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1 <tool name="Quantifere" id="quantifere1" version="1.0.2">
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2 <description>Protein Inference by Peptide Quantification patterns</description>
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3 <!--
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4 For remote debugging start you listener on port 8000 and use the following as command interpreter:
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5 java -jar -Xdebug -Xrunjdwp:transport=dt_socket,address=D0100564.wurnet.nl:8000
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6 //////////////////////////
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7 -->
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8 <command interpreter="java -jar ">
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9 Quantifere.jar
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10 -annotatedQuantificationFilesList $annotatedQuantificationFilesList
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11 -identificationFilesList $identificationFilesList
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12 -statisticalMeasuresConfigFile $statisticalMeasuresConfigFile
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13 -quantificationDataToUse $quantificationDataToUse
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14 -minCorrel $minCorrel
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15 -minProtCoverage $minProtCoverage
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16 -minAboveAverageHits $minAboveAverageHits
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17 -minNrIdsForInferencePeptide $minNrIdsForInferencePeptide
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18 -refineModel $refineModel
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19 -functionalAnnotationCSV $functionalAnnotationCSV
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20 -outputCSV $outputCSV
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21 -outputInferenceLogCSV $outputInferenceLogCSV
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22 -outputSummaryAnnotationCSV $outputSummaryAnnotationCSV
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23 -outReport $htmlReportFile
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24 -outReportPicturesPath $htmlReportFile.files_path
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25 #if $is2D_LC_MS.fractions == True
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26 -namingConventionCodesForFractions $is2D_LC_MS.namingConventionCodesForFractions
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27 #end if
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28 </command>
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29
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30 <inputs>
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31
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32 <repeat name="annotatedQuantificationFiles" title="Peptide (filtered) quantification files (APML)"
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33 help="The APML contents as aligned, annotated and scored feature lists,
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34 as produced by MsFilt tool. Select one or more files. For 2D-LC-MS we expect one file per fraction.">
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35 <param name="annotatedQuantificationFile" size="50" type="data" format="apml" label="File (APML format)" />
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36 </repeat>
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37
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38 <repeat name="identificationFiles" title="Peptide (filtered) identification files (MS/MS identifications)"
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39 help="Full set of MS/MS peptide identification files, including peptides that could not be quantified.
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40 This set of identifications is ideally filtered on some quality and
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41 statistical measures (e.g. as is done by MsFilt). Tip: to base the inference only on the
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42 selected peptide quantification files, you
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43 can select the same quantification files here as well. Select one or more files.">
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44 <param name="identificationFile" size="50" type="data" format="apml,mzid" label="File (APML or MZIDENTML format)" />
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45 </repeat>
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46
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47 <conditional name="is2D_LC_MS">
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48 <param name="fractions" type="boolean" truevalue="Yes" falsevalue="No" checked="false"
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49 label="Data is from 2D LC-MS"
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50 help="Data acquisition was done in multiple fractions."/>
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51 <when value="Yes">
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52 <param name="namingConventionCodesForFractions" type="text" size="100" value=""
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53 label="Part of run/file name that identifies the 2D LC-MS fraction"
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54 help="Add the CSV list of codes that occur in the file names
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55 and that stand for a fraction code. E.g. '_F1,_F2,_F3,etc.' In this
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56 way different peptide identifications from the same sample but measured
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57 in different fractions can be merged together. Otherwise each (fraction) file
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58 is seen as a separate sample."/> <!-- could do regular expressions as well but this would be hard for biologists, e.g. _F\d\b -->
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59 </when>
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60 </conditional>
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61
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62 <param name="statisticalMeasuresConfig" type="text" area="true" size="6x70" label="Statistical measures configuration"
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63 help="Here you may specify the statistical measures that are found in the ms/ms results (e.g. p or e-values).
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64 The format is: SM alias => SM name,type,mode[min/max]. Leaving this configuration out while these are present in the
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65 dataset will have the effect that they will be wrongly used as a regular scoring scheme, having effect on for example
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66 the filter criteria below like 'Minimum number of peptide matches with a score above average' ."
