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1 <tool name="MsFilt" id="msfilt" version="1.0.3">
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2 <description>Filters annotations based MS/MS peptide identification and annotation quality measures</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 MsFilt.jar
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10 -apmlFile $apmlFile
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11 -datasetCode $apmlFile.metadata.base_name
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12 -rankingMetadataFile $rankingMetadataFile
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13 -statisticalMeasuresConfigFile $statisticalMeasuresConfigFile
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14 -annotationSourceConfigFile $annotationSourceConfigFile
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15 -outApml $outputApml
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16 -outNewIdsApml $outNewIdsApml
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17 -outFullCSV $outputCSV
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18 -outRankingTable $outRankingTable
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19 -outProteinCoverageCSV $outProteinCoverageCSV
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20 -fpCriteriaExpression "$fpCriteriaExpression"
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21 -filterOutFPAnnotations $filterOutFPAnnotations
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22 -fpCriteriaExpressionForIds "$fpCriteriaExpressionForIds"
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23 -filterOutFPIds $filterOutFPIds
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24 -filterOutUnannotatedAlignments $filterOutUnannotatedAlignments
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25 -addRawRankingInfo $addRawRankingInfo
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26 -addScaledIntensityInfo $addScaledIntensityInfo
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27 -addRawIntensityInfo $addRawIntensityInfo
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28 -outReport $htmlReportFile
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29 -outReportPicturesPath $htmlReportFile.files_path
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30 </command>
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31
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32 <inputs>
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33
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34 <param name="apmlFile" type="data" format="apml" optional="true"
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35 label="(Optional) Peptide quantification file (APML)"
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36 help="The APML contents as aligned and annotated feature lists. E.g. produced by
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37 SEDMAT or Quantiline tools." />
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38
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39 <repeat name="annotationSourceFiles" title="(Optional) Peptide identification files" help="Full set of MS/MS peptide identification files, including peptides that could not be quantified.">
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40 <param name="identificationsFile" type="data" format="apml,mzidentml,prims.fileset.zip" label="Identifications file (APML or MZIDENTML or MZIDENTML fileSet)" />
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41 </repeat>
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42
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43 <!--
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44 <param name="maxNrRankings" type="integer" size="10" value="0" label="Maximum nr. of items to leave in the final ranking (set=0 for no limit) " />
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45 -->
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46 <!-- TODO add info somewhere that deltaRt is 'corrected deltaRt' -->
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47 <param name="rankingWeightConfig" type="text" area="true" size="13x70" label="Quality Measures (qm's) and ranking weights configuration"
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48 help="Here you may specify a weight for each of the Quality Measures (QMs). These are used for the final QM score and possibly for ranking (e.g. in case of label-free data
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49 processed by SEDMAT). The format is: QM alias => QM name,weight. "
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50 value="qmDRT => delta rt (standard score),1
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51 
qmDMA => delta mass annotation (standard score),1
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52 
qmDMP => delta mass psm (standard score),1
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53 
qmBSCR => best peptide score (standard score),1
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54 
qmALCV => alignment coverage (fraction),1
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55 
qmSTCV => score type coverage (fraction),1
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56 
qmPACV => peptide's best proteinAnnotCoverage (standard score),1
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57 
qmPICV => peptide's best proteinIdentifCoverage (standard score),1
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58 
qmANS => annotation sources (count),1
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59 
qmCSEV => charge states evidence (count),0.2
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60 
qmBCSP=> best correlation with source or product peptide (correl),1
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61 
qmBCCS => best correlation with other charge state (correl),1
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62 
qmBCOS => best correlation with other sibling peptide (correl),1
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63 "/>
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64
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65 <param name="statisticalMeasuresConfig" type="text" area="true" size="6x70" label="Statistical measures configuration"
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66 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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67 The format is: SM alias => SM name,type,mode[min/max]. "
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68 value="smXTD => MS:1001330,XSLASH!Tandem:expect,min
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69 
pvCSVEX => p_value,CSV_EXPORT,min
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70 
smAUTO_LIKELIHOOD => AUTOMOD_LOGLIKELIHOOD,PLGS/Auto-mod,max
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71 
smLIKELIHOOD => LOGLIKELIHOOD,PLGS/Databank-search,max
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72 "/>
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73
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74 <param name="filterOutUnannotatedAlignments" type="boolean" checked="true"
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75 label="Filter out unannotated alignments"
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76 help="This helps decrease the output file size (features with no annotation are then not reported anymore)"/>
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77
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78 <param name="filterOutFPAnnotations" type="boolean" checked="true"
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79 label="Filter out False Positive (FP) annotations" />
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80
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81 <param name="fpCriteriaExpression" type="text" size="120" label="False Positive (FP) criteria for annotations"
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82 help="Criteria (in standard score measures) for classifying an annotation as False Positive (FP).
