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Related Concept Videos

Spontaneous and Induced Mutations01:30

Spontaneous and Induced Mutations

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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).
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Mutations01:35

Mutations

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Mutations are changes in the sequence of DNA. These changes can occur spontaneously or they can be induced by exposure to environmental factors. Mutations can be characterized in a number of different ways: whether and how they alter the amino acid sequence of the protein, whether they occur over a small or large area of DNA, and whether they occur in somatic cells or germline cells.
Chromosomal Alterations Are Large-Scale Mutations
While point mutations are changes in a single nucleotide in...
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Mutations01:39

Mutations

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Overview
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Point and Frameshift Mutations01:30

Point and Frameshift Mutations

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Point mutations are genetic alterations involving the change of a single nucleotide base pair in DNA. Depending on how the alteration affects protein synthesis, they can lead to various consequences.Point mutations fall into the following types:Silent mutations occur when a nucleotide change does not alter the amino acid sequence due to the redundancy of the genetic code. For instance, changing ACC to ACA still encodes threonine, leaving the protein function unaffected. This occurs because...
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Mutations in Microorganisms01:18

Mutations in Microorganisms

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Mutations are heritable changes in an organism’s genome involving alterations in the base sequence of DNA or RNA. These changes can influence cellular processes and phenotypic traits, potentially transforming the unaltered wild type into a mutant form. Such changes, termed forward mutations, are pivotal in shaping the genetic diversity of organisms.RNA viruses exhibit the highest mutation rates due to the absence of robust proofreading mechanisms during genome replication. In contrast,...
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Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

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In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
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Related Experiment Video

Updated: Dec 19, 2025

Characterizing Mutational Load and Clonal Composition of Human Blood
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Characterizing Mutational Load and Clonal Composition of Human Blood

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CoMut: visualizing integrated molecular information with comutation plots.

Jett Crowdis1,2, Meng Xiao He1,2,3, Brendan Reardon1,2

  • 1Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.

Bioinformatics (Oxford, England)
|June 6, 2020
PubMed
Summary
This summary is machine-generated.

CoMut is a new Python package for creating customizable comutation plots from genomic and phenotypic data. This tool enhances visualization of patient cohort genomic characteristics for research.

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Related Experiment Videos

Last Updated: Dec 19, 2025

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Large-scale sequencing studies generate complex genomic and phenotypic data for patient cohorts.
  • Visualizing the co-occurrence of genetic variants with clinical data is crucial for understanding disease.
  • Existing comutation plot tools lack the flexibility for highly customized, publication-quality outputs from diverse datasets.

Purpose of the Study:

  • To develop a versatile software tool for generating customized comutation plots.
  • To address the limitations of current visualization methods for large-scale genomic and phenotypic data.

Main Methods:

  • Developed CoMut, an object-oriented, stand-alone Python package.
  • Designed CoMut to accept arbitrary input data, including categorical, continuous, and relational data.
  • Ensured the package supports various plot types beyond standard comutation plots.

Main Results:

  • CoMut successfully generates highly customizable comutation plots.
  • The package accommodates diverse data types, including categorical and continuous variables, bar graphs, and sample relationships.
  • An open-source implementation with comprehensive documentation and examples is available.

Conclusions:

  • CoMut provides a flexible and powerful solution for visualizing complex genomic and phenotypic data.
  • The tool facilitates the creation of publication-quality comutation plots, aiding in the analysis of patient cohorts.
  • The availability on Google Colab ensures accessibility for researchers without installation requirements.