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Cancer Res. 2017 Nov 1;77(21):e35-e38. doi: 10.1158/0008-5472.CAN-17-0338.

CRAVAT 4: Cancer-Related Analysis of Variants Toolkit.

Author information

1
Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, Maryland.
2
The Institute for Computational Medicine, The Johns Hopkins University, Baltimore, Maryland.
3
Department of Computer Science, The Johns Hopkins University, Baltimore, Maryland.
4
In Silico Solutions, Falls Church, Virginia.
5
Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, Maryland. rkarchi1@jhmi.edu.
6
Department of Oncology, The Johns Hopkins University School of Medicine, Baltimore, Maryland.

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 mapping and quality control, impact prediction and extensive annotation, gene- and mutation-level interpretation, including joint prioritization of all nonsilent mutation 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 mutations, 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. Cancer Res; 77(21); e35-38. ©2017 AACR.

PMID:
29092935
PMCID:
PMC5850945
DOI:
10.1158/0008-5472.CAN-17-0338
[Indexed for MEDLINE]
Free PMC Article

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