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

Harnessing interfacial click polymerization using pyridinium-yne films as photochromic, radical generation and sensing platforms.

Nature communications·2026
Same author

Expression of PIEZO1 in lung adenocarcinoma correlates with PD-L1 expression, cell migration, and poor prognosis: an exploratory study.

Discover oncology·2026
Same author

Functional Capacity and Gut Microbiota Shifts in Heart Failure Patients Following Cardiac Rehabilitation.

Journal of clinical medicine·2026
Same author

Establishment and Characterization of a Long-Term Ovarian Cell Line (SBO) from Asian Seabass (<i>Lates calcarifer</i>) Expressing Germline Stem Cell Markers.

International journal of molecular sciences·2026
Same author

GLEAM: A Multimodal Imaging Dataset and HAMM for Glaucoma Classification.

IEEE transactions on medical imaging·2026
Same author

Recent progress in small-molecule targeted therapies for melanoma treatment.

Bioorganic & medicinal chemistry·2026
Same journal

Applying Bayesian Multivariable Mendelian Randomisation to Prioritise Candidate Causal Traits From High-Dimensional Data: Illustration From Estimation of the Effect of Maternal Metabolites on Offspring Birthweight.

Genetic epidemiology·2026
Same journal

Individualized Bayesian Inference Identifies Novel Genetic Variants for Parkinson's Disease.

Genetic epidemiology·2026
Same journal

DRIVE v3: Command Line Application for Identity-by-Descent Haplotype Clustering in Large Biobank Scale Data.

Genetic epidemiology·2026
Same journal

Deep Unsupervised Domain Adaptation for Translating Cancer Dependency Maps From Cell Lines to Breast Cancer Tumor Genomics.

Genetic epidemiology·2026
Same journal

Polygenic Risk Scores for Incident Dementia in the Multi-Ethnic Study of Atherosclerosis.

Genetic epidemiology·2026
Same journal

Outcome and Exposure Polygenic Risk Scores Can Help Reduce Information Bias and Selection Bias in Regression Estimates From Biobank Data.

Genetic epidemiology·2026
See all related articles

Related Experiment Video

Updated: May 12, 2026

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

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

A method to detect differentially methylated loci with next-generation sequencing.

Hongyan Xu1, Robert H Podolsky, Duchwan Ryu

  • 1Department of Biostatistics and Epidemiology, Georgia Health Sciences University, Augusta, GA 30912-4900, USA. hxu@gru.edu

Genetic Epidemiology
|April 5, 2013
PubMed
Summary
This summary is machine-generated.

A new statistical test addresses challenges in analyzing DNA methylation data from next-generation sequencing (NGS). This robust and efficient method accurately identifies differential methylation crucial for understanding complex diseases like cancer.

More Related Videos

Methyl-binding DNA capture Sequencing for Patient Tissues
08:40

Methyl-binding DNA capture Sequencing for Patient Tissues

Published on: October 31, 2016

Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients
13:21

Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients

Published on: June 16, 2017

Related Experiment Videos

Last Updated: May 12, 2026

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

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

Methyl-binding DNA capture Sequencing for Patient Tissues
08:40

Methyl-binding DNA capture Sequencing for Patient Tissues

Published on: October 31, 2016

Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients
13:21

Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients

Published on: June 16, 2017

Area of Science:

  • Genomics
  • Epigenetics
  • Bioinformatics

Background:

  • DNA methylation at CpG loci is vital in complex diseases, including cancer.
  • Next-generation sequencing (NGS) enables genome-wide methylation analysis but lacks efficient statistical tests.
  • Existing methods face challenges with count data, unknown distributions, and high dimensionality.

Purpose of the Study:

  • To develop a robust and computationally efficient statistical test for differential DNA methylation analysis using NGS data.
  • To address the unique characteristics of NGS methylation count data and the complexities of biological samples.

Main Methods:

  • Proposed a novel statistical test based on clustered data analysis, modeling methylation counts.
  • Conducted simulations to evaluate robustness across various methylation level distributions.
  • Assessed statistical power and computational efficiency.

Main Results:

  • The proposed test demonstrated robustness under different methylation level distributions.
  • Simulations confirmed good statistical power and computational efficiency.
  • The test was successfully applied to NGS data from chronic lymphocytic leukemia patients.

Conclusions:

  • The developed statistical test is a promising and practical tool for analyzing genome-wide differential methylation from NGS data.
  • This method can aid in understanding the role of epigenetic changes in diseases.
  • The test's efficiency and robustness make it suitable for large-scale genomic studies.