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Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...
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Examining clustered somatic mutations with SigProfilerClusters.

Erik N Bergstrom1,2,3, Mousumy Kundu1,2,3, Noura Tbeileh1,2,3

  • 1Department of Cellular and Molecular Medicine, UC San Diego, La Jolla, CA 92093, USA.

Bioinformatics (Oxford, England)
|May 20, 2022
PubMed
Summary
This summary is machine-generated.

SigProfilerClusters is a new tool that identifies all types of clustered mutations in genomes. It uses a sample-dependent threshold to distinguish clustered events from non-clustered mutations, aiding cancer research.

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Area of Science:

  • Genomics
  • Computational Biology
  • Cancer Research

Background:

  • Clustered mutations occur in human germline and somatic cells, influencing cancer evolution and developmental disorders.
  • Existing tools for clustered mutation detection are limited, often relying on fixed thresholds and specific event subtypes.
  • Understanding mutation patterns is crucial for deciphering genomic instability and disease mechanisms.

Purpose of the Study:

  • To develop an automated and comprehensive tool for detecting all types of clustered mutations.
  • To overcome the limitations of existing methods by employing a dynamic, sample-dependent threshold.
  • To provide a robust platform for analyzing mutation patterns across diverse genomic contexts.

Main Methods:

  • Developed SigProfilerClusters, an automated software tool for clustered mutation detection.
  • Implemented a simulated background model to calculate a sample-dependent inter-mutational distance (IMD) threshold.
  • Incorporated extended sequence context, transcriptional strand asymmetries, and regional mutation densities into the analysis.

Main Results:

  • SigProfilerClusters accurately disentangles various clustered mutation types (doublet-base substitutions, multi-base substitutions, omikli, kataegis) from non-clustered events.
  • The tool provides annotations for each clustered event and outputs data in standard formats.
  • Multiple visualizations are generated for exploring genomic distributions of clustered mutations.

Conclusions:

  • SigProfilerClusters offers a versatile solution for identifying and analyzing all classes of clustered mutations.
  • The tool's ability to account for genomic context and sample-specific mutation rates enhances accuracy.
  • SigProfilerClusters facilitates deeper insights into the mutational processes underlying cancer and developmental disorders.