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Related Concept Videos

Mismatch Repair01:20

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...
Spontaneous and Induced Mutations01:30

Spontaneous and Induced Mutations

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).
Cancers Originate from Somatic Mutations in a Single Cell02:21

Cancers Originate from Somatic Mutations in a Single Cell

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...
Mutagenicity and Carcinogenicity01:25

Mutagenicity and Carcinogenicity

Mutagenicity and carcinogenicity refer to the ability of drugs to cause genetic defects and induce cancer, respectively. The International Agency for Research on Cancer (IARC) classifies agents into four groups based on their carcinogenic potential. Group 1 agents are known human carcinogens; group 2A agents are probably carcinogenic to humans; group 3 agents lack data to support their role in carcinogenesis; and group 4 includes agents for which data support that they are not likely to be...
Cancer-Critical Genes II: Tumor Suppressor Genes01:05

Cancer-Critical Genes II: Tumor Suppressor Genes

Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
When the function of certain critical genes, especially those involved in cell cycle regulation and cell growth signaling cascades, gets disrupted, it upsets the cell cycle progression. Such cells with unchecked cell cycles start proliferating uncontrollably and eventually develop into tumors.
Such genes that act...
In vitro Mutagenesis01:16

In vitro Mutagenesis

To learn more about the function of a gene, researchers can observe what happens when the gene is inactivated or “knocked out,” by creating genetically engineered knockout animals. Knockout mice have been particularly useful as models for human diseases such as cancer, Parkinson’s disease, and diabetes.

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Related Experiment Video

Updated: May 18, 2026

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
08:46

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Published on: December 9, 2015

Confidence-based somatic mutation evaluation and prioritization.

Martin Löwer1, Bernhard Y Renard, Jos de Graaf

  • 1TRON - Translational Oncology at Johannes Gutenberg University of Mainz Medicine, Mainz, Germany.

Plos Computational Biology
|October 3, 2012
PubMed
Summary
This summary is machine-generated.

Next generation sequencing (NGS) identifies somatic mutations but often yields errors. A new algorithm assigns a false discovery rate (FDR) to accurately distinguish true mutations from false positives.

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Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
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Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

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Last Updated: May 18, 2026

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
08:46

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Published on: December 9, 2015

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
11:02

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Next-generation sequencing (NGS) enables high-throughput somatic mutation discovery.
  • Current NGS somatic mutation detection methods suffer from high error rates, with validation rates around 54% and low algorithm congruence (<50%).

Purpose of the Study:

  • To develop a novel algorithm for assigning a False Discovery Rate (FDR) statistic to somatic mutations identified by NGS.
  • To improve the accuracy and reliability of somatic mutation detection.

Main Methods:

  • Developed an algorithm to calculate an FDR for each somatic mutation detected by NGS.
  • Applied existing algorithms (GATK, SAMtools, SomaticSNiPer) to analyze triplicate exome sequencing data from mice and melanoma cells.
  • Validated selected mutations based on their assigned FDR confidence values.

Main Results:

  • The FDR statistic accurately discriminated true somatic mutations from erroneous calls.
  • All high-confidence somatic mutations (low FDR) validated (50/50).
  • None of the low-confidence somatic mutations (high FDR) validated (0/44).
  • 15 of 45 intermediate FDR mutations validated.
  • Single-end 50 nt reads from replicates using HiSeq 2000 generated the highest confidence somatic mutation call set.

Conclusions:

  • The developed FDR algorithm significantly enhances the accuracy of NGS-based somatic mutation detection.
  • FDR values enable statistical comparisons of different sequencing and computational methodologies.
  • High-confidence somatic mutations identified by this method are highly reliable for further study.