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

Identifying cycling genes by combining sequence homology and expression data.

Yong Lu1, Roni Rosenfeld, Ziv Bar-Joseph

  • 1School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213, USA.

Bioinformatics (Oxford, England)
|July 29, 2006
PubMed
Summary

Identifying cell cycle genes across species is challenging due to noisy data. This study introduces a novel algorithm combining multi-species gene expression data and sequence similarity to accurately identify cycling genes, especially in humans.

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

Real-time forecasting of data revisions in epidemic surveillance streams.

PLoS computational biology·2025
Same author

Federated epidemic surveillance.

PLoS computational biology·2025
Same author

Infectious disease surveillance needs for the United States: lessons from Covid-19.

Frontiers in public health·2024
Same author

Title evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizations.

Nature communications·2024
Same author

Evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizations.

medRxiv : the preprint server for health sciences·2024
Same author

Recalibrating probabilistic forecasts of epidemics.

PLoS computational biology·2022

Area of Science:

  • Genomics
  • Computational Biology
  • Systems Biology

Background:

  • Identifying cell cycle genes is crucial for understanding cell division across species.
  • Previous methods analyzed each species independently, facing challenges with noisy expression data, particularly in humans.
  • Accurate identification of cycling genes is essential for biological research.

Purpose of the Study:

  • To develop and present the first algorithm for combining multi-species microarray expression data to identify cycling genes.
  • To improve the accuracy of cell cycle gene identification by leveraging cross-species information.
  • To overcome data noise issues in gene expression analysis.

Main Methods:

  • Developed a novel algorithm representing genes from multiple species as a graph, with edges indicating sequence similarity.

Related Experiment Videos

  • Utilized Belief Propagation to calculate posterior scores for genes based on expression data.
  • Applied the algorithm to datasets from budding yeast and humans to identify cycling genes.

    • Main Results:

    • The algorithm successfully improved the identification of cell cycle genes in both budding yeast and humans.
    • Incorporating sequence similarity information yielded more accurate gene sets compared to expression data alone.
    • The method demonstrated particular success with human data, using high-quality data from one species to mitigate noise in another.

    Conclusions:

    • The developed algorithm provides a more accurate method for identifying cycling genes by integrating multi-species data and sequence similarity.
    • This approach effectively addresses data noise challenges, especially in complex organisms like humans.
    • The findings offer a significant advancement in the study of cell cycle regulation across different species.