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83 You can build logical rules using the QM aliases above, the keywords 'and', 'or' and parenthesis.
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84 Comparisons can be made with '==,<,><=,>='"
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85 value="qmDRT <0 or qmDMA <-0.5 or (qmDMP <-0.5 and qmBSCR<-0.5) or (!isNaN(smXTD) and smXTD >0.01)"/>
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86
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87
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88 <param name="filterOutFPIds" type="boolean" checked="true"
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89 label="Filter out False Positive (FP) peptide identifications" />
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90
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91 <param name="fpCriteriaExpressionForIds" type="text" size="120"
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92 label="False Positive (FP) criteria for identifications"
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93 help="Criteria (in standard score measures) for classifying a peptide identification as False Positive (FP).
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94 Here you can use a subset of the quality measures (qmDMP, qmBSCR, qmSTCV, qmPICV, qmCSEV) and all statistical measures."
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95 value="(qmDMP <-0.5 and qmBSCR<-0.5) or (!isNaN(smXTD) and smXTD >0.01)"/>
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96
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97
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98 <param name="addRawRankingInfo" type="boolean" checked="false"
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99 label="Include the raw scores/values of the ranking attributes in the CSV output"
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100 help="This will result in one extra column per ranking attribute, each column holding the original data for this attribute (before normalization)."/>
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101
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102 <param name="addScaledIntensityInfo" type="boolean" checked="false"
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103 label="Include computed scaled intensity values in the CSV output"
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104 help="The autoscaled and 'z-score'scaled (aka 'standard-score'scaled) intensity values are then added to the full CSV output file"/>
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105
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106 <param name="addRawIntensityInfo" type="boolean" checked="false"
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107 label="Include the raw intensity values in the CSV output"
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108 help="The original intensity values (as found in the input file) are then added to the full CSV output file"/>
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109
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110
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111 </inputs>
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112 <configfiles>
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113 <configfile name="rankingMetadataFile">${rankingWeightConfig}</configfile>
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114 <configfile name="statisticalMeasuresConfigFile">${statisticalMeasuresConfig}</configfile>
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115 <configfile name="annotationSourceConfigFile">## start comment
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116 ## iterate over the selected files and store their names in the config file
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117 #for $i, $s in enumerate( $annotationSourceFiles )
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118 ${s.identificationsFile}
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119 ## also print out the datatype in the next line, based on previously configured datatype
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120 #if isinstance( $s.identificationsFile.datatype, $__app__.datatypes_registry.get_datatype_by_extension('apml').__class__):
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121 apml
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122 #else:
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123 mzid
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124 #end if
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125 #end for
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126 ## end comment</configfile>
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127 </configfiles>
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128 <outputs>
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129 <data name="outputApml" format="apml" label="${apmlFile.metadata.base_name} - ${tool.name} on ${on_string}: quantifications (filtered APML)" metadata_source="apmlFile">
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130 <!-- If the expression is false, the file is not created -->
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131 <filter>( apmlFile != None )</filter>
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132 </data>
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133 <data name="outNewIdsApml" format="apml" label="${tool.name} on ${on_string}: identifications (filtered APML)" >
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134 <filter>( filterOutFPIds == True )</filter>
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135 </data>
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136 <data name="outputCSV" format="csv" label="${apmlFile.metadata.base_name} - ${tool.name} on ${on_string}: Full CSV" metadata_source="apmlFile">
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137 <filter>( apmlFile != None )</filter>
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138 </data>
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139 <data name="outRankingTable" format="csv" label="${apmlFile.metadata.base_name} - ${tool.name} on ${on_string}: Ranking table (CSV)" metadata_source="apmlFile">
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140 <filter>( apmlFile != None )</filter>
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141 </data>
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142 <data name="outProteinCoverageCSV" format="csv" label="${tool.name} on ${on_string}: Protein coverage details (CSV)">
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143 <!-- If the expression is false, the file is not created -->
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144 <filter>( len(list(enumerate(annotationSourceFiles))) > 0 )</filter>
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145 </data>
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146 <data name="htmlReportFile" format="html" label="${tool.name} on ${on_string} - HTML report"/>
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147 </outputs>
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148 <tests>
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149 </tests>
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150 <help>
