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comparison tools/chasm/chasm_web.xml @ 1:8eaaa7f6b619

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author Saket Choudhary <saketkc@gmail.com>
date Fri, 01 Nov 2013 02:07:53 +0530
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1 <tool id="chasm_webservice" name="CHASM Webservice" version="1.0.0" hidden="false">
2 <requirements>
3 <requirement type="python-module">requests</requirement>
4 <requirement type="python-module">xlrd</requirement>
5 </requirements>
6 <description>CHASM score using CRAVAT webservice</description>
7 <command interpreter="python">
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
9 </command>
10 <inputs>
11 <param format="txt" name="input" type="data" label="Variants File" />
12 <param name="analysis_type" type="select" label="Choose analysis type" help="
13 Cancer driver analysis predicts whether\
14 the submitted variants are cancer drivers.\
15 Functional effect analysis predicts whether\
16 the submitted variants will have any\
17 functional effect on their translated proteins.\
18 Annotation only provides\
19 GeneCard and PubMed information on\
20 the genes containing the submitted variants.">
21 <option value="driver">Cancer driver analysis</option>
22 <option value="functional">Functional effect analysis</option>
23 <option value="geneannotationonly">Annotation only</option>
24 </param>
25
26 <param name="gene_annotation" type="select" label="Include Gene annotation">
27 <option value="no">No</option>
28 <option value="yes">Yes</option>
29 </param>
30
31 <param name="tissue_type" type="select" label="Tissue Type">
32 <option value="Bladder">Bladder</option>
33 <option value="Blood-Lymphocyte">Blood-Lymphocyte</option>
34 <option value="Blood-Myeloid">Blood-Myeloid</option>
35 <option value="Brain-Cerebellum">Brain-Cerebellum</option>
36 <option value="Brain-Glioblastoma_Multiforme">Brain-Glioblastoma_Multiforme</option>
37 <option value="Brain-Lower_Grade_Glioma">Brain-Lower_Grade_Glioma</option>
38 <option value="Breast">Breast</option>
39 <option value="Cervix">Cervix</option>
40 <option value="Colon">Colon</option>
41 <option value="Head_and_Neck">Head_and_Neck</option>
42 <option value="Kidney-Chromophobe">Kidney-Chromophobe</option>
43 <option value="Kidney-Clear_Cell">Kidney-Clear_Cell</option>
44 <option value="Kidney-Papiallary_Cell">Kidney-Papiallary_Cell</option>
45 <option value="Liver-Nonviral">Liver-Nonviral</option>
46 <option value="Liver-Viral">Liver-Viral</option>
47 <option value="Lung-Adenocarcinoma">Lung-Adenocarcinoma</option>
48 <option value="Lung-Squamous_Cell">Lung-Squamous_Cell</option>
49 <option value="Melanoma">Melanoma</option>
50 <option value="Other">Other</option>
51 <option value="Ovary">Ovary</option>
52 <option value="Pancreas">Pancreas</option>
53 <option value="Prostate-Adenocarcinoma">Prostate-Adenocarcinoma</option>
54 <option value="Rectum">Rectum</option>
55 <option value="Skin">Skin</option>
56 <option value="Stomach">Stomach</option>
57 <option value="Thyroid">Thyroid</option>
58 <option value="Uterus">Uterus</option>
59 </param>
60 </inputs>
61 <outputs>
62 <data format="tabular" name="gene_analysis_out"/>
63 <data format="tabular" name="variant_analysis_out" />
64 <data format="tabular" name="amino_acid_level_analysis_out" />
65 <data format="tabular" name="error_file"/>
66 </outputs>
67 <help>
68 **What it does**
69 * CHASM (Cancer-specific High-throughput Annotation of Somatic Mutations) is a method that predicts the functional significance of somatic missense variants
70 observed in the genomes of cancer cells, allowing variants to be prioritized in subsequent functional studies, based on the probability that they confer
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.
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,
73 according to passenger base substitution frequencies estimated for a specific tumor type. Each variant is represented by a list of features,
74 including amino acid substitution properties, alignment-based estimates of conservation at the variant position, predicted local structure and annotations from
75 the UniProt Knowledgebase. Only missense mutations are analyzed by CHASM. For more information on CHASM, please visit http://wiki.chasmsoftware.org
76
77 * SNVGet retrieves selected predictive features for a variant. Features can be broadly categorized into 3 types:
78 - Amino Acid Substitution features
79 - Protein-based position-specific features
80 - Exon-specific features
81 Only missense mutations are analyzed by SNVGet. For more information on SNVBox (database made with SNVGet), please visit http://wiki.chasmsoftware.org
82 * VEST is a method that predicts the functional effect of a variant.
83
84
85
86 **Citation**
87 If you use this Galaxy tool in work leading to a scientific publication please cite:
88
89 Carter, Hannah, et al. "Cancer-specific high-throughput annotation of somatic mutations: computational prediction of driver missense mutations."
90 Cancer research 69.16 (2009): 6660-6667.
91
92 Wong, Wing Chung, et al. "CHASM and SNVBox: toolkit for detecting biologically important single nucleotide mutations in cancer."
93 Bioinformatics 27.15 (2011): 2147-2148.
94 </tool>