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67 value="smXTD => MS:1001330,XSLASH!Tandem:expect,min
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68 
pvCSVEX => p_value,CSV_EXPORT,min
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69 
smAUTO_LIKELIHOOD => AUTOMOD_LOGLIKELIHOOD,PLGS/Auto-mod,max
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70 
smLIKELIHOOD => LOGLIKELIHOOD,PLGS/Databank-search,max
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71 "/>
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72 <!-- keep value attribute above aligned like this to avoid white spaces in the value -->
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73 <param name="quantificationDataToUse" type="select"
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74 label="Quantification data to use"
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75 help="Quantification data to use for the pattern clustering and inference steps. NB: check if the chosen data is also
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76 present in your file, or choose 'auto' to let Quantifere check which quantification type is present in most peptides.">
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77 <option value="auto" selected="true">auto</option>
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78 <option value="getIntensity">(TODO)raw intensities</option>
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79 <option value="getApexIntensity">(TODO)apex intensities</option>
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80 <option value="getNormalizedIntensity">(TODO)normalized intensities</option>
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81 </param>
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82 <!-- TODO let minCorrel default value vary according to quantification type chosen above -->
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83 <param name="minCorrel" type="float" size="10" value="0.85" label="Minimum correlation in a cluster" help="Features will be grouped by their protein annotation and
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84 sample intensity values correlation. Set here the minimum correlation expected between grouped members. This is used to guide the clustering algorithm."/>
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85
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86 <!-- simple extra heuristics to remove some "noise" protein hits -->
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87 <param name="minProtCoverage" type="float" size="10" value="5.0" label="Minimum protein coverage (%)" help="This will remove proteins that have a too small
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88 portion of their sequence covered by peptide matches."/>
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89
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90 <param name="minAboveAverageHits" type="integer" size="10" value="1" label="Minimum number of different peptide matches with a score above average"
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91 help="This will remove proteins that do not have enough reasonable peptides hits."/>
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92
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93 <param name="minNrIdsForInferencePeptide" type="integer" size="10" value="1" label="Minimum number of peptide identifications for inference peptides"
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94 help="Minimum number of peptide identifications a peptide needs to be used as inference peptide for secondary proteins."/>
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95
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96
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97 <param name="functionalAnnotationCSV" type="data" format="csv,txt,tsv" optional="true"
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98 label="(Functional)annotation mapping file (csv or tsv format)"
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99 help="Optional file that maps protein accessions to a network, pathway or other higher level annotations. In this file a header line is expected with these 2 columns (names and lower case is important): accession,annotation"/>
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100
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101 <param name="refineModel" type="boolean" checked="true" label="Refine matches model"
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102 help="This will let the algorithm search for a reduced set of secondary protein matches that still explains the variation in the peptide quantification patterns"/>
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103
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104
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105 <param name="summaryReport" type="boolean" checked="true" label="Generate summary report"/>
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106
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107 </inputs>
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108 <configfiles>
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109 <configfile name="annotatedQuantificationFilesList">## start comment
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110 ## iterate over the selected files and store their names in the config file
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111 #for $i, $s in enumerate( $annotatedQuantificationFiles )
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112 ${s.annotatedQuantificationFile}
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113 #end for
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114 ## end comment</configfile>
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115
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116 <configfile name="identificationFilesList">## start comment
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117 ## iterate over the selected files and store their names in the config file
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118 #for $i, $s in enumerate( $identificationFiles )
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119 ${s.identificationFile}
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120 ## also print out the datatype in the next line, based on previously configured datatype
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121 #if isinstance( $s.identificationFile.datatype, $__app__.datatypes_registry.get_datatype_by_extension('apml').__class__):
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122 apml
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123 #else:
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124 mzid
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125 #end if
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126 #end for
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127 ## end comment</configfile>
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128 <configfile name="statisticalMeasuresConfigFile">## start comment
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129 ${statisticalMeasuresConfig}
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130 </configfile>
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131 </configfiles>
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132 <outputs>
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133 <data name="outputCSV" format="csv" label="${tool.name} on ${on_string}: Proteins list (CSV)" />
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134 <data name="outputInferenceLogCSV" format="csv" label="${tool.name} on ${on_string}: Inference log (CSV)"/>
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135 <data name="htmlReportFile" format="html" label="${tool.name} on ${on_string} - HTML report">
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136 <!-- If the expression is false, the file is not created -->
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137 <filter>( summaryReport == True )</filter>
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138 </data>
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139 <data name="outputSummaryAnnotationCSV" format="csv" label="${tool.name} on ${on_string} - Functional annotation summary (CSV)">
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140 <!-- If the expression is false, the file is not created -->
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141 <filter>( functionalAnnotationCSV != None )</filter>
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142 </data>
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143 </outputs>
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144 <tests>
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145 </tests>
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146 <help>
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147
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148 .. class:: infomark
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149
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150 This tool takes Peptide Quantification patterns and uses this to do Protein Inference of both Primary Protein
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151 identifications as well as Secondary Protein identifications. This last class of protein identifications
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152 can not be done by traditional protein inference methods that look only at peptide identifications and
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153 their quality parameters.
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154
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155
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156 -----
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157
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158 **List of definitions**
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159
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160 Primary Protein identification: protein identification belonging to the minimum set of proteins needed
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161 to account for the observed peptides.
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162
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163 Secondary Protein identification: extra protein identifications that do not below to the minimum set
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164 of proteins mentioned above.
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165
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166 raw intensities : is the intensity value resulting from the integration of the feature peak area
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167
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168 apex intensities: is the intensity value as on the highest point of the feature peak
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169
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170 normalized intensities : is the intensity normalized by some means
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171
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172 -----
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173
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174 **Minimum correlation in a cluster**
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175
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176 TODO - add doc.
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177
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178 -----
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179
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180 **Output details**
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181
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182 *Proteins list (CSV)*
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183
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184 This is the list of primary and secondary proteins and their calculated inference score. Proteins
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185 with exactly the same peptide hits are also grouped together and labeled as primary_group and secondary_group
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186 instead of simply primary and secondary.
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187
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188
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189 *Inference log (CSV)*
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190
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191 This CSV table shows all data, both inferred and ruled out proteins. This can be used by the user to
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192 troubleshoot the inference process and understand why certain proteins might have been ruled out.
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193 The CSV is provided in such a format that the data can easily be explored in a Cytoscape network.
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194
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195 The figure below shows an example of the data being explored in Cytoscape using also the
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196 `Cytoscape chartplugin`_ to visualize the quantification data when selecting the peptide nodes.
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197
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198 .. image:: $PATH_TO_IMAGES/quantifere_cyto_out.png
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199
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200 .
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201
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202 .. _Cytoscape chartplugin: http://apps.cytoscape.org/apps/chartplugin
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203
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204
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205
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206 </help>
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207 </tool>
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