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 Experiment Videos

Model-based methods for identifying periodically expressed genes based on time course microarray gene expression

Y Luan1, H Li

  • 1Rowe Program in Human Genetics, School of Medicine, University of California, Davis, CA 95616, USA.

Bioinformatics (Oxford, England)
|February 13, 2004
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Phenobarbital induction mediated by a distal CYP2B2 sequence in rat liver transiently transfected in situ.

The Journal of biological chemistry·1996
Same author

Induction of phosphoglycerate kinase 1 gene expression by hypoxia. Roles of Arnt and HIF1alpha.

The Journal of biological chemistry·1996
Same author

Identification of novel regions of deletion in familial Wilms' tumor by comparative genomic hybridization.

Cancer research·1996
Same author

Alternatively spliced cyclin C mRNA is widely expressed, cell cycle regulated, and encodes a truncated cyclin box.

Oncogene·1996
Same author

Emergence of preferred structures in a simple model of protein folding.

Science (New York, N.Y.)·1996
Same author

Developmental regulation of collagen differential expression in the rabbit bladder.

The Journal of urology·1996
Same journal

Cross-Domain Transfer Learning from Peptides to Metabolites Using a Multi-Property Fine-Tuned LLM.

Bioinformatics (Oxford, England)·2026
Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026
Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026
Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
See all related articles

This study introduces a statistical framework to identify periodically expressed genes from microarray data. The method successfully identified numerous cell-cycle and circadian rhythm-regulated genes in yeast and mouse tissues.

Area of Science:

  • Genomics
  • Systems Biology
  • Computational Biology

Background:

  • Gene expression exhibits rhythmic patterns, particularly in biological processes like cell cycles and circadian rhythms.
  • Understanding these rhythmic genes is crucial for elucidating disease mechanisms and identifying potential drug targets.

Purpose of the Study:

  • To develop a statistical framework for identifying periodically expressed genes using microarray time-course data.
  • To apply this framework to yeast cell cycle and mouse circadian rhythm datasets.

Main Methods:

  • A shape-invariant model was developed for analyzing gene expression time-course data.
  • A false discovery rate (FDR) procedure was employed for robust gene identification.
  • The methods were validated using known periodically expressed genes and established cell cycle datasets.

Related Experiment Videos

Main Results:

  • Identified 1010 cell-cycle-regulated genes in yeast datasets with a 0.5% FDR, including 86% of known periodic transcripts.
  • Discovered 344 and 201 circadian rhythmic genes in mouse heart and liver tissues, respectively, at low FDRs.
  • The shape-invariant model demonstrated good data fit and provided estimates for common gene expression patterns and relative phases.

Conclusions:

  • The proposed statistical framework effectively identifies periodically expressed genes from microarray data.
  • This approach advances the understanding of rhythmic gene regulation in cell cycle and circadian processes.
  • The identified genes represent valuable candidates for further investigation into disease mechanisms and therapeutic strategies.