Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Genome Annotation and Assembly

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

RNA-seq

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...

You might also read

Related Articles

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

Sort by

Same author

Adjustment for day-to-day variability in the estimation of effective concentrations for the assessment of mixture toxicity.

Archives of toxicology·2025

Same author

Information sharing in high-dimensional gene expression data for improved parameter estimation in concentration-response modelling.

PloS one·2023

Same author

Transcriptome-based prediction of drugs, inhibiting cardiomyogenesis in human induced pluripotent stem cells.

Cell death discovery·2023

Same author

Guidance for statistical design and analysis of toxicological dose-response experiments, based on a comprehensive literature review.

Archives of toxicology·2023

Same author

Statistical analysis of high-dimensional biomedical data: a gentle introduction to analytical goals, common approaches and challenges.

BMC medicine·2023

Same author

Discordance of HER2-Low between Primary Tumors and Matched Distant Metastases in Breast Cancer.

Cancers·2023

Same journal

OmicsTransformer: Self-Supervised Masked Consistency and Uncertainty-Aware Fusion for Robust Multi-Omics Prediction.

Bioinformatics (Oxford, England)·2026

Same journal

Computational Tool Choice Impacts CRISPR Spacer-Proto spacer Detection.

Bioinformatics (Oxford, England)·2026

Same journal

ARISE: RNA-Anchored Shared-Edge Topology and Hierarchical Fusion for Spatial Multi-Omics Integration.

Bioinformatics (Oxford, England)·2026

Same journal

Interactive exploration of biobank-scale ancestral recombination graphs with Lorax.

Bioinformatics (Oxford, England)·2026

Same journal

PepMCP: A Graph-Based Membrane Contact Probability Predictor for Membrane-Lytic Antimicrobial Peptides.

Bioinformatics (Oxford, England)·2026

Same journal

ARGscape: A modular, interactive tool for manipulation of spatiotemporal ancestral recombination graphs.

Bioinformatics (Oxford, England)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 16, 2025

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

AlertGS: determining alerts for gene sets.

Franziska Kappenberg¹, Jörg Rahnenführer¹

¹Department of Statistics, TU Dortmund University, 44227 Dortmund, Germany.

Bioinformatics (Oxford, England)

|April 3, 2025

Summary

This summary is machine-generated.

This study introduces AlertGS, a novel method to identify significant gene sets in expression data. AlertGS provides a global significance statement and the earliest time or concentration of gene set enrichment.

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

Related Experiment Videos

Last Updated: May 16, 2025

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Gene expression studies aim to identify significant genes within specific gene sets.
Modeling concentration/time-response relationships for individual genes is common.
Current methods lack a global significance measure for entire gene sets.

Purpose of the Study:

To extend the concept of gene-wise alerts to gene sets for global significance.
To determine the earliest point of gene set enrichment in concentration or time-response data.
To develop a robust methodology for analyzing gene set behavior in expression studies.

Main Methods:

The AlertGS methodology utilizes a Kolmogorov-Smirnoff type test statistic.
It transfers the concept of alerts from single genes to gene sets.
The approach models concentration/time-response relationships for gene sets.

Main Results:

Simulations demonstrate successful identification of a majority of true gene sets, particularly with fewer signals.
False positive rates are manageable through decorrelation techniques.
Gene set alerts are generally not overestimated, tending towards underestimation.

Conclusions:

AlertGS offers a powerful tool for identifying enriched gene sets in gene expression data.
The method provides a global significance measure and identifies the onset of enrichment.
The AlertGS methodology is implemented and available for reproducible research.