CRAVAT: cancer-related analysis of variants toolkit
Open Access
- 16 January 2013
- journal article
- research article
- Published by Oxford University Press (OUP) in Bioinformatics
- Vol. 29 (5), 647-648
- https://doi.org/10.1093/bioinformatics/btt017
Abstract
Summary: Advances in sequencing technology have greatly reduced the costs incurred in collecting raw sequencing data. Academic laboratories and researchers therefore now have access to very large datasets of genomic alterations but limited time and computational resources to analyse their potential biological importance. Here, we provide a web-based application, Cancer-Related Analysis of Variants Toolkit, designed with an easy-to-use interface to facilitate the high-throughput assessment and prioritization of genes and missense alterations important for cancer tumorigenesis. Cancer-Related Analysis of Variants Toolkit provides predictive scores for germline variants, somatic mutations and relative gene importance, as well as annotations from published literature and databases. Results are emailed to users as MS Excel spreadsheets and/or tab-separated text files. Availability:http://www.cravat.us/ Contact:karchin@jhu.edu Supplementary information: Supplementary data are available at Bioinformatics online.Keywords
This publication has 15 references indexed in Scilit:
- Identifying Mendelian disease genes with the Variant Effect Scoring ToolBMC Genomics, 2013
- The 1000 Genomes Project: data management and community accessNature Methods, 2012
- Ensembl 2012Nucleic Acids Research, 2011
- KEGG for integration and interpretation of large-scale molecular data setsNucleic Acids Research, 2011
- GIFtS: annotation landscape analysis with GeneCardsBMC Bioinformatics, 2009
- Cancer-Specific High-Throughput Annotation of Somatic Mutations: Computational Prediction of Driver Missense MutationsCancer Research, 2009
- Next generation tools for the annotation of human SNPsBriefings in Bioinformatics, 2009
- The Catalogue of Somatic Mutations in Cancer (COSMIC)Current Protocols in Human Genetics, 2008
- Random ForestsMachine Learning, 2001
- Shape Quantization and Recognition with Randomized TreesNeural Computation, 1997