OpenCRAVAT, an open source collaborative platform for the annotation of human genetic variation

Abstract

PURPOSEThe modern researcher is confronted with hundreds of published methods to interpret genetic variants. There are databases of genes and variants, phenotype-genotype relationships, algorithms that score and rank genes, and in silico variant effect prediction tools. Because variant prioritization is a multi-factorial problem, a welcome development in the field has been the emergence of decision support frameworks, which make it easier to integrate multiple resources in an interactive environment. Current decision support frameworks are typically limited by closed proprietary architectures, access to a restricted set of tools, lack of customizability, web dependencies that expose protected data, or limited scalability. METHODSWe present OpenCRAVAT, a new open source, scalable decision support system for variant and gene prioritization. We have designed the resource catalog to be open and modular to maximize community and developer involvement, and as a result the catalog is being actively developed and growing every month. Resources made available via the store are well-suited for analysis of cancer, as well as Mendelian and complex diseases. RESULTS High-throughput annotation of genetic variation with OpenCRAVAT 2OpenCRAVAT offers both command line utility and dynamic GUI, allowing users to install with a single command, easily download tools from an extensive resource catalog, create customized pipelines, and explore results in a richly detailed viewing environment. We present several case studies to illustrate the design of custom workflows to prioritize genes and variants. CONCLUSION OpenCRAVAT is distinguished from similar tools by its capabilities to access and integrate an unprecedented amount of diverse data resources and computational prediction methods, which span germline, somatic, common, rare, coding and noncoding variants. OpenCRAVAT is freely available at https://opencravat.org

Identifying Cancer Drivers Using DRIVE: A Feature-Based Machine Learning Model for a Pan-Cancer Assessment of Somatic Missense Mutations

“…Consequently, all features were extracted again using the above-mentioned methodology, comprising 4244 mutations with a complete feature set. These mutations were inputted into CHASMplus (v1.2.0) classifier via OpenCRAVAT web server [ 40 ], this approach being considered one of the best scoring state-of-the-art models which has outclassed other methodologies [ 13 ]. The output comprised 1578 mutations and a score that represents the probability of a mutation of being a driver.…”

“…The combined annotation was mainly based on the Catalogue Of Somatic Mutations In Cancer (COSMIC, v75), classifying mutations into neutral and non-neutral [ 39 ], thus for the present evaluation, these represented the true class labels. Performance assessment was done using the random forest model to predict on the benchmark dataset, while also using CHASMplus (v1.2.0) through Open Custom Ranked Analysis of Variants Toolkit (OpenCRAVAT) web server for extracting the mutation probability of being a driver [ 40 ].…”

Elucidating the heterogeneity of immunotherapy response and immune-related toxicities by longitudinal ctDNA and immune cell compartment tracking in lung cancer

“…Plasma variants were cross-referenced against the Catalogue of Somatic Mutations in Cancer (COSMIC) v95 database for the annotation of hotspot alterations using OpenCRAVAT (26). A conservative COSMIC frequency threshold of 25 hits was used to define a lung cancer hotspot with high confidence (8,27).…”

CCAS: One-stop and comprehensive annotation system for individual cancer genome at multi-omics level

“…ANNOVAR (Wang et al, 2010), Ensembl-VEP (McLaren et al, 2016), and SnpEff (Cingolani et al, 2012) were developed as annotation tools for variants function based on population frequencies in normal or disease cohorts, as well as damage predictions at genomic level. PCAWG-Scout (Goldman et al, 2020a), UCSC Xena (Goldman et al, 2020b), and OpenCRAVAT (Pagel et al, 2020) were designed for complex visualization and analysis services of large scale cancer datasets. PCGR (Nakken et al, 2018), GenomeChronicler (Guerra-Assuncao et al, 2020), and PORI (Reisle et al, 2022) were developed for cancer genome annotation at the individual patient level, providing many useful functions, such as mutation signature analysis, mutation burden analysis, drug interactions, as well as clinical trials analysis.…”