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Genomic variant annotation workflow for clinical applications.

Thomas Thurnherr1, Franziska Singer2, Daniel J Stekhoven2

  • 1Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland; SIB Swiss Institute of Bioinformatics, Basel, Switzerland.

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Summary
This summary is machine-generated.

This study introduces rDGIdb, an R package for analyzing DNA aberrations. It helps identify potential gene targets and drugs from next-generation sequencing data, aiding medical applications.

Keywords:
Bioconductor packageDrug-gene interactionannotationclinical applicationgenomicsnext-generation sequencingpipeline.somatic variant

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Interpreting DNA aberrations from next-generation sequencing (NGS) is crucial for medical applications.
  • Genomic aberrations correlate with clinical and phenotypic features, necessitating robust analysis tools.

Purpose of the Study:

  • To develop a workflow for identifying potential gene targets and associated drugs from aberrated genes or pathways.
  • To provide an R package, rDGIdb, as an interface to the Drug-Gene Interaction Database (DGIdb).

Main Methods:

  • Developed the R/Bioconductor package rDGIdb.
  • Utilized DGIdb, a comprehensive drug-gene interaction database aggregating data from 15 resources.
  • Integrated rDGIdb into next-generation sequencing (NGS) data analysis pipelines for automated querying.

Main Results:

  • rDGIdb facilitates querying and filtering of drug-gene interaction data within the R environment.
  • The package enables the identification of potential therapeutic targets and drugs linked to genomic aberrations.
  • Automation of queries through rDGIdb integration into NGS pipelines streamlines data analysis.

Conclusions:

  • The rDGIdb package enhances the annotation and interpretation of DNA aberrations in medical genomics.
  • It provides a valuable tool for R users to explore drug-gene interactions relevant to genomic alterations.
  • Facilitates the discovery of targeted therapies by linking genomic findings to actionable drug information.