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Related Concept Videos

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Related Experiment Video

Updated: Oct 12, 2025

Identification of Transcription Factor Regulators using Medium-Throughput Screening of Arrayed Libraries and a Dual-Luciferase-Based Reporter
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Identifying tumorigenic non-coding mutations through altered cis-regulation.

Zhongshan Cheng1, Michael Vermeulen1, Micheal Rollins-Green1

  • 1Department of Biology, Queen's University, Kingston, ON K7L 3N6, Canada.

STAR Protocols
|November 24, 2021
PubMed
Summary
This summary is machine-generated.

Identifying non-coding mutations that drive cancer requires new methods. This study presents a bioinformatics pipeline to find associations between mutated regulatory elements and altered gene regulation in tumors, aiding the discovery of non-coding cancer drivers.

Keywords:
BioinformaticsCancerGenomicsRNAseqSequence analysis

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Identifying non-coding mutations driving tumorigenesis is challenging.
  • Non-coding mutations can alter gene regulation and contribute to cancer.
  • Associations between regulatory elements and cis-regulation offer a promising avenue for discovery.

Purpose of the Study:

  • To develop a bioinformatics pipeline for identifying non-coding driver mutations.
  • To mine Cancer Genomic Commons (GDC) data for associations between mutated regulatory elements and altered cis-regulation.
  • To reveal putative non-coding driver mutations specific to cancer types.

Main Methods:

  • Integration of RNA sequencing, whole-genome sequencing, and genotyping data.
  • Development of a bioinformatics pipeline to analyze large-scale cancer genomic data.
  • Mining the Cancer Genomic Commons (GDC) database.

Main Results:

  • The pipeline successfully identifies associations between mutated regulatory elements and altered cis-regulation.
  • Putative non-coding driver mutations were revealed across different cancer types.
  • The approach provides a method to stratify candidate non-coding mutations.

Conclusions:

  • The developed bioinformatics pipeline is effective for identifying non-coding driver mutations.
  • This approach aids in understanding the role of non-coding mutations in tumorigenesis.
  • The findings facilitate the stratification of candidate non-coding driver mutations for further investigation.