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Mutations in Microorganisms

Mutations in Microorganisms

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,...

Mutations

Mutations

Mutations

Mutations

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...

Mutations

Mutations

No description available

Mismatch Repair

Mismatch Repair

Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
The Mutator Protein Family Plays a Key Role in DNA Mismatch Repair
The human genome has more than 3 billion base pairs of DNA per cell. Prior to cell division, that vast amount of genetic...

Mismatch Repair

Mismatch Repair

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Related Experiment Video

Updated: Apr 1, 2026

Measuring Microbial Mutation Rates with the Fluctuation Assay

Measuring Microbial Mutation Rates with the Fluctuation Assay

Published on: November 28, 2019

GARCOM: A user-friendly R package for genetic mutation counts.

Sanjeev Sariya^1,2, Giuseppe Tosto^1,2,3

¹The Gertrude H. Sergievsky Center College of Physicians and Surgeons, Columbia University Medical Center, New York, NY, 10032, USA.

|March 14, 2025

Summary

This summary is machine-generated.

Next-generation sequencing (NGS) analysis of rare variants is enhanced by GARCOM. This R package simplifies obtaining allelic counts within genes and regions, aiding genetic studies.

Keywords:

VCF allele genetics mutation plink

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Identifying DNA Mutations in Purified Hematopoietic Stem/Progenitor Cells

Identifying DNA Mutations in Purified Hematopoietic Stem/Progenitor Cells

Published on: February 24, 2014

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Next-generation sequencing (NGS) facilitates the study of rare and uncommon genetic variants in large populations.
Variant collapsing strategies are commonly used to analyze low-frequency variants or those with small effect sizes by grouping them within genes or genomic regions.
Existing bioinformatics tools often lack straightforward functionalities for basic tasks like calculating allelic counts within specific gene or region boundaries, necessitating complex custom coding.

Purpose of the Study:

To introduce GARCOM (Gene And Region Count Of Mutations), a novel open-source R package.
To provide a user-friendly tool for calculating allelic counts within defined genomic regions and genes from NGS data.
To simplify a fundamental step in variant analysis, particularly for strategies involving variant collapsing.

Main Methods:

Development of GARCOM as an R package.
Implementation of functionalities to process genetic data from PLINK or VCF file formats.
Inclusion of options for data subsetting to enable refined analyses within specific genes or regions.

Main Results:

GARCOM successfully generates a matrix of allelic counts per sample for specified genes or genomic regions.
The package provides a simplified alternative to complex coding for obtaining essential variant count data.
Input data flexibility is supported through PLINK and VCF formats, with options for targeted analysis.

Conclusions:

GARCOM addresses a critical gap in bioinformatics tools by offering an accessible method for calculating gene and region-specific allelic counts.
This tool is expected to streamline variant analysis workflows, particularly for researchers utilizing variant collapsing strategies in large-scale genetic studies.
The open-source nature and ease of use of GARCOM promote its adoption in genetic research and bioinformatics.