Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Cancers Originate from Somatic Mutations in a Single Cell02:21

Cancers Originate from Somatic Mutations in a Single Cell

12.7K
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...
12.7K
Cancers Originate from Somatic Mutations in a Single Cell02:21

Cancers Originate from Somatic Mutations in a Single Cell

3.2K
3.2K
Abnormal Proliferation02:23

Abnormal Proliferation

4.0K
Under normal conditions, most adult cells remain in a non-proliferative state unless stimulated by internal or external factors to replace lost cells. Abnormal cell proliferation is a condition in which the cell's growth exceeds and is uncoordinated with normal cells. In such situations, cell division persists in the same excessive manner even after cessation of the stimuli, leading to persistent tumors. The tumor arises from the damaged cells that replicate to pass the damage to the...
4.0K
Loss of Tumor Suppressor Gene Functions01:12

Loss of Tumor Suppressor Gene Functions

5.0K
Tumor suppressor genes are normal genes that can slow down cell division, repair DNA mistakes, or program the cells for apoptosis in case of irreparable damage. Hence, they play an essential role in preventing the proliferation of damaged cells.
When the tumor suppressor genes develop mutations or are lost, cells start growing out of control, leading to cancer. However, a single functional copy of the tumor suppressor gene is enough for the cells to maintain their normal functions and cell...
5.0K
Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

5.8K
Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
5.8K
Mutations01:39

Mutations

66.9K
Overview
66.9K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

HSP47 is a potential dual cell target and prognostic factor in pancreatic cancer.

Oncogene·2026
Same author

Clinical and molecular landscape of surgically resected early onset pancreatic cancer.

The British journal of surgery·2026
Same author

Clonal evolutionary analysis reveals patterns of malignant transformation of Intraductal Papillary Mucinous Neoplasms of the pancreas.

Nature communications·2026
Same author

Divergent trajectories to structural diversity impact patient survival in high grade serous ovarian cancer.

Nature communications·2025
Same author

Sequential ATR and PARP inhibition overcomes acquired DNA damaging agent resistance in pancreatic ductal adenocarcinoma.

British journal of cancer·2025
Same author

Mucinous cystic neoplasms and simple mucinous cysts are two distinct precursors of pancreatic cancer: clinicopathological, genomic, and transcriptomic characterization.

The Journal of pathology·2025
Same journal

Invaders taking over-Mollusc faunal change in volcanic barrier lakes of the Albertine Rift biodiversity hotspot.

PloS one·2026
Same journal

AI-driven molecular diversification and ligand-based optimization of macitentan derivatives targeting VEGFR1 and endothelin signaling pathways.

PloS one·2026
Same journal

Performance patterns and records in the world aquatics masters championships: Where do the most frequently represented nations among the top-ten masters swimmers come from?

PloS one·2026
Same journal

Modeling diurnal Temperature-Rainfall relationships under multicollinearity using PLS-SEM: A case study of Ghana.

PloS one·2026
Same journal

Organizational culture, social capital, and emergency capacity in primary healthcare institutions: A cross-sectional structural equation modeling study comparing ordinary and older communities.

PloS one·2026
Same journal

Impact of kidney function on the metabolome in the general population.

PloS one·2026
See all related articles

Related Experiment Video

Updated: May 5, 2026

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors
11:15

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors

Published on: September 20, 2016

26.1K

Somatic point mutation calling in low cellularity tumors.

Karin S Kassahn1, Oliver Holmes, Katia Nones

  • 1Queensland Centre for Medical Genomics, Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.

Plos One
|November 20, 2013
PubMed
Summary
This summary is machine-generated.

Accurately identifying somatic mutations from next-generation sequencing data is difficult. Our new heuristic strategy and software improve sensitivity and precision for somatic mutation calling, especially in low tumor content samples.

More Related Videos

Visualizing Genetic Variants, Short Targets, and Point Mutations in the Morphological Tissue Context with an RNA In Situ Hybridization Assay
10:57

Visualizing Genetic Variants, Short Targets, and Point Mutations in the Morphological Tissue Context with an RNA In Situ Hybridization Assay

Published on: August 14, 2018

13.8K
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

19.0K

Related Experiment Videos

Last Updated: May 5, 2026

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors
11:15

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors

Published on: September 20, 2016

26.1K
Visualizing Genetic Variants, Short Targets, and Point Mutations in the Morphological Tissue Context with an RNA In Situ Hybridization Assay
10:57

Visualizing Genetic Variants, Short Targets, and Point Mutations in the Morphological Tissue Context with an RNA In Situ Hybridization Assay

Published on: August 14, 2018

13.8K
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

19.0K

Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Somatic mutation calling from next-generation sequencing (NGS) data is challenging due to artifacts like PCR errors, sequencing mistakes, and misalignments.
  • Identifying true somatic events is further complicated by low tumor cellularity, sub-clonality, and copy number variations, which can obscure germline variants.

Purpose of the Study:

  • To develop and validate a heuristic strategy and associated software for improved somatic mutation calling.
  • To enhance the sensitivity and precision of mutation detection, particularly in samples with low tumor purity.

Main Methods:

  • Development of a novel heuristic strategy for somatic mutation identification.
  • Implementation of the strategy into user-friendly software, accessible at http://www.qcmg.org/bioinformatics/qsnp/.
  • Validation using a well-characterized cell line, controlled tumor/normal admixtures, and orthogonal verification of 3,253 single nucleotide variants (SNVs).

Main Results:

  • The developed heuristic strategy and software demonstrate superior sensitivity and precision compared to existing methods.
  • The approach effectively identifies true somatic events even in samples with low tumor cellularity.
  • Validation confirmed the reliability of the method across diverse sample types and a significant number of SNVs.

Conclusions:

  • The new heuristic strategy and software offer a robust solution for somatic mutation calling in challenging genomic samples.
  • This advancement is particularly beneficial for analyzing low-purity tumor samples, improving the accuracy of cancer variant detection.
  • The validated approach enhances the reliability of identifying somatic single nucleotide variants (SNVs) in genomic research.