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

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

Mesothelioma location influences the tumour microenvironment and immune checkpoint therapy response in preclinical models.

Scientific reports·2026
Same author

AI succeeds in diagnosing rare diseases.

Nature·2026
Same author

A precision medicine approach to interpret a GATA4 genetic variant in a paediatric patient with congenital heart disease.

Human genomics·2026
Same author

The United Nations convention on rare diseases-A framework for research prioritization.

Frontiers in public health·2025
Same author

Functional characterization of the MED12 p.Arg1138Trp variant in females: implications for neural development and disease mechanism.

Molecular medicine (Cambridge, Mass.)·2025
Same author

Distinct Phenotypes of Peripheral Innate Lymphoid Cells and T Cells in Type 2 and Non-Type 2 Asthma.

Clinical and translational allergy·2025
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
Same journal

KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to Virtual Screening.

Bioinformatics (Oxford, England)·2026
Same journal

IDR searcher: a search engine solution for public image resources.

Bioinformatics (Oxford, England)·2026
Same journal

KCFtools: Rapid alignment-free method for introgression screening and GWAS using k-mer profiles.

Bioinformatics (Oxford, England)·2026
Same journal

Meta2DB: Curated shotgun metagenomic feature sets and metadata for health state prediction.

Bioinformatics (Oxford, England)·2026
Same journal

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

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

Related Experiment Video

Updated: Jun 20, 2026

Targeted DNA Methylation Analysis by Next-generation Sequencing
08:38

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

TagDust--a program to eliminate artifacts from next generation sequencing data.

Timo Lassmann1, Yoshihide Hayashizaki, Carsten O Daub

  • 1Omics Science Center, Riken Yokohama Institute, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan. timolassmann@gmail.com

Bioinformatics (Oxford, England)
|September 10, 2009
PubMed
Summary
This summary is machine-generated.

TagDust is a new program that identifies artifactual sequences in next-generation sequencing data. This tool helps improve the accuracy of RNA and DNA sequencing analysis by filtering out unwanted reads.

More Related Videos

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome
06:40

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

Published on: March 22, 2018

Related Experiment Videos

Last Updated: Jun 20, 2026

Targeted DNA Methylation Analysis by Next-generation Sequencing
08:38

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome
06:40

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

Published on: March 22, 2018

Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • Next-generation sequencing generates vast amounts of short reads.
  • Experimental procedures can introduce artifactual sequences alongside target RNA or DNA.
  • Identifying these artifacts is crucial for assay development and data analysis.

Purpose of the Study:

  • To present TagDust, a computational tool for identifying artifactual sequences in large-scale sequencing runs.
  • To provide a method for filtering unwanted reads in next-generation sequencing data.

Main Methods:

  • TagDust utilizes user-defined false discovery rate cutoffs.
  • The program identifies reads explainable by known library preparation sequences through combinations and partial matches.

Main Results:

  • TagDust effectively identifies artifactual sequences in high-throughput sequencing data.
  • The method's quality was demonstrated on sequencing runs from Illumina's Genome Analyzer platform.

Conclusions:

  • TagDust is a valuable tool for improving the quality of next-generation sequencing data analysis.
  • Accurate identification of artifacts enhances the reliability of RNA and DNA sequencing results.