Mercurial > repos > saketkc > chasm_web
diff 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 |
| parents | |
| children | 89407d4da3ca |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tools/chasm/chasm_web.xml Fri Nov 01 02:07:53 2013 +0530 @@ -0,0 +1,94 @@ +<tool id="chasm_webservice" name="CHASM Webservice" version="1.0.0" hidden="false"> + <requirements> + <requirement type="python-module">requests</requirement> + <requirement type="python-module">xlrd</requirement> + </requirements> + <description>CHASM score using CRAVAT webservice</description> + <command interpreter="python"> + 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 + </command> + <inputs> + <param format="txt" name="input" type="data" label="Variants File" /> + <param name="analysis_type" type="select" label="Choose analysis type" help=" + Cancer driver analysis predicts whether\ + the submitted variants are cancer drivers.\ + Functional effect analysis predicts whether\ + the submitted variants will have any\ + functional effect on their translated proteins.\ + Annotation only provides\ + GeneCard and PubMed information on\ + the genes containing the submitted variants."> + <option value="driver">Cancer driver analysis</option> + <option value="functional">Functional effect analysis</option> + <option value="geneannotationonly">Annotation only</option> + </param> + + <param name="gene_annotation" type="select" label="Include Gene annotation"> + <option value="no">No</option> + <option value="yes">Yes</option> + </param> + + <param name="tissue_type" type="select" label="Tissue Type"> + <option value="Bladder">Bladder</option> + <option value="Blood-Lymphocyte">Blood-Lymphocyte</option> + <option value="Blood-Myeloid">Blood-Myeloid</option> + <option value="Brain-Cerebellum">Brain-Cerebellum</option> + <option value="Brain-Glioblastoma_Multiforme">Brain-Glioblastoma_Multiforme</option> + <option value="Brain-Lower_Grade_Glioma">Brain-Lower_Grade_Glioma</option> + <option value="Breast">Breast</option> + <option value="Cervix">Cervix</option> + <option value="Colon">Colon</option> + <option value="Head_and_Neck">Head_and_Neck</option> + <option value="Kidney-Chromophobe">Kidney-Chromophobe</option> + <option value="Kidney-Clear_Cell">Kidney-Clear_Cell</option> + <option value="Kidney-Papiallary_Cell">Kidney-Papiallary_Cell</option> + <option value="Liver-Nonviral">Liver-Nonviral</option> + <option value="Liver-Viral">Liver-Viral</option> + <option value="Lung-Adenocarcinoma">Lung-Adenocarcinoma</option> + <option value="Lung-Squamous_Cell">Lung-Squamous_Cell</option> + <option value="Melanoma">Melanoma</option> + <option value="Other">Other</option> + <option value="Ovary">Ovary</option> + <option value="Pancreas">Pancreas</option> + <option value="Prostate-Adenocarcinoma">Prostate-Adenocarcinoma</option> + <option value="Rectum">Rectum</option> + <option value="Skin">Skin</option> + <option value="Stomach">Stomach</option> + <option value="Thyroid">Thyroid</option> + <option value="Uterus">Uterus</option> + </param> + </inputs> + <outputs> + <data format="tabular" name="gene_analysis_out"/> + <data format="tabular" name="variant_analysis_out" /> + <data format="tabular" name="amino_acid_level_analysis_out" /> + <data format="tabular" name="error_file"/> + </outputs> + <help> + **What it does** + * CHASM (Cancer-specific High-throughput Annotation of Somatic Mutations) is a method that predicts the functional significance of somatic missense variants + observed in the genomes of cancer cells, allowing variants to be prioritized in subsequent functional studies, based on the probability that they confer + increased fitness to a cancer cell. CHASM uses a machine learning method called Random Forest to distinguish between driver and passenger somatic missense variation. + 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, + according to passenger base substitution frequencies estimated for a specific tumor type. Each variant is represented by a list of features, + including amino acid substitution properties, alignment-based estimates of conservation at the variant position, predicted local structure and annotations from + the UniProt Knowledgebase. Only missense mutations are analyzed by CHASM. For more information on CHASM, please visit http://wiki.chasmsoftware.org + + * SNVGet retrieves selected predictive features for a variant. Features can be broadly categorized into 3 types: + - Amino Acid Substitution features + - Protein-based position-specific features + - Exon-specific features + Only missense mutations are analyzed by SNVGet. For more information on SNVBox (database made with SNVGet), please visit http://wiki.chasmsoftware.org + * VEST is a method that predicts the functional effect of a variant. + + + + **Citation** + If you use this Galaxy tool in work leading to a scientific publication please cite: + + Carter, Hannah, et al. "Cancer-specific high-throughput annotation of somatic mutations: computational prediction of driver missense mutations." + Cancer research 69.16 (2009): 6660-6667. + + Wong, Wing Chung, et al. "CHASM and SNVBox: toolkit for detecting biologically important single nucleotide mutations in cancer." + Bioinformatics 27.15 (2011): 2147-2148. +</tool>