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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="python-module">requests</requirement>
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6 </requirements>
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7 <command interpreter="python">
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8 condel_web.py --input $input --output $output
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9 </command>
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10 <inputs>
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11 <param name="input" format="text" type="data" label="Input Variants" />
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12 </inputs>
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13 <outputs>
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14 <data name="output" format="tabular"/>
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15 </outputs>
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16 <tests>
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17 <test>
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18 <param name="input" value="condel_input.txt"/>
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19 <output name="output" file="condel_output.txt"/>
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20 </test>
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21 </tests>
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22 <help>
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23 **What it does**
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24
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25 This script calls condel web api at http://bg.upf.edu/condel/
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26
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27 Condel stands for CONsensus DELeteriousness score of non-synonymous single nucleotide variants (SNVs).
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28 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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29 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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30 (implemented ad hoc following the instructions at Clifford RJ, Edmonson MN, Nguyen C, and Buetow KH (2004)
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31 Large-scale analysis of non-synonymous coding region single nucleotide polymorphisms. Bioinformatics 20, 1006-1014) and MutationAssessor
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32
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33 **How does it work**
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34
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35 The scores of different methods are weighted using the complementary cumulative distributions produced by the five methods on a
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36 dataset of approximately 20000 missense SNPs, both deleterious and neutral. The probability that a predicted deleterious mutation is not a
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37 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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38
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39 **Citation**
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40
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41
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42 If you use this tool please cite:
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43
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44
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45 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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46
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47
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48 </help>
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49 </tool>
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50
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