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

12.1K
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...
12.1K
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

815
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
815
Overview of Microsoft Excel as a Data Analysis Tool01:13

Overview of Microsoft Excel as a Data Analysis Tool

1.7K
Microsoft Excel is a cornerstone tool for data analysis and statistical operations, offering a wide array of functionalities to manage, analyze, and visualize data efficiently. Recognized for its versatility, Excel facilitates the performance of basic to complex statistical operations, serving as an indispensable asset for analysts, researchers, and students alike. Excel's significance in data analysis emanates from its spreadsheet environment, where data can be organized in rows and...
1.7K
RNA Structure01:23

RNA Structure

79.2K
Overview
The basic structure of RNA consists of a five-carbon sugar and one of four nitrogenous bases. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
Different Types of RNA Have the Same Basic Structure
There are three main types of ribonucleic acid (RNA): messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). All three RNA types consist of a...
79.2K
RNA Editing02:23

RNA Editing

9.9K
RNA editing is a post-transcriptional modification where a precursor mRNA (pre-mRNA) nucleotide sequence is changed by base insertion, deletion, or modification. The extent of RNA editing varies from a few hundred bases, in mitochondrial DNA of trypanosomes, to a just single base, in nuclear genes of mammals. Even a single base change in the pre-mRNA can convert a codon for one amino acid into the codon for another amino acid or a stop codon. This type of re-coding can significantly affect the...
9.9K
Performing a Simple Data Analysis using MS-Excel Function01:17

Performing a Simple Data Analysis using MS-Excel Function

1.1K
Microsoft Excel offers a suite of functions and tools ideal for statistical analysis, making it accessible to students and researchers. This article outlines fundamental Excel functions pivotal for data analysis.
SUM: This function calculates the total sum of a range of values. It's the foundation for aggregating data, essential for determining overall trends and totals in datasets.
AVERAGE: It computes the mean value of a given set of numbers, providing a quick insight into the central...
1.1K

You might also read

Related Articles

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

Sort by
Same author

Author Correction: A likelihood-based framework for demographic inference from genealogical trees.

Nature genetics·2025
Same author

A likelihood-based framework for demographic inference from genealogical trees.

Nature genetics·2025
Same author

Tree-based QTL mapping with expected local genetic relatedness matrices.

American journal of human genetics·2023
Same author

A likelihood-based framework for demographic inference from genealogical trees.

bioRxiv : the preprint server for biology·2023
Same author

Tree-based QTL mapping with expected local genetic relatedness matrices.

bioRxiv : the preprint server for biology·2023
Same author

A genealogical estimate of genetic relationships.

American journal of human genetics·2022
Same journal

Isolation of Mesenchymal Stem Cell-Derived Extracellular Vesicles.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Modeling Melanoma Immune Surveillance by CAR-T Cells in Human Skin Organoids.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Stepwise Optimization of a Matrigel-Based In Vitro Angiogenesis Assay for Reproducible and Quantifiable 2D-Tube Formation Using HUVECs.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Quantifying Mechanical Properties of Fresh Ovarian Tissue with Optical Brillouin Microscopy.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

3D Chromatin Architecture During Early Development: New Methods and New Findings.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Metabolic Plasticity in Embryogenesis Throughout the Lens of NAD<sup></sup>.

Methods in molecular biology (Clifton, N.J.)·2026
See all related articles

Related Experiment Video

Updated: Feb 13, 2026

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
08:35

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

6.5K

iSeq: Web-Based RNA-seq Data Analysis and Visualization.

Chao Zhang1, Caoqi Fan2, Jingbo Gan2

  • 1PKU-Tsinghua-NIBS Graduate Program, School of Life Sciences, Peking University, Beijing, 100871, China.

Methods in Molecular Biology (Clifton, N.J.)
|March 15, 2018
PubMed
Summary
This summary is machine-generated.

iSeq is a user-friendly web server that simplifies transcriptome sequencing (RNA-seq) data analysis and visualization for researchers. This R-based platform requires no programming skills, making complex genomic data accessible.

Keywords:
Data visualizationGene expression analysisGene ontology enrichmentR-ShinyRNA-seq

More Related Videos

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis
07:29

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Published on: May 16, 2020

6.8K
Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
05:07

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

401

Related Experiment Videos

Last Updated: Feb 13, 2026

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
08:35

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

6.5K
Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis
07:29

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Published on: May 16, 2020

6.8K
Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
05:07

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

401

Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • Transcriptome sequencing (RNA-seq) is a powerful tool for genome-wide transcript characterization.
  • Analyzing large RNA-seq datasets presents significant technical challenges for researchers without specialized bioinformatics expertise.

Purpose of the Study:

  • To develop a user-friendly, integrated platform for RNA-seq data analysis and visualization.
  • To provide wet-lab researchers with accessible tools for interpreting complex sequencing data.

Main Methods:

  • Development of iSeq, an R-based web server utilizing the Shiny framework.
  • Implementation of a streamlined web application with a simple user interface and multiple analysis modules.
  • Creation of a standardized, customizable analytical pipeline for RNA-seq data.

Main Results:

  • iSeq enables users without programming or statistical skills to analyze RNA-seq data.
  • The platform facilitates the generation of publication-level graphs from RNA-seq results.
  • iSeq offers a standardized yet customizable analytical pipeline.

Conclusions:

  • iSeq addresses the need for accessible RNA-seq data analysis tools for wet-lab researchers.
  • The platform democratizes the interpretation of transcriptome sequencing data.
  • iSeq enhances the utility of RNA-seq experiments by simplifying downstream analysis.