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

100.0K
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....
100.0K

You might also read

Related Articles

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

Sort by
Same author

A highly diverse set of novel immunoglobulin-like transcript (NILT) genes in zebrafish indicates a wide range of functions with complex relationships to mammalian receptors.

Immunogenetics·2022
Same author

Circulating Exosomal miRNAs Signal Circadian Misalignment to Peripheral Metabolic Tissues.

International journal of molecular sciences·2020
Same author

Healthy infants harbor intestinal bacteria that protect against food allergy.

Nature medicine·2019
Same author

Topoisomerase IIβ-binding protein 1 activates expression of E2F1 and p73 in HPV-positive cells for genome amplification upon epithelial differentiation.

Oncogene·2019
Same author

Post-Transcriptional Regulation of KLF4 by High-Risk Human Papillomaviruses Is Necessary for the Differentiation-Dependent Viral Life Cycle.

PLoS pathogens·2016
Same author

[Polyethylene glycol-accompanied ion-exchange chromatography to purify recombinant hepatitis B virus surface antigen].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology·2006
Same journal

2DKD: a toolkit for content-based local image search.

Source code for biology and medicine·2020
Same journal

Computing and graphing probability values of pearson distributions: a SAS/IML macro.

Source code for biology and medicine·2020
Same journal

iPBAvizu: a PyMOL plugin for an efficient 3D protein structure superimposition approach.

Source code for biology and medicine·2019
Same journal

Social support for collaboration and group awareness in life science research teams.

Source code for biology and medicine·2019
Same journal

MZPAQ: a FASTQ data compression tool.

Source code for biology and medicine·2019
Same journal

IPCAPS: an R package for iterative pruning to capture population structure.

Source code for biology and medicine·2019
See all related articles

Related Experiment Video

Updated: Mar 7, 2026

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens
09:14

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Published on: June 28, 2018

7.6K

DEApp: an interactive web interface for differential expression analysis of next generation sequence data.

Yan Li1, Jorge Andrade1

  • 1Center for Research Informatics, University of Chicago, Knapp Center for Biomedical Discovery, 900 East 57th St., Chicago, 60637 USA.

Source Code for Biology and Medicine
|February 9, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces DEApp, a user-friendly web application simplifying differential expression (DE) analysis for Next Generation Sequencing (NGS) data. It empowers researchers without extensive bioinformatics expertise to effectively analyze genomics data.

Keywords:
Differential Expression (DE) analysisGenomicsNext Generation Sequence (NGS)RShinyWeb interface

More Related Videos

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms
10:41

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Published on: May 9, 2017

9.7K
Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

34.9K

Related Experiment Videos

Last Updated: Mar 7, 2026

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens
09:14

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Published on: June 28, 2018

7.6K
Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms
10:41

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Published on: May 9, 2017

9.7K
Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

34.9K

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Next Generation Sequencing (NGS) is increasingly used in biomedical research.
  • Differential expression (DE) analysis of NGS data presents computational challenges, requiring programming skills.
  • Researchers need tools to interactively evaluate statistical models, parameters, and cross-validation for DE analysis.

Purpose of the Study:

  • To develop an accessible tool for DE analysis of NGS count data.
  • To provide an interactive platform for model selection, parameter tuning, and result visualization.
  • To overcome the bioinformatics bottleneck in NGS data analysis.

Main Methods:

  • Development of DEApp, an interactive web application.
  • Implementation of a user-friendly interface for DE analysis.
  • Integration of features for model selection, parameter tuning, and cross-validation.

Main Results:

  • DEApp offers a dynamic and interactive environment for analyzing count-based NGS data.
  • The application facilitates easy selection of statistical and error models.
  • Users can tune parameters, perform cross-validation, and visualize results effectively.

Conclusions:

  • DEApp democratizes NGS data analysis for labs lacking dedicated bioinformaticians.
  • It enables broader adoption of NGS technologies in biomedical research.
  • The application is freely available online.