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1 <tool id="rankfilterGCMS_tabular" name="RIQC-RankFilter GC-MS from tabular file" version="1.0.2">
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2 <description>Convert Retention Time to Retention Index</description>
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3 <command interpreter="python">rankfilter_GCMS/rankfilter.py $input_file</command>
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4 <inputs>
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5 <param format="tabular" name="sample" type="data" label="Sample File"
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6 help="Select a tab delimited NIST metabolite identifications file (converted from PDF)" />
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7 <!-- question: is this calibration file not column specific as it includes RT info?? -->
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8 <!-- this one should be input file for now:<param name="calibration" type="select" label="Calibration File"
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9 help="Calibration file with reference masses (e.g. alkanes) with their RT and RI values"
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10 dynamic_options='get_directory_files("tool-data/shared/PRIMS-metabolomics/RankFilter_Calibration_Files")'/>
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11 -->
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12 <param name="calibration" format="any" type="data" label="Calibration File"
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13 help="Calibration file containing reference masses (e.g. alkanes) with their respective RT and RI values"/>
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14
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15 <param name="analysis_type" type="select" format="text" label="Analysis Type"
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16 help="Select the type of analysis that has been used to generate the sample file">
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17 <option value="NIST">NIST</option>
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18 <option value="AMDIS">AMDIS</option>
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19 </param>
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20 <param name="model" type="select" format="text" label="Select a model to be used "
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21 help="Both linear and (3rd degree) polynomial models are available ">
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22 <option value="linear">Linear</option>
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23 <option value="poly">Polynomial</option>
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24 </param>
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25 <param name="lib_data" type="select" label="Library"
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26 help="Reference global lookup library file with CAS numbers and respective (previously calculated) RIsvr values"
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27 dynamic_options='get_directory_files("tool-data/shared/PRIMS-metabolomics/RankFilter_lookup_libraries")'/>
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28
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29 <param name="window" type="float" label="Window" value="10.56" />
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30 </inputs>
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31 <outputs>
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32 <data format="tabular" label="${tool.name}" name="onefile" />
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33 </outputs>
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34 <!-- file with implementation of the function get_directory_files() used above -->
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35 <code file="match_library.py" />
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36 <configfiles>
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37 <configfile name="input_file">
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38 sample = ${sample}
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39 calibration = ${calibration}
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40 lib_data = ${lib_data}
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41 window = ${window}
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42 analysis_type = ${analysis_type}
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43 tabular = True
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44 onefile = ${onefile}
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45 model = ${model}
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46 </configfile>
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47 </configfiles>
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48 <help>
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49 Basically estimates the experimental RI (RIexp) by building a RI(RT) function based on the
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50 given calibration file.
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51
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52 It also determines the estimated RI (RIsvr) by looking up for each entry of the given input file (Sample File),
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53 based on its CAS number, its respective RIsvr value in the given global lookup library
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54 (this step is also called the "RankFilter analysis" -see reference below; Sample File may be either from NIST or AMDIS).
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55 This generates an prediction of the RI for
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56 a compound according to the "RankFilter procedure" (RIsvr).
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57
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58 Output is a tab separated file in which four columns are added:
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59
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60 - **Rank** Calculated rank
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61 - **RIexp** Experimental Retention Index (RI)
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62 - **RIsvr** Calculated RI based on support vector regression (SVR)
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63 - **%rel.err** Relative RI error (%rel.error = 100 * (RISVR − RIexp) / RIexp)
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64
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65 .. class:: infomark
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66
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67 **Notes**
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68
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69 - The layout of the Calibration file should include the following columns: 'MW', 'R.T.' and 'RI'.
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70 - Selecting 'Polynomial' in the model parameter will calculate a 3rd degree polynomial model that will
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71 be used to convert from XXXX to YYYY.
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72
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73 -----
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74
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75 **References**
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76
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77 - **RankFilter**: Mihaleva et. al. (2009) *Automated procedure for candidate compound selection in GC-MS
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78 metabolomics based on prediction of Kovats retention index*. Bioinformatics, 25 (2009), pp. 787–794
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79 </help>
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80 </tool>
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