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1 <tool id="condel_web" name="condel">
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2 <description>Condel web service</description>
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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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11 <command interpreter="python">
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12 condel_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="Input 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="condel_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 condel web api at http://bg.upf.edu/condel/
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30
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31 Condel stands for CONsensus DELeteriousness score of non-synonymous single nucleotide variants (SNVs).
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32 The idea behind it is to integrate the output of computational tools aimed at assessing the impact of non synonymous SNVs on protein function.
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33 To do this, it computes a weighted average of the scores (WAS) of these tools : SIFT, Polyphen2, MAPP, LogR Pfam E-value
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34 (implemented ad hoc following the instructions at Clifford RJ, Edmonson MN, Nguyen C, and Buetow KH (2004)
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35 Large-scale analysis of non-synonymous coding region single nucleotide polymorphisms. Bioinformatics 20, 1006-1014) and MutationAssessor
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36
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37 **How does it work**
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38
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39 The scores of different methods are weighted using the complementary cumulative distributions produced by the five methods on a
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40 dataset of approximately 20000 missense SNPs, both deleterious and neutral. The probability that a predicted deleterious mutation is not a
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41 false positive of the method and the probability that a predicted neutral mutation is not a false negative are employed as weights.
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42
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43 **Input**
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44
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45 There are two main formats allowed:
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46
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47
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48 SNVs may be submitted for analysis both in chromosome and protein coordinates.
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49
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50
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51 The chromosome coordinates (hg19) input must follow this format:
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52
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53
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54 [CHROMOSOME] [START] [END] [MUTANT_NUCLEOTIDE]
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55
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56
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57
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58 The END column is the same as the START for SNVs.
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59 Those four columns must be separated by tabs. Also a fifth column can optionally be added with the Variant name
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60
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61
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62 Ex:
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63
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64 9 32473058 32473058 A
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65
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66 7 43918688 43918688 C
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67
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68 Additionally, the input could be composed by two columns the strand of the SNV and an identifier:
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69
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70 [PROTEIN_ID][variant]
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71
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72 Also tab separated. Currently only Uniprot, RefSeq_Peptide and Ensembl identifiers are recognized by the webserver.
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73
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74 The variant column must contain the following information (in this order ): change_position, reference_aminoacid and changed_aminoacid
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75
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76 Ex:
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77
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78 EGFR_HUMAN R521K
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79
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80 EGFR_HUMAN R98Q
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81
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82 .. class:: warningmark
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83
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84 Note
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85
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86 Please, note that the Variant Effect Predictor assumes that the allele submitted is coded in the forward strand.
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87
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88 If you are assessing the effect of variants coded in the reverse strand, please introduce the complementary nucleotide instead.
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89
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90 **Citation**
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91
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92
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93 If you use this tool please cite:
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94
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95
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96 Improving the Assessment of the Outcome of Nonsynonymous SNVs with a Consensus Deleteriousness Score, Condel (2011) Abel González-Pérez and Nuria López-Bigas, American Journal of Human Genetics 10.1016/j.ajhg.2011.03.004
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97
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98
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99 </help>
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100 </tool>
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101
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