CRAVAT 4: Cancer-Related Analysis of Variants Toolkit

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 interestAll authors declare no potential conflict of interest. Running titleInterpreting and prioritizing somatic mutations in cancer.

Pediatric Cancer Variant Pathogenicity Information Exchange (PeCanPIE): A Cloud-based Platform for Curating and Classifying Germline Variants

“…Although PeCanPIE’s features partially overlap those of other available tools (Li and Wang 2017; Masica et al 2017), it provides several new capabilities. Specifically, variant classification is tightly integrated with the rich resource of somatic mutation data in pediatric cancer, which can be explored online via the embedded ProteinPaint view.…”

“…This design also allows back end analysis pipelines to be invoked independently from PeCanPIE, for users who prefer direct or programmatic access over a graphical interface. In comparison with web-based systems (Masica et al 2017) which provide batch annotation of variants based on machine-learning scores (Carter et al 2013, 2009), PeCanPIE provides more granular annotations and individual ACMG-recommended evidence tags to facilitate interpretation of pathogenicity classifications. Via dbNSFP, PeCanPIE also provides access to REVEL (loannidis et al 2016) pathogenicity scores, which fared well in a recent comparison of algorithms for use with ACMG clinical variant interpretation guidelines (Ghosh et al 2017).…”

RASInternal Tandem Duplication Disrupts GAP-binding to Activate Oncogenic Signaling

“…Variant call files were annotated using Cravat (36,37) and Annovar (38). Annotated variant files were filtered to remove synonymous variants not affecting splice sites and any passenger somatic variants that had been recorded as polymorphisms in healthy human population databases with minor allele frequencies > 0.01.…”

Cancer genetics meets biomolecular mechanism—bridging an age‐old gulf

“…ActiveDriver [72] M Considers frequency and clustering of mutations in the context of phosphorylation signalling (phosphosites, kinase domains, etc). CRAVAT [73] M, I Integrates CHASM, VEST and different annotations. IntOGen [74] M, I Web platform for pan-cancer driver identification.…”