open-cravat
open-cravat aggregates annotations and computational predictions to prioritize genes and variants for interpretation of germline and somatic, coding and non-coding genetic variation.
Key Features:
- Open and Modular Resource Catalog: Provides an extensible catalog of annotation and analysis modules that can be added or updated to expand available resources and predictors.
- Extensive Data Integration: Integrates a wide array of data resources to support analysis of germline, somatic, common, rare, coding, and non-coding variants.
- Customizable Pipelines: Enables construction of customized analysis pipelines by combining modules from the resource catalog to tailor variant and gene prioritization workflows.
- Scalability and Performance: Implements a scalable framework for high-throughput annotation and prioritization, with reported speeds surpassing variant annotation API services.
- Case Study Applications: Demonstrated in case studies for prioritizing genes and variants across cancer, Mendelian diseases, and complex genetic disorders.
Scientific Applications:
- Cancer genomics: Prioritizes somatic variants and integrates annotations relevant to oncogenic processes and tumor profiling.
- Mendelian disease research: Ranks candidate variants and genes for rare and inherited disorders to support gene discovery and pathogenicity assessment.
- Complex trait and disorder studies: Integrates multiple resources to investigate genotype–phenotype relationships in complex genetic disorders.
- Variant effect prediction and interpretation: Combines computational predictors and annotations to assess potential functional impact of variants.
Methodology:
Integrates an extensible catalog of annotation modules and computational prediction methods within a scalable framework to annotate, score, and rank variants and genes.
Topics
Details
- Cost:
- Free of charge
- Tool Type:
- web application
- Operating Systems:
- Mac, Windows
- Programming Languages:
- Python
- Added:
- 4/21/2021
- Last Updated:
- 11/24/2024
Operations
Publications
Pagel KA, Kim R, Moad K, Busby B, Zheng L, Tokheim C, Ryan M, Karchin R. Integrated Informatics Analysis of Cancer-Related Variants. JCO Clinical Cancer Informatics. 2020. doi:10.1200/cci.19.00132. PMID:32228266. PMCID:PMC7113103.