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...

You might also read

Related Articles

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

Sort by

Same author

Australian parents' perspectives on extended genomic screening: what information to return and when?

European journal of human genetics : EJHG·2026

Same author

How parents decide whether to have genomic newborn screening: experiences from BabyScreen.

European journal of human genetics : EJHG·2026

Same author

Automated reanalysis of genomic data for rare disease diagnostics at scale.

Nature medicine·2026

Same author

Explainable machine learning identifies features and thresholds predictive of immunotherapy response.

Scientific reports·2026

Same author

Unraveling a Diagnostic Enigma: A TECPR2 Case Solved Through Multi-Omic Genomics.

American journal of medical genetics. Part A·2026

Same author

KCNQ2 neonatal epilepsy: Impact of prompt diagnosis and treatment, and early predictors of outcome severity.

Epilepsia open·2026

Same journal

Diagnostic Yield of Genome Sequencing in an Iranian Exome-Negative Autosomal-Recessive Intellectual Disability Cohort.

Human mutation·2026

Same journal

Exploring the Functional Impact of Individual <i>DDX41</i> Variants With a Fast and Robust Cell-Based Method.

Human mutation·2026

Same journal

Modeling the Effects of Single Nucleotide Polymorphisms (SNPs) on the Structure and Function of the Human <i>RET</i> Gene: An In Silico Study.

Human mutation·2026

Same journal

Driver Mutation Subtypes Differentially Shape Immune Evasion Landscapes in Melanoma: An AI-Driven Inflammatory Pathway Model Implicating CCNE1.

Human mutation·2026

Same journal

AI-Augmented Hematological Signatures for Equitable Detection of Hereditary Hemolytic Anemia Carriers: A Global Systematic Review and Meta-Analysis.

Human mutation·2026

Same journal

Pharmacovigilance Signal Detection and Mutually Exclusive Driver Mutations of the PI3K/AKT Pathway in Breast Cancer Treated With Capivasertib.

Human mutation·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 17, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

AmpliVar: mutation detection in high-throughput sequence from amplicon-based libraries.

Arthur L Hsu¹, Olga Kondrashova, Sebastian Lunke

¹Department of Pathology, The University of Melbourne, Parkville, Victoria, Australia.

|February 10, 2015

Summary

This summary is machine-generated.

This study introduces AmpliVar, a novel method for identifying sequence variations in high-throughput sequencing data. AmpliVar groups reads as amplicons before alignment, improving sensitivity, specificity, and computational efficiency for variant detection.

Keywords:

amplicon sequencing grouped reads mutation detection next generation sequencing

More Related Videos

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

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

Published on: September 20, 2016

Oncogenic Gene Fusion Detection Using Anchored Multiplex Polymerase Chain Reaction Followed by Next Generation Sequencing

Oncogenic Gene Fusion Detection Using Anchored Multiplex Polymerase Chain Reaction Followed by Next Generation Sequencing

Published on: July 5, 2019

Related Experiment Videos

Last Updated: Apr 17, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

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

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

Published on: September 20, 2016

Oncogenic Gene Fusion Detection Using Anchored Multiplex Polymerase Chain Reaction Followed by Next Generation Sequencing

Oncogenic Gene Fusion Detection Using Anchored Multiplex Polymerase Chain Reaction Followed by Next Generation Sequencing

Published on: July 5, 2019

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

High-throughput sequencing (HTS) typically identifies genetic variants by aligning individual reads to a reference sequence.
This conventional approach can be computationally intensive and may lack sensitivity for certain types of data, particularly amplicon-based sequencing.

Purpose of the Study:

To develop and evaluate an orthogonal method for sequence variant identification in HTS data.
To improve the sensitivity, specificity, and computational efficiency of variant analysis for amplicon-based HTS data.

Main Methods:

Developed AmpliVar, a method that groups sequencing reads into amplicons before alignment.
Utilized key-value hashes and flanking sequences to group reads.
Implemented a selectable threshold to remove low-abundance reads.
Aligned grouped amplicons using sensitive alignment tools.

Main Results:

Demonstrated that grouping reads as amplicons prior to alignment is more sensitive and specific than conventional read-by-read alignment.
Showcased improved computational efficiency for amplicon-based HTS data analysis.
Extended the method for alignment-free variant confirmation in hybridization capture data.

Conclusions:

AmpliVar offers a more effective approach for analyzing amplicon-based HTS data.
The method enhances variant detection accuracy and processing speed.
The approach is adaptable for confirming variants in other sequencing enrichment strategies.