Welcome to open-cravat’s documentation!

OpenCRAVAT

OpenCRAVAT is a python package that performs genomic variant interpretation including variant impact, annotation, and scoring. There is a web-based version of OpenCRAVAT (https://run.opencravat.org) but it can also be installed locally and is easy to integrate into bioinformatics pipelines. OpenCRAVAT has a modular architecture with a wide variety of analysis modules that can be selected and installed/run based on the needs of a given study. The modules are made available via the CRAVAT Store and are developed both by the CRAVAT team and the broader variant analysis community. OpenCRAVAT is a product of the Karchin Lab at Johns Hopkins University with funding provided by the National Cancer Institute’s ITCR program.

Overview

OpenCRAVAT overview

OpenCRAVAT is a modular python package that is available in the pip PyPI repository. It takes a file of genomic variants as input. The most common input format is a VCF file but other formats are supported including dbSNP identifiers, 23&Me and Ancestry.com file formats.

The analysis performed by OpenCRAVAT depends upon user-selected annotation and visualization options, available for download from the free OpenCRAVAT Store. In addition to the interactive user interface, OpenCRAVAT provides several output formats including text reports, Excel spreadsheets, and a SQLite database of results used by cravat_view.

OpenCRAVAT Processing

OpenCRAVAT schematic

When the pipeline program is run, it will execute a series of modules required for variant analysis. First, the appropriate converter will be run to parse the input variant file. Next, a mapper module will determine the transcripts and associated genes affected by each variant including protein impact. Then OpenCRAVAT runs all of the requested/installed annotation modules and after all annotation is complete, an aggregator program collects and collates the results into a SQLite database. Finally, reporter modules are run to produce the requested format of results.

Available Modules

As of 5/22/2025, OpenCRAVAT has the following annotators available, with more on the way.

  • Gene-level annotators: BioGRID, Cancer Gene Census, Cancer Gene Landscape, Cancer Genome Interpreter, Constrained Coding Regions, Clinical Genomic Database, CIViC Gene, ClinGen Gene, COSMIC Gene, Essential Genes, ExAC Gene and CNV, Gene Ontology, GHIS, gnomAD, GTEx, HaploReg, HGDP, Human Phenotype Ontology, IntAct, InterPro, LoFtool, LINSIGHT, MuPIT, Mutpanning, NCBI Gene, NDEx, P(rec), p(HI), PangaloDB, Promoter IR, PubMed, RVIS, TARGET, UniProt, VEST

  • Variant-level annotators: All of Us, ALoFT, AlphaMissense, Arrhythmia Channelopathy Variants, BayesDel, BRCA1 Saturation Genome Editing Scores, CADD, CADD Exome, Cancer Hotspots, CardioBoost, CEDAR, CIViC, ClinGen Allele Registry, ClinPred, ClinVar, ClinVar ACMG, ClinVar, COSMIC, CScape, CScape Coding, Cardiovascular Disease Knowledge Portal, DANN, DANN Coding, dbCID: Database of Cancer Driver InDels, dbscSNV, dbSNP, dbSNP Common, Denovo-DB, DGIdb: The Drug Interaction Database, DITTO, ESM1b, EVE, FATHMM, FATHMM MKL, FATHMM XF, FATHMM XF Coding, fitCons, Flanking Sequence, FunSeq2, GERP++, Geuvadis eQTLs, gMVP, GRASP, GTEx eQTLs, GWAS Catalog, HaploReg African, HaploReg American, HaploReg Asian, HaploReg European, hg19 coordinates, LINSIGHT, LitVar Full, Likelihood Ratio Test, MetaLR, MetaRNN, MetaSVM, MISTIC (MISsense deleTeriousness predICtor), MITOMAP, MuPIT, Mutation Assessor, MutationTaster, MutPred, MutPred-Indel, NDEx Congenital Heart Disease, NDEx SIGNOR, OMIM, PharmGKB, Phast Cons, PhD-SNPg, PhyloP, PolyPhen-2, PrimateAI, Regeneron, REVEL, Segway, SIFT, SiPhy, SpliceAI, Uniprot Domain, VARITY_R, VEST4

  • Converters (input formats): TSV, VCF, HGVS, dbSNP, ClinGen Allele Registry, Ancestry.com, 23andMe, FamilyTreeDNA

  • Reporters (output formats): Text format, Excel, TSV, CSV, Annotated VCF, Pandas, RData

System Capabilities

In most cases, OpenCRAVAT can process approximately 1 million variants per hour. This estimate assumes that 2/3 of the input variants are in coding regions, approximately ten annotation modules, and the system is running on an at least 4 year old laptop with a solid state drive. Runtimes depend heavily on disk speed. A mechanical hard drive will perform about 1/3 to 1/4 as well as an SSD. Most modern processors are equivalent since the disk will bottleneck annotation speed before the processor. However, processors with fewer than four cores may see reduced runtimes. Memory size is not typically a limitation.

Getting Started

For a simple introduction to running OpenCRAVAT, please consult the Quickstart guide.

How to cite

Pagel KA et al. Integrated Informatics Analysis of Cancer-Related Variants. JCO Clinical Cancer Informatics 2020 4, 310-317.

OpenCRAVAT users are encouraged to cite individual annotations used in their study analysis.

Indices and tables