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CRAVAT 4: Cancer-Related Analysis of Variants Toolkit

David L. Masica, Christopher Douville, Collin Tokheim, Rohit Bhattacharya, RyangGuk Kim, Kyle Moad, Michael C. Ryan, Rachel Karchin
doi: https://doi.org/10.1101/162859
David L. Masica
‡Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD
¥The Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD
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Christopher Douville
‡Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD
¥The Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD
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Collin Tokheim
‡Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD
¥The Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD
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Rohit Bhattacharya
¥The Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD
§Department of Computer Science, The Johns Hopkins University, Baltimore, MD
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RyangGuk Kim
†In Silico Solutions, Falls Church, VA
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Kyle Moad
†In Silico Solutions, Falls Church, VA
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Michael C. Ryan
†In Silico Solutions, Falls Church, VA
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Rachel Karchin
‡Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD
¥The Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD
£Department of Oncology, The Johns Hopkins University School of Medicine, Baltimore, MD
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Abstract

Cancer sequencing studies are increasingly comprehensive and well-powered, returning long lists of somatic mutations that can be difficult to sort and interpret. Diligent analysis and quality control can require multiple computational tools of distinct utility and producing disparate output, creating additional challenges for the investigator. The Cancer-Related Analysis of Variants Toolkit (CRAVAT) is an evolving suite of informatics tools for mutation interpretation that includes mutation projecting and quality control, impact prediction and extensive annotation, gene- and mutation-level interpretation including joint prioritization of all nonsilent consequence types, and structural and mechanistic visualization. Results from CRAVAT submissions are explored in an interactive, user-friendly web-environment with dynamic filtering and sorting designed to highlight the most informative mutation, even in the context of very large studies. CRAVAT can be run on a public web-portal, in the cloud, or downloaded for local use, and is easily integrated with other methods for cancer omics analysis.

Conflict of interest All authors declare no potential conflict of interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted July 12, 2017.
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CRAVAT 4: Cancer-Related Analysis of Variants Toolkit
David L. Masica, Christopher Douville, Collin Tokheim, Rohit Bhattacharya, RyangGuk Kim, Kyle Moad, Michael C. Ryan, Rachel Karchin
bioRxiv 162859; doi: https://doi.org/10.1101/162859
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CRAVAT 4: Cancer-Related Analysis of Variants Toolkit
David L. Masica, Christopher Douville, Collin Tokheim, Rohit Bhattacharya, RyangGuk Kim, Kyle Moad, Michael C. Ryan, Rachel Karchin
bioRxiv 162859; doi: https://doi.org/10.1101/162859

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