OncoGEMINI

OncoGEMINI enables identification and analysis of biologically and clinically relevant tumor variants from multi-sample and longitudinal tumor sequencing data.


Key Features:

  • GEMINI-compatible database: Uses a GEMINI-compatible database generated from annotated Variant Call Format (VCF) files to leverage existing genomic annotations.
  • Filtering capabilities: Provides filtering of tumor variants based on included genomic annotations and allele frequency signatures.
  • Handling tumor heterogeneity: Facilitates identification and tracing of mutations within diverse subclonal populations across samples.
  • Longitudinal analysis: Stores tumor sampling timepoints in a searchable database to observe changes in variant allele frequencies over time.
  • External annotation integration: Integrates CIViC and DGIdb annotations to associate mutations with cancer interpretation and potential therapeutic targets.

Scientific Applications:

  • Mutation prioritization: Prioritizes mutations likely contributing to tumor evolution using annotations and allele frequency dynamics.
  • Therapeutic target identification: Identifies potential therapeutic targets within heterogeneous tumors by querying CIViC and DGIdb annotations.
  • Longitudinal variant tracing: Traces trajectories of somatic and inherited variations across biopsies and timepoints to monitor clonal dynamics.

Methodology:

Generates a GEMINI-compatible database from annotated Variant Call Format (VCF) files; creates a searchable database including tumor sampling timepoints; applies filters based on genomic annotations and allele frequency signatures; and integrates CIViC and DGIdb annotations.

Topics

Details

License:
MIT
Tool Type:
command-line tool
Programming Languages:
Python, Shell
Added:
1/18/2021
Last Updated:
3/13/2021

Operations

Publications

Nicholas TJ, Cormier MJ, Huang X, Qiao Y, Marth GT, Quinlan AR. OncoGEMINI: Software for Investigating Tumor Variants From Multiple Biopsies With Integrated Cancer Annotations. Unknown Journal. 2020. doi:10.1101/2020.03.10.979591.