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

Ribosome Profiling02:24

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

Antibiotic non-susceptibility associated serotypes of invasive pneumococcal disease - a nationwide population study from Switzerland, 2012-2022.

Pneumonia (Nathan Qld.)·2026
Same author

Efficacy and Safety Evidence Supporting Cancer Drug Approvals in Switzerland (2001-2020): A Meta-Analysis of Pivotal Randomised Controlled Trials.

The Lancet regional health. Europe·2026
Same author

Referral pathway for adults with chronic cough in Switzerland from a Delphi-based consensus study.

Postgraduate medicine·2026
Same author

Temporal Trends and Burden of Hospitalizations for Presumed Cardiac Sarcoidosis.

Respiration; international review of thoracic diseases·2026
Same author

Safety of Multi-Omics-Guided Therapy in Advanced Melanoma: A Matched Comparative Cohort Analysis.

JCO precision oncology·2026
Same author

Effect of electronic nicotine delivery systems for smoking cessation on sleep quality: secondary analysis of a randomized controlled trial.

Sleep·2026
Same journal

Covariance decomposition for distance based species tree estimation.

BMC bioinformatics·2026
Same journal

SNPio: a Python interface for population genomic data processing.

BMC bioinformatics·2026
Same journal

SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.

BMC bioinformatics·2026
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: May 10, 2026

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
12:54

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Exploring the transcription factor activity in high-throughput gene expression data using RLQ analysis.

Florent Baty1, Jochen Rüdiger, Nicola Miglino

  • 1Division of Pulmonary Medicine, Cantonal Hospital St, Gallen, Rorschacherstrasse 95, CH-9007 St, Gallen, Switzerland. florent.baty@kssg.ch

BMC Bioinformatics
|June 8, 2013
PubMed
Summary
This summary is machine-generated.

The RLQ statistical method enhances gene expression analysis by integrating external data. This approach effectively predicts transcription factor activity and reveals hidden biological insights from complex microarray datasets.

More Related Videos

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

Identification of Transcription Factor Regulators using Medium-Throughput Screening of Arrayed Libraries and a Dual-Luciferase-Based Reporter
11:32

Identification of Transcription Factor Regulators using Medium-Throughput Screening of Arrayed Libraries and a Dual-Luciferase-Based Reporter

Published on: March 27, 2020

Related Experiment Videos

Last Updated: May 10, 2026

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
12:54

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

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

Identification of Transcription Factor Regulators using Medium-Throughput Screening of Arrayed Libraries and a Dual-Luciferase-Based Reporter
11:32

Identification of Transcription Factor Regulators using Medium-Throughput Screening of Arrayed Libraries and a Dual-Luciferase-Based Reporter

Published on: March 27, 2020

Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Gene expression microarray data analysis is complex.
  • External information (gene annotations, experimental variables) aids interpretation.
  • Transcription factor activity is crucial for gene regulation.

Purpose of the Study:

  • To present the multivariate statistical method RLQ for analyzing microarray data with external information.
  • To demonstrate RLQ's utility in predicting transcription factor activity.

Main Methods:

  • The RLQ multivariate statistical method was developed and applied.
  • The method integrates gene expression data with external information on genes and samples.
  • Analysis of transcription factor activity in response to steroid treatment in lung fibroblasts served as an example.

Main Results:

  • RLQ successfully predicted transcription factor activity.
  • The method integrated diverse external information sources.
  • RLQ analysis was validated using alternative statistical and biological methods.

Conclusions:

  • RLQ efficiently extracts and visualizes structures in gene expression datasets.
  • The method directly models the relationship between experimental variables and gene annotations.