 1 30 29 3 cfa00564=Glycerophospholipid metabolism |
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166 etc. |
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167 </help> |
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168 </tool> |