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151
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152 .. class:: infomark
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153
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154 This tool takes in peptide quantification results (e.g. either by SEDMAT for label-free data or by Quantiline for labeled data)
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155 and calculates a number of quality measures that can help in assessing the correctness of the quantification assignment and of the MS/MS peptide
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156 identification itself. The user can use any combination of quality measures (qm's) and statistical measures (sm's) to filter out
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157 low scoring entries.
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158
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159 .. class:: infomark
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160
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161 In the label-free data processed by SEDMAT it is possible that a feature quantification gets assigned to different peptides. This means
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162 we have an ambiguous assignment. In such a case
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163 this tool also does a ranking of the different assignments according to their quality measures so that the best scoring assignment
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164 gets ranked as first.
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165
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166 -----
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167
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168 **List of abbreviations**
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169
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170 QM: Quality Measure
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171
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172 SM: Statistical Measure (e.g. p-value, e-value from MS/MS identification)
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173
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174 PSM: "Peptide to Spectrum Match" (aka peptide identification)
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175
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176 FP: False Positive
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177
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178 -----
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179
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180 **Filtering options details**
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181
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182 The FP criteria will be applied to an annotation even if the corresponding quality measures involved
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183 in the expression can NOT ALL be determined. QMs that cannot be determined, get the value 0 (zero) which is
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184 equal to giving it the average value.
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185
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186 The output report shows some plots that visualize the filtering done. This can help in fine-tuning the right filtering
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187 criteria.
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188
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189 -----
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190
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191 **Output details**
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192
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193 *APML output*
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194
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195 This tools returns the given APML alignment file further annotated at the alignment level with the best ranking
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196 peptides of each respective alignment. This APML can be used in subsequent Galaxy tools like the proteomics tools
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197 from NBIC.
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198
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199 The APML output can also be used for the Protein Inference step (see Quantifere tool).
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200
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201 *CSV output*
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202
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203 It also returns a CSV format output with the full quality measures and scoring and ranking details. The user could use
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204 this to manually determine new weights for some of the quality measures by techniques such as
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205 linear regression. In other words, this CSV can then be used to fine-tune the weights in a next run.
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206
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207 Many of the quality measures (QMs) are normalized to their Standard Score (aka z-score).
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208 `See Standard Score for more details...`__
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209
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210 Next to giving insight into how the ranking was established, a more complete version of this CSV file is also
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211 generated for tools that cannot or won't process the APML output format.
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212
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213 Below an brief overview of the CSV and an illustration of the ranking done in case of ambiguous peptides to feature assignments
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214 (explained above, can happen in case of label-free data processing by SEDMAT).
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215
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216
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217 .. image:: $PATH_TO_IMAGES/msfilt_csv_out.png
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218
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219
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220
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221 .. __: javascript:window.open('http://en.wikipedia.org/wiki/Standard_score','popUpWindow','height=700,width=800,left=10,top=10,resizable=yes,scrollbars=yes,toolbar=yes,menubar=no,location=no,directories=no,status=yes')
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222
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223
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224
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225
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226 </help>
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227 </tool>
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