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

RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
Sanger Sequencing01:57

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...
Next-generation Sequencing03:00

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.

You might also read

Related Articles

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

Sort by
Same author

Author Correction: Transformer patient embedding using electronic health records enables patient stratification and progression analysis.

NPJ digital medicine·2026
Same author

Gene- and domain-aware calibration increases the clinical utility of variant effect predictors.

Research square·2026
Same author

Clinical Outcomes of Lentiviral Vector Gene Therapy for Sickle Cell Disease.

Blood advances·2026
Same author

Development of a Novel Method to Detect AAV Vector Integration.

Viruses·2026
Same author

Understanding Molecular Basis of PTPN11-Related Diseases.

ArXiv·2026
Same author

Promoting primary palliative care in Western Kenya using Project ECHO®.

African journal of primary health care & family medicine·2025
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
Same journal

SpaMFG: a Spatial Multi-omics Integration Method based on Feature Grouping.

Bioinformatics (Oxford, England)·2026
Same journal

CSCN: Inference of Cell-Specific Causal Networks Using Single-Cell RNA-Seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

Sparse CCA-Based Mediation Analysis with High-Dimensional Exposures and Mediators.

Bioinformatics (Oxford, England)·2026
Same journal

Enhancing Cross-Context Generalization in Drug Perturbation Prediction with a Multimodal Conditional Diffusion Framework.

Bioinformatics (Oxford, England)·2026
Same journal

Primer Design through Submodular Function Estimation.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Jun 5, 2026

Amplification, Next-generation Sequencing, and Genomic DNA Mapping of Retroviral Integration Sites
09:31

Amplification, Next-generation Sequencing, and Genomic DNA Mapping of Retroviral Integration Sites

Published on: March 22, 2016

Identifying viral integration sites using SeqMap 2.0.

Troy B Hawkins1, Jessica Dantzer, Brandon Peters

  • 1Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA. troyhawk@iupui.edu

Bioinformatics (Oxford, England)
|January 20, 2011
PubMed
Summary
This summary is machine-generated.

We developed a fast method to identify viral vector integration sites using long-read sequencing. This tool aids in cancer gene discovery and gene therapy safety by pinpointing integration locations.

More Related Videos

Bidirectional Retroviral Integration Site PCR Methodology and Quantitative Data Analysis Workflow
12:53

Bidirectional Retroviral Integration Site PCR Methodology and Quantitative Data Analysis Workflow

Published on: June 14, 2017

Identification of Functionally-Relevant Lentivirus Integration Sites in an Insertional Mutagenesis Cell Library
07:28

Identification of Functionally-Relevant Lentivirus Integration Sites in an Insertional Mutagenesis Cell Library

Published on: January 10, 2025

Related Experiment Videos

Last Updated: Jun 5, 2026

Amplification, Next-generation Sequencing, and Genomic DNA Mapping of Retroviral Integration Sites
09:31

Amplification, Next-generation Sequencing, and Genomic DNA Mapping of Retroviral Integration Sites

Published on: March 22, 2016

Bidirectional Retroviral Integration Site PCR Methodology and Quantitative Data Analysis Workflow
12:53

Bidirectional Retroviral Integration Site PCR Methodology and Quantitative Data Analysis Workflow

Published on: June 14, 2017

Identification of Functionally-Relevant Lentivirus Integration Sites in an Insertional Mutagenesis Cell Library
07:28

Identification of Functionally-Relevant Lentivirus Integration Sites in an Insertional Mutagenesis Cell Library

Published on: January 10, 2025

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Retroviral integration is crucial for gene therapy and cancer research.
  • Identifying integration sites is vital for assessing gene therapy safety and discovering cancer-associated genes.

Purpose of the Study:

  • To introduce an efficient and scalable method for rapid identification of viral vector integration sites.
  • To provide a web server platform for collaborative analysis of integration sites.

Main Methods:

  • Masking individual sequence reads to remove non-genomic content.
  • Aligning processed reads to the host genome.
  • Assembling aligned fragments to pinpoint viral vector integration sites.

Main Results:

  • The method enables fast and accurate identification of viral vector integration sites from long-read sequencing data.
  • SeqMap 2.0 provides a collaborative platform for researchers to analyze integration data.

Conclusions:

  • This new method enhances the analysis of retroviral integration.
  • The SeqMap 2.0 platform facilitates research in gene therapy and cancer genomics.