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.

Documentation

Links