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Mutations in Microorganisms01:18

Mutations in Microorganisms

182
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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Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Point and Frameshift Mutations01:30

Point and Frameshift Mutations

262
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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Mutations01:39

Mutations

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Overview
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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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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: Oct 15, 2025

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
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Generator based approach to analyze mutations in genomic datasets.

Siddharth Jain1, Xiongye Xiao2, Paul Bogdan3

  • 1California Institute of Technology, Electrical Engineering, Pasadena, 91125, USA.

Scientific Reports
|October 27, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel computational method for comparing genomic sequences by analyzing their underlying generators, enabling efficient clustering and evolutionary analysis of large datasets without alignment. The approach aids in identifying mutation hotspots.

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Conventional genomic sequence comparison relies on sequence alignment tools, which can be computationally intensive and less effective for large datasets.
  • Existing methods may struggle with capturing the statistical properties and evolutionary dynamics of genomic sequences efficiently.

Purpose of the Study:

  • To propose a novel computational approach for comparing genomic sequences based on learned sequence generators.
  • To enable efficient statistical distance computation, clustering, and evolutionary characterization of large genomic datasets.
  • To identify mutation activity regions within genomic sequences without direct alignment.

Main Methods:

  • Developed a data-driven approach to learn state machine-based sequence generators for genomic sequences.
  • Utilized these generators to compute statistical distances between sequences, independent of sequence length.
  • Applied the method to cluster genomic datasets, analyze evolutionary trajectories, and detect mutation hotspots.

Main Results:

  • Demonstrated an efficient method for computing statistical distances between large sets of genomic sequences.
  • Successfully clustered diverse strains of SARS-CoV-2 viral sequences, revealing their evolutionary patterns.
  • Identified specific regions within SARS-CoV-2 sequences exhibiting mutation activity, aiding potential alignment techniques.

Conclusions:

  • The proposed sequence generator comparison method offers a fast and efficient alternative to traditional alignment for genomic data analysis.
  • This technique provides valuable insights into genomic sequence evolution, clustering, and mutation detection.
  • The approach has practical applications in analyzing viral evolution, such as with SARS-CoV-2 strains.