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1 <tool id="transFIC_web" name="TransFIC">
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2 <description>TransFIC web service</description>
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4
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3 <requirements>
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4 <requirement type="package" version="2.2.1">requests</requirement>
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5 <requirement type="package" version="7.19.3.1">pycurl</requirement>
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6 <requirement type="package" version="4.1.0">beautifulsoup4</requirement>
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7 <requirement type="python-module">requests</requirement>
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8 <requirement type="python-package">pycurl</requirement>
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9 <requirement type="python-package">bs4</requirement>
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10 </requirements>
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3
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11 <command interpreter="python">
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12 transFIC_web.py --input $input --output $output
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13 </command>
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14 <inputs>
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15 <param name="input" format="text" type="data" label="Inout Variants"/>
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16 </inputs>
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17 <outputs>
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18 <data name="output" format="tabular"/>
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19 </outputs>
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20 <tests>
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21 <test>
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22 <param name="input" value="condel_input.tsv"/>
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23 <output name="output" file="transfic_output.csv"/>
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24 </test>
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25 </tests>
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26 <help>
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27 **What it does**
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28
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29 This script calls TransFIC web api at http://bg.upf.edu/transfic/
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30
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31 TransFIC stands for TRANsformed Functional Impact for Cancer.
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32 It is a method to transform Functional Impact Scores taking into account the differences in basal tolerance to germline SNVs of genes that belong to
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33 different functional classes. This transformation allows to use the scores provided by well-known tools (e.g. SIFT, Polyphen2, MutationAssessor)
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34 to rank the functional impact of cancer somatic mutations. Mutations with greater transFIC are more likely to be cancer drivers.
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35
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36
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37 **How does it work**
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38
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39
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40 TransFIC takes as input the Functional Impact Score of a somatic mutation provided by one of the aforementioned tools.
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41 It then compares that score to the distribution of scores of germline SNVs observed in genes with similar functional
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42 annotations (for instance genes with the same molecular function as provided by the Gene Ontologies).
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43 The score is thus transformed using the Zscore formula.
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44 The result is that mutations in genes that are less tolerant to germline SNVs are amplified,
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45 while the scores of mutations on relatively tolerant genes are decreased.
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46
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47 **Input**
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48
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49 There are two main formats allowed:
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50
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51
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52 SNVs may be submitted for analysis both in chromosome and protein coordinates.
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53
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54
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55 The chromosome coordinates (hg19) input must follow this format:
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56
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57
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58 [CHROMOSOME] [START] [END] [MUTANT_NUCLEOTIDE]
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59
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60
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61
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62 The END column is the same as the START for SNVs.
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63 Those four columns must be separated by tabs. Also a fifth column can optionally be added with the Variant name
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64
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65
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66 Ex:
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67
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68 9 32473058 32473058 A
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69
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70 7 43918688 43918688 C
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71
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72 Additionally, the input could be composed by two columns the strand of the SNV and an identifier:
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73
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74 [PROTEIN_ID][variant]
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75
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76 Also tab separated. Currently only Uniprot, RefSeq_Peptide and Ensembl identifiers are recognized by the webserver.
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77
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78 The variant column must contain the following information (in this order ): change_position, reference_aminoacid and changed_aminoacid
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79
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80 **Citation**
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81
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82 If you use this Galaxy tool in work leading to a scientific publication please cite:
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83
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84 Gonzalez-Perez A, Deu-Pons J and Lopez-Bigas N. Improving the prediction of the functional impact of cancer mutations by baseline tolerance transformation
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85 Genome Medicine 2012. 4:89 doi:10.1186/gm390s
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86 </help>
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3
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87 </tool>
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88
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