Genome Interpretation For Everybody

OpenCRAVAT

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OpenCRAVAT is a dynamic software platform that annotates and prioritizes genomic alterations. Initially meant to share our lab’s methodologies, it has evolved into a comprehensive resource offering approximately 300 tools, databases, and visualization options. Users can craft custom analysis pipelines via an adaptable filtering system. OpenCRAVAT is designed for widespread use, providing intuitive interfaces for researchers of varying expertise, from clinicians to lab scientists. For those requiring high-throughput genomic data processing, OpenCRAVAT integrates seamlessly into large-scale analysis workflows.

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Collaborator

Software Tools

openCRAVAT

openCRAVAT

[url]  [github]  [pub]
A modular annotation tool for genomic variants

Software Tools
VEST

VEST

[url]  [pub]
Predict pathogenic variants

Software Tools
CHASM

CHASM

Random Forest algorithm for predicting driver missense mutations.

[pub]

Publications

Walker LC, Hoya M, Wiggins GAR, Lindy A, Vincent LM, Parsons MT, Canson DM, Bis-Brewer D, Cass A, Tchourbanov A, Zimmermann H, Byrne AB, Pesaran T, Karam R, Harrison SM, Spurdle AB; ClinGen Sequence Variant Interpretation Working Group. Using the ACMG/AMP framework to capture evidence related to predicted and observed impact on splicing: Recommendations from the ClinGen SVI Splicing Subgroup. Am J Hum Genet. 2023 Jul 6;110(7):1046-1067. doi: 10.1016/j.ajhg.2023.06.002. Epub 2023 Jun 22. PMID: 37352859; PMCID: PMC10357475.
Stenton SL, Pejaver V, Bergquist T, Biesecker LG, Byrne AB, Nadeau EAW, Greenblatt MS, Harrison SM, Tavtigian SV, Radivojac P, Brenner SE, O'Donnell-Luria A; ClinGen Sequence Variant Interpretation Working Group. Assessment of the evidence yield for the calibrated PP3/BP4 computational recommendations. Genet Med. 2024 Jul 25;26(11):101213. doi: 10.1016/j.gim.2024.101213. Epub ahead of print. PMID: 39030733.
Karchin R, Radivojac P, O'Donnell-Luria A, Greenblatt MS, Tolstorukov MY, Sonkin D. Improving transparency of computational tools for variant effect prediction. Nat Genet. 2024 Jul 2. doi: 10.1038/s41588-024-01821-8. Epub ahead of print. PMID: 38956207.
The Critical Assessment of Genome Interpretation Consortium. CAGI, the critical assessment of genome interpretation, establishes progress and prospects for computational genetic variant interpretation methods. Genome Biology 2024 25(1):53. PMID: 38389099 PMCID: PMC10882881
Pagel KA, Kim R, Moad K, Busby B, Zheng L, Tokheim C, Ryan M, Karchin R. (2020) Integrated informatics analysis of cancer-related variants. JCO CCI Mar;4:310-317. doi: 10.1200/CCI.19.00132.
Brnich SE, Abou Tayoun AN, Couch FJ, Cutting GR, Greenblatt MS, Heinen CD, Kanavy DM, Luo X, McNulty SM, Starita LM, Tavtigian SV, Wright MW, Harrison SM, Biesecker LG, Berg JS; Clinical Genome Resource Sequence Variant Interpretation Working Group. Recommendations for application of the functional evidence PS3/BS3 criterion using the ACMG/AMP sequence variant interpretation framework. Genome Med. 2019 Dec 31;12(1):3. doi: 10.1186/s13073-019-0690-2.
Kasak L, Bakolitsa C, Hu Z, Yu C, Rine J, Dimster-Denk DF, Pandey G, De Baets G, Bromberg Y, Cao C, Capriotti E, Casadio R, Van Durme J, Giollo M, Karchin R, Katsonis P, Leonardi E, Lichtarge O, Martelli PL, Masica D, Mooney SD, Olatubosun A, Pal LR, Radivojac P, Rousseau F, Savojardo C, Schymkowitz J, Thusberg J, Tosatto SCE, Vihinen M, Väliaho J, Repo S, Moult J, Brenner SE, Friedberg I. Assessing Computational Predictions of the Phenotypic Effect of Cystathionine-beta-Synthase Variants Hum Mutat. 2019 Jul 13. doi: 10.1002/humu.23868. [Epub ahead of print]
Sajulga R, Mehta S, Kumar P, Johnson JE, Guerrero CR, Ryan MC, Karchin R, Jagtap PD, Griffin TJ. (2018). Bridging the Chromosome-centric and Biology/Disease-driven Human Proteome Projects: Accessible and Automated Tools for Interpreting the Biological and Pathological Impact of Protein Sequence Variants Detected via Proteogenomics. J. Proteome Res. Sep 5. doi: 10.1021/acs.jproteome.8b00404. [Epub ahead of print]
Guidugli L, Shimelis H, Masica DL, Pankratz VS, Lipton GB, Singh N, Hu C, Monteiro ANA, Lindor NM, Goldgar DE, Karchin R, Iversen ES, Couch FJ (2018) Assessment of the clinical relevance of BRCA2 missense variants by functional and computational approaches. Am. Journal of Human Genetics. S0002-9297(17)30502-5
Masica DL, Douville C, Tokheim C, Bhattacharya R, Kim R, Moad K, Ryan MC, Karchin R (2017) CRAVAT 4: Cancer-Related Analysis of Variants Toolkit. Cancer Research. Nov 1;77(21):e35-38.
Mascia DL, Karchin R (2016) Towards increasing the clinical relevance of in silico methods to predict pathogenic missense variants. PLoS Computational Biology. May 12;12(5):e1004725.
Douville C, Masica DL, Stenson PD, Cooper DN, Gygax D, Kim R, Ryan M, Karchin R (2016) Assessing the pathogenicity of insertion and deletion variants with the Variant Effect Scoring Tool (VEST-indel) Human Mutation. 37(1):28-35
International Cancer Genome Consortium Mutation Pathways and Consequences Subgroup of the Bioinformatics Analyses Working Group, Gonzalez-Perez A, Mustonen V, Reva B, Ritchie GR, Creixell P, Karchin R, Vazquez M, Fink JL, Kassahn KS, Pearson JV, Bader GD, Boutros PC, Muthuswamy L, Ouellette BF, Reimand J, Linding R, Shibata T, Valencia A, Butler A, Dronov S, Flicek P, Shannon NB, Carter H, Ding L, Sander C, Stuart JM, Stein LD, Lopez-Bigas N. (2013) Computational approaches to identify functional genetc variants in cancer genomes. Nat Methods.10(8):723-9
Wong WC, Kim D, Carter H, Diekhans M, Ryan M and Karchin R (2011) CHASM and SNVBox: toolkit for detecting biologically important single nucleotide mutations in cancer. Bioinformatics. 27(15):2147-8
Cline MS and Karchin R (2011) Using bioinformatics to predict the functional impact of SNVs. Bioinformatics. Feb 15;27(4):441-8. Dec 15
Karchin R. (2009) Next generation tools for the annotation of human SNPs. Briefing in Bioinformatics. 10(1):35-52