Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Sanger Sequencing

Sanger Sequencing

DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...

Cancers Originate from Somatic Mutations in a Single Cell

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...

Cancers Originate from Somatic Mutations in a Single Cell

Cancers Originate from Somatic Mutations in a Single Cell

You might also read

Related Articles

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

Sort by

Same author

A class of deep intronic <i>IGHMBP2</i> variants activate a shared cryptic splice donor, enabling correction of select variants with a single antisense oligonucleotide.

medRxiv : the preprint server for health sciences·2026

Same author

Large-scale, spatially resolved panoramic CRISPR screening in native tissue environments using Perturb-DBiT.

Nature biotechnology·2026

Same author

Optical genome mapping identifies clinically relevant somatic structural variation in epilepsy-affected brain tissue.

medRxiv : the preprint server for health sciences·2026

Same author

Somatic variants activating the RAS-MAPK pathway confer susceptibility to hippocampal sclerosis in drug-resistant epilepsy.

bioRxiv : the preprint server for biology·2026

Same author

Functional role of small extrachromosomal circular DNA in colorectal cancer.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

Monoallelic and biallelic KDM5A variants identified in patients with autism spectrum disorder.

HGG advances·2026

Same journal

UK Biobank whole-genome sequencing reveals robust contributions of rare variants to complex-trait heritability.

Genome biology·2026

Same journal

A one-week automated genome-wide optical pooled screen using OttoSeq.

Genome biology·2026

Same journal

Integrated lipidomic and transcriptomic profiling of the host response in human malaria.

Genome biology·2026

Same journal

Centromeric satellite expansion drives genome evolution in the snowy owl.

Genome biology·2026

Same journal

Mapping the landscape of allele-specific expression in porcine genomes.

Genome biology·2026

Same journal

Genomic sequence evolution underlying human neocortical interareal diversification.

Genome biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 3, 2026

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

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

Published on: October 18, 2013

An ensemble approach to accurately detect somatic mutations using SomaticSeq.

Li Tai Fang¹, Pegah Tootoonchi Afshar², Aparna Chhibber³

¹Bina Technologies, Roche Sequencing, Redwood City, 94065, CA, USA. li\_tai.fang@bina.roche.com.

|September 19, 2015

Summary

This summary is machine-generated.

SomaticSeq accurately detects somatic mutations using a novel algorithm that combines multiple callers and genomic features. This approach surpasses the accuracy of individual tools for variant calling in cancer research.

More Related Videos

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

Novel Sequence Discovery by Subtractive Genomics

Novel Sequence Discovery by Subtractive Genomics

Published on: January 25, 2019

Related Experiment Videos

Last Updated: Apr 3, 2026

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

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

Published on: October 18, 2013

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

Novel Sequence Discovery by Subtractive Genomics

Novel Sequence Discovery by Subtractive Genomics

Published on: January 25, 2019

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Somatic mutations are crucial in cancer development and progression.
Accurate detection of somatic variants (single nucleotide variants, small insertions/deletions) is essential for cancer genomics.
Existing somatic mutation callers have limitations in accuracy and sensitivity.

Purpose of the Study:

To develop and validate an accurate somatic mutation detection pipeline.
To improve upon the performance of individual somatic mutation callers.
To provide a robust tool for analyzing genomic and sequencing features for mutation prediction.

Main Methods:

Implemented a stochastic boosting algorithm within the SomaticSeq pipeline.
Integrated five state-of-the-art somatic mutation callers.
Extracted over 70 genomic and sequencing features for candidate sites.
Utilized an adaptively boosted decision tree learner for classification.

Main Results:

SomaticSeq achieved high accuracy in somatic mutation detection.
The pipeline demonstrated superior performance compared to individual constituent tools.
Validation with both synthetic and real data confirmed the pipeline's effectiveness.

Conclusions:

SomaticSeq offers a more accurate approach to somatic mutation detection.
The ensemble method leveraging multiple callers and features enhances variant calling.
This pipeline provides a valuable tool for cancer genomics research and clinical applications.