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

Experimental RNAi

Experimental RNAi

RNA interference (RNAi) is a cellular mechanism that inhibits gene expression by suppressing its transcription or activating the RNA degradation process. The mechanism was discovered by Andrew Fire and Craig Mello in 1998 in plants. Today, it is observed in almost all eukaryotes, including protozoa, flies, nematodes, insects, parasites, and mammals. This precise cellular mechanism of gene silencing has been developed into a technique that provides an efficient way to identify and determine the...

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...

Ribosome Profiling

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique helps...

You might also read

Related Articles

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

Sort by

Same author

Integrating AUROC and SSMD for quality control in high-throughput screening assays.

SLAS discovery : advancing life sciences R & D·2025

Same author

Fasting and Glucose Metabolism Differentially Impact Peripheral Inflammation in Human Type 2 Diabetes.

Nutrients·2024

Same author

RespirAnalyzer: an R package for analyzing data from continuous monitoring of respiratory signals.

Bioinformatics advances·2024

Same author

MIRS: An AI scoring system for predicting the prognosis and therapy of breast cancer.

iScience·2023

Same author

Comprehensive Analysis of Tumor Microenvironment Reveals Prognostic ceRNA Network Related to Immune Infiltration in Sarcoma.

Clinical cancer research : an official journal of the American Association for Cancer Research·2023

Same author

Metabolic Pathway Pairwise-Based Signature as a Potential Non-Invasive Diagnostic Marker in Alzheimer's Disease Patients.

Genes·2023

Search research articles

Related Experiment Video

Updated: Jun 13, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

A method for effectively comparing gene effects in multiple conditions in RNAi and expression-profiling research.

Xiaohua Douglas Zhang¹

¹Biometrics Research, Merck Research Laboratories, West Point, PA 19486, USA. Xiaohua_zhang@merck.com

Pharmacogenomics

|April 20, 2010

Summary

A novel analytical method using standardized mean of contrast (SMC) and c(+)-probability analysis improves gene effect comparisons in RNA interference (RNAi) and expression profiling research. This approach offers more robust and reliable conclusions than traditional contrast analysis.

More Related Videos

Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish

Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish

Published on: October 27, 2017

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Related Experiment Videos

Last Updated: Jun 13, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish

Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish

Published on: October 27, 2017

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Area of Science:

Bioinformatics
Genomics
Statistical Genetics

Background:

Traditional contrast analysis in gene expression studies faces limitations in accurately comparing gene effects across multiple conditions.
Existing methods can yield misleading results, particularly in RNA interference (RNAi) and microarray experiments.

Purpose of the Study:

To develop and validate a new analytical method for comparing gene effects in RNAi and expression-profiling research.
To overcome the shortcomings of traditional contrast analysis in handling multiple experimental conditions.

Main Methods:

Introduction of a new method incorporating a contrast variable, standardized mean of contrast (SMC), and c(+)-probability analysis.
Evaluation through simulation studies and application to real-world data.

Main Results:

The proposed method directly assesses the strength of comparison, addressing the primary research question.
Standardized mean of contrast (SMC) and c(+)-probability are robust to sample size and capture data variability effectively.
Simulation and application studies demonstrated that the new method yields reasonable and sensible conclusions, unlike traditional methods which produced erroneous results.

Conclusions:

The new analytical method offers a more reliable approach for comparing gene effects across multiple conditions.
This method has potential applications in hit selection for RNAi screens and identifying differentially expressed genes in microarray analyses.