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Related Experiment Video

Updated: Dec 4, 2025

Studying Ribonucleotide Incorporation: Strand-specific Detection of Ribonucleotides in the Yeast Genome and Measuring Ribonucleotide-induced Mutagenesis
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NIMBus: a negative binomial regression based Integrative Method for mutation Burden Analysis.

Jing Zhang1, Jason Liu2,3, Patrick McGillivray2

  • 1Department of Computer Science, University of California, Irvine, CA, 92617, USA.

BMC Bioinformatics
|October 23, 2020
PubMed
Summary
This summary is machine-generated.

We developed NIMBus, a new method to identify cancer mutation hotspots by analyzing whole-genome sequences and functional genomics data. This tool helps discover DNA elements influencing cancer progression.

Keywords:
Mutation count overdispersionMutation rate estimationMutation rate heterogeneitySomatic mutation burden

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Area of Science:

  • Genomics
  • Cancer Biology
  • Bioinformatics

Background:

  • Identifying frequently mutated regions is crucial for understanding cancer progression.
  • Mutation rate heterogeneity across genomes and individuals poses challenges.
  • Genomic factors like replication timing and chromatin organization contribute to heterogeneity.

Purpose of the Study:

  • To develop a robust method for identifying mutation hotspots.
  • To account for mutation rate heterogeneity using genomic features.
  • To analyze whole-genome cancer sequences and functional genomics data.

Main Methods:

  • Developed a negative binomial regression-based Integrative Method for mutation Burden analysiS (NIMBus).
  • Utilized a Gamma-Poisson mixture model to capture mutation rate heterogeneity.
  • Integrated genomic features from the Encyclopedia of DNA Elements (ENCODE) to estimate background mutation rates.

Main Results:

  • NIMBus successfully identified known coding and noncoding cancer drivers (e.g., TP53, TERT promoter).
  • Identified novel mutational hotspots in non-coding regions, particularly transcription factor binding sites intersecting DNase I hypersensitive sites.
  • These hotspots may disrupt gene regulatory networks in cancer.

Conclusions:

  • NIMBus is an effective tool for identifying mutational hotspots in cancer genomes.
  • The method aids in discovering DNA elements that influence cancer progression.
  • NIMBus software and results are publicly available online.