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Related Concept Videos

Spontaneous and Induced Mutations01:30

Spontaneous and Induced Mutations

39
Spontaneous mutations arise infrequently during DNA replication due to errors in the process. A key factor behind these errors is tautomeric shifts in nitrogenous bases, where bases transition from keto to enol forms or amino to imino forms. This shift can alter base-pairing rules, leading to mutations. Additionally, reactive oxygen species (ROS) arising from aerobic metabolism can damage DNA, resulting in depurination (loss of a purine base) or depyrimidination (loss of a pyrimidine base).
39

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Characterizing Mutational Load and Clonal Composition of Human Blood
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Visualizing and exploring patterns of large mutational events with SigProfilerMatrixGenerator.

Azhar Khandekar1,2,3, Raviteja Vangara1,2,3, Mark Barnes1,2,3

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

BMC Genomics
|August 22, 2023
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Summary

A new bioinformatics tool, SigProfilerMatrixGenerator, now enables efficient exploration and visualization of large-scale somatic mutational events in cancer genomes. This tool supports copy-number and structural variants, offering new insights into cancer biology.

Keywords:
Copy-number signaturesMutational patternsStructural variant signatures

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Cancers are characterized by somatic mutations, ranging from small base pair changes to large structural and copy-number variants.
  • While tools exist for small mutational events, computationally efficient methods for analyzing large-scale mutational events in cancer genomes were lacking.
  • Recent classification schemas have highlighted the utility of studying large-scale mutational events across human cancers.

Purpose of the Study:

  • To introduce a computationally efficient bioinformatics tool for visualizing and exploring large-scale mutational events in cancer.
  • To provide integrated capabilities for examining copy-number variants and structural variants within established classification schemas.

Main Methods:

  • Development of a new version of SigProfilerMatrixGenerator, a Python-based bioinformatics tool.
  • Integration of support for analyzing copy-number variants and structural variants.
  • Inclusion of compatibility with two established classification schemas and multiple data modalities/algorithms.
  • Provision of an R wrapper package for R environment users.

Main Results:

  • SigProfilerMatrixGenerator now offers integrated functionalities for analyzing large mutational events, including copy-number and structural variants.
  • The tool supports data from various algorithms and data modalities, enhancing its applicability.
  • A Python implementation with an R wrapper is available, catering to diverse user preferences.

Conclusions:

  • The updated SigProfilerMatrixGenerator is the first standardized bioinformatics tool for exploring and visualizing copy number and structural variants using established schemas.
  • This tool facilitates optimized analysis of large-scale mutational events in cancer research.
  • The software is freely accessible with comprehensive documentation.