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Identification of Mutated Cancer Driver Genes in Unpaired RNA-Seq Samples

David Mosen-Ansorena1,2

  • 1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. davidm@jimmy.harvard.edu.

Methods in Molecular Biology (Clifton, N.J.)
|November 1, 2018
PubMed

Insights

Identifying cancer driver genes is crucial. This workflow uses RNA-sequencing data to find functional mutations and prioritize genes involved in cancer development.

Area of Science:

  • Genomics
  • Cancer Biology
  • Bioinformatics

Background:

  • High-throughput sequencing generates vast amounts of data for cancer genomics.
  • Identifying cancer driver genes is essential for understanding oncogenesis.
  • Leveraging existing RNA-sequencing (RNA-seq) data offers an efficient approach.

Purpose of the Study:

  • To present a robust workflow for identifying cancer driver genes.
  • To utilize unpaired tumor RNA-seq samples for transcriptome analysis.
  • To enable the prioritization of genes implicated in carcinogenic processes.

Main Methods:

  • The workflow employs established variant detection methods.
  • It incorporates rigorous data cleaning and comprehensive annotation steps.
  • Analysis focuses on somatic mutations with potential functional impact.

Main Results:

  • The workflow effectively leverages unpaired tumor RNA-seq data.
  • It facilitates the selection of functionally significant somatic mutations.
  • Prioritization of cancer-relevant genes is achieved through detailed annotation.

Conclusions:

  • This RNA-seq based workflow provides a valuable tool for cancer driver gene identification.
  • The approach enhances the utility of existing RNA-seq data for cancer genomics research.
  • It aids in pinpointing genes critical to tumor development and progression.
Keywords:
CancerDriver genesHigh-throughput sequencingMutationsRNA-seq

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