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

You might also read

Related Articles

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

Sort by
Same author

Recurrent patterns of TOP1-mediated neuronal genomic damage shared by major neurodegenerative disorders.

Cell·2026
Same author

Duplex-Indel: a Snakemake pipeline for somatic Indel calling in Tn5 transposase-based duplex sequencing data.

Bioinformatics (Oxford, England)·2026
Same author

Somatic cancer variants enriched in Alzheimer's disease microglia-like cells drive inflammatory and proliferative states.

Cell·2026
Same author

Somatic mosaicism in ALS and FTD identifies focal mutations associated with widespread degeneration.

Nature genetics·2026
Same author

Inverted Alu repeats in loop-out exon skipping across hominoid evolution.

Nucleic acids research·2026
Same author

Somatic mutation in human cerebellum illustrates neuron type-specific patterns of age-related mutation.

bioRxiv : the preprint server for biology·2026

Related Experiment Video

Updated: Sep 5, 2025

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

19.5K

Identification of Somatic Mutations From Bulk and Single-Cell Sequencing Data.

August Yue Huang1, Eunjung Alice Lee1

  • 1Division of Genetics and Genomics, Manton Center for Orphan Diseases, Boston Children's Hospital, Boston, MA, United States, Department of Pediatrics, Harvard Medical School, Boston, MA, United States.

Frontiers in Aging
|July 13, 2022
PubMed
Summary

Somatic mutations, DNA changes after fertilization, contribute to cancer and other diseases. New bioinformatic tools improve their detection, crucial for understanding disease mechanisms.

Keywords:
bioinformatic toolbulk sequencingsingle-cell sequencingsingle-nucleotide variantsomatic mutation

More Related Videos

Detection of Copy Number Alterations Using Single Cell Sequencing
09:45

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

11.7K
Comparative Lesions Analysis Through a Targeted Sequencing Approach
08:16

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

6.8K

Related Experiment Videos

Last Updated: Sep 5, 2025

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

19.5K
Detection of Copy Number Alterations Using Single Cell Sequencing
09:45

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

11.7K
Comparative Lesions Analysis Through a Targeted Sequencing Approach
08:16

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

6.8K

Area of Science:

  • Genetics
  • Bioinformatics
  • Genomics

Background:

  • Somatic mutations are DNA alterations acquired post-fertilization, accumulating throughout life.
  • These mutations are linked to cancer and various non-cancerous diseases.
  • Mutational signatures offer insights into the processes driving mutation.

Purpose of the Study:

  • To review recent advancements in bioinformatic methods for identifying somatic mutations.
  • To highlight the ongoing challenges in somatic mutation detection and analysis.

Main Methods:

  • Genome-wide profiling using next-generation sequencing.
  • Development of sensitive bioinformatic tools to differentiate somatic mutations from technical errors.
  • Single-cell genome sequencing following whole-genome amplification (WGA).

Main Results:

  • Next-generation sequencing enables high-throughput, genome-wide DNA variant analysis.
  • Bioinformatic tools are essential for detecting low-frequency somatic mutations in bulk tissues.
  • WGA for single-cell analysis presents technical challenges due to amplification artifacts.

Conclusions:

  • Accurate identification of somatic mutations is critical for understanding disease etiology.
  • Continued development of robust bioinformatic approaches is necessary.
  • Addressing technical artifacts in single-cell genomics remains a key challenge.