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DrGA: cancer driver gene analysis in a simpler manner

Quang-Huy Nguyen1, Tin Nguyen2, Duc-Hau Le3

  • 1School of Computer Science and Engineering, Thuyloi University, Hanoi, Vietnam.

BMC Bioinformatics
|March 6, 2022
PubMed
Abstract

Insights

A new R package, DrGA, simplifies driver gene analysis for cancer research. It automates key tasks, providing consistent and reproducible results for researchers with limited IT backgrounds.

Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Cancer remains a leading cause of death globally.
  • Identifying driver genes is crucial for understanding cancer development.
  • Previous analysis pipelines were complex and time-consuming for non-technical users.

Purpose of the Study:

  • To develop a user-friendly R package for driver gene analysis.
  • To automate downstream analyses of predicted driver genes.
  • To improve the efficiency and accessibility of driver gene identification.

Main Methods:

  • Development of the DrGA R package based on a previously established pipeline.
  • Automation of four widely used downstream analyses for driver genes.
  • Application of DrGA to analyze human breast cancer driver genes and genomic biomarkers in another organism.

Main Results:

  • DrGA successfully automates driver gene analysis.
  • The package provides consistent and reproducible results.
  • Demonstrated utility in analyzing human breast cancer and other complex diseases.

Conclusions:

  • DrGA empowers users with limited IT backgrounds to perform complex analyses.
  • The package accelerates the creation of reliable research outcomes.
  • DrGA is freely available with example data for broader scientific use.
Keywords:
Clinical featureDriver geneGenetic biomakerHuman breast cancerMouse metabolic syndromeOmics data

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