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1 <tool id="chasm_webservice" name="CHASM Webservice" version="1.0.0" hidden="false">
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2 <requirements>
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3 <requirement type="python-module">requests</requirement>
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4 <requirement type="python-module">xlrd</requirement>
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5 </requirements>
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6 <description>CHASM score using CRAVAT webservice</description>
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7 <command interpreter="python">
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8 chasm_web.py --path $input --analysis_type $analysis_type --cancertype $tissue_type --email $__user_email__ --gene_analysis_out $gene_analysis_out --variant_analysis_out $variant_analysis_out --amino_acid_level_analysis_out $amino_acid_level_analysis_out --error_file $error_file
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9 </command>
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10 <inputs>
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11 <param format="txt" name="input" type="data" label="Variants File" />
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12 <param name="analysis_type" type="select" label="Choose analysis type" help="
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13 Cancer driver analysis predicts whether\
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14 the submitted variants are cancer drivers.\
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15 Functional effect analysis predicts whether\
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16 the submitted variants will have any\
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17 functional effect on their translated proteins.\
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18 Annotation only provides\
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19 GeneCard and PubMed information on\
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20 the genes containing the submitted variants.">
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21 <option value="driver">Cancer driver analysis</option>
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22 <option value="functional">Functional effect analysis</option>
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23 <option value="geneannotationonly">Annotation only</option>
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24 </param>
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25
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26 <param name="gene_annotation" type="select" label="Include Gene annotation">
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27 <option value="no">No</option>
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28 <option value="yes">Yes</option>
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29 </param>
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30
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31 <param name="tissue_type" type="select" label="Tissue Type">
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32 <option value="Bladder">Bladder</option>
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33 <option value="Blood-Lymphocyte">Blood-Lymphocyte</option>
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34 <option value="Blood-Myeloid">Blood-Myeloid</option>
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35 <option value="Brain-Cerebellum">Brain-Cerebellum</option>
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36 <option value="Brain-Glioblastoma_Multiforme">Brain-Glioblastoma_Multiforme</option>
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37 <option value="Brain-Lower_Grade_Glioma">Brain-Lower_Grade_Glioma</option>
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38 <option value="Breast">Breast</option>
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39 <option value="Cervix">Cervix</option>
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40 <option value="Colon">Colon</option>
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41 <option value="Head_and_Neck">Head_and_Neck</option>
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42 <option value="Kidney-Chromophobe">Kidney-Chromophobe</option>
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43 <option value="Kidney-Clear_Cell">Kidney-Clear_Cell</option>
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44 <option value="Kidney-Papiallary_Cell">Kidney-Papiallary_Cell</option>
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45 <option value="Liver-Nonviral">Liver-Nonviral</option>
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46 <option value="Liver-Viral">Liver-Viral</option>
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47 <option value="Lung-Adenocarcinoma">Lung-Adenocarcinoma</option>
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48 <option value="Lung-Squamous_Cell">Lung-Squamous_Cell</option>
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49 <option value="Melanoma">Melanoma</option>
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50 <option value="Other">Other</option>
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51 <option value="Ovary">Ovary</option>
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52 <option value="Pancreas">Pancreas</option>
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53 <option value="Prostate-Adenocarcinoma">Prostate-Adenocarcinoma</option>
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54 <option value="Rectum">Rectum</option>
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55 <option value="Skin">Skin</option>
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56 <option value="Stomach">Stomach</option>
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57 <option value="Thyroid">Thyroid</option>
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58 <option value="Uterus">Uterus</option>
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59 </param>
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60 </inputs>
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61 <outputs>
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62 <data format="tabular" name="gene_analysis_out"/>
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63 <data format="tabular" name="variant_analysis_out" />
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64 <data format="tabular" name="amino_acid_level_analysis_out" />
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65 <data format="tabular" name="error_file"/>
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66 </outputs>
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67 <help>
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68 **What it does**
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69 * CHASM (Cancer-specific High-throughput Annotation of Somatic Mutations) is a method that predicts the functional significance of somatic missense variants
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70 observed in the genomes of cancer cells, allowing variants to be prioritized in subsequent functional studies, based on the probability that they confer
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71 increased fitness to a cancer cell. CHASM uses a machine learning method called Random Forest to distinguish between driver and passenger somatic missense variation.
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72 The Random Forest is trained on a positive class of drivers curated from the COSMIC database and a negative class of passengers, generated in silico,
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73 according to passenger base substitution frequencies estimated for a specific tumor type. Each variant is represented by a list of features,
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74 including amino acid substitution properties, alignment-based estimates of conservation at the variant position, predicted local structure and annotations from
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75 the UniProt Knowledgebase. Only missense mutations are analyzed by CHASM. For more information on CHASM, please visit http://wiki.chasmsoftware.org
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76
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77 * SNVGet retrieves selected predictive features for a variant. Features can be broadly categorized into 3 types:
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78 - Amino Acid Substitution features
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79 - Protein-based position-specific features
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80 - Exon-specific features
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81 Only missense mutations are analyzed by SNVGet. For more information on SNVBox (database made with SNVGet), please visit http://wiki.chasmsoftware.org
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82 * VEST is a method that predicts the functional effect of a variant.
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83
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84
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85
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86 **Citation**
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87 If you use this Galaxy tool in work leading to a scientific publication please cite:
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88
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89 Carter, Hannah, et al. "Cancer-specific high-throughput annotation of somatic mutations: computational prediction of driver missense mutations."
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90 Cancer research 69.16 (2009): 6660-6667.
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91
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92 Wong, Wing Chung, et al. "CHASM and SNVBox: toolkit for detecting biologically important single nucleotide mutations in cancer."
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93 Bioinformatics 27.15 (2011): 2147-2148.
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94 </help>
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95 </tool>
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