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Related Experiment Video

Updated: Nov 29, 2025

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
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Fully automated web-based tool for identifying regulatory hotspots.

Ju Hun Choi1, Taegun Kim1, Junghyun Jung2

  • 1Department of Computer Science and Engineering, Dongguk University-Seoul, Seoul, 04620, South Korea.

BMC Genomics
|November 19, 2020
PubMed
Summary
This summary is machine-generated.

NICER (NICE Renew) is a user-friendly web tool that simplifies regulatory hotspot analysis using expression quantitative trait locus (eQTL) data. It significantly reduces computational complexity and running time, making it a more accessible method for identifying genuine genetic hotspots.

Keywords:
Google cloud compute engine (GCE)PLINKParallel processingRegulatory hotspotVCF (variant call format)Web-based tooleQTL

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Area of Science:

  • Genomics and Bioinformatics
  • Computational Biology
  • Statistical Genetics

Background:

  • Regulatory hotspots are genetic variations influencing gene expression, crucial for understanding gene regulation.
  • Expression quantitative trait locus (eQTL) analysis is key to identifying these hotspots.
  • Existing methods often yield spurious results due to confounding factors and suffer from complexity and high computational cost.

Purpose of the Study:

  • To develop a user-friendly, automated web-based tool for regulatory hotspot analysis.
  • To overcome the limitations of existing methods, including complexity and computational burden.
  • To improve the accessibility and efficiency of eQTL analysis for identifying genuine regulatory hotspots.

Main Methods:

  • Development of NICER (NICE Renew), a web-based tool based on the NICE program.
  • Incorporation of multi-processing to significantly reduce running time.
  • Provision of user-friendly interfaces, multiple input formats, and visualization tools.
  • Deployment options including web-based access and local installation on Google Compute Engine.

Main Results:

  • NICER successfully reduces the installation and running burden of the NICE program.
  • Multi-processing integration leads to significant reductions in analysis time.
  • A yeast dataset analysis demonstrated NICER's ability to identify numerous genuine regulatory hotspots, with over half previously reported.
  • The tool offers enhanced usability through diverse input formats and result visualization.

Conclusions:

  • NICER addresses the practical limitations hindering the widespread adoption of hotspot analysis tools.
  • It provides a fully automated, web-based solution for eQTL mapping and regulatory hotspot identification.
  • NICER's user-friendly interface and reduced running time enhance the viability of hotspot analysis, positioning it as a preferred method for increasing eQTL analysis power.