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

Fast approximate motif statistics.

P Nicodème1

  • 1DKFZ Theoretische Bioinformatik, 69120 Heidelberg, Germany. nicodeme@genopole.cnrs.fr

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|September 6, 2001
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

Proteome analysis based on motif statistics.

Bioinformatics (Oxford, England)·2002
Same author

WWW access to the SYSTERS protein sequence cluster set.

Bioinformatics (Oxford, England)·1999
Same author

SSMAL: similarity searching with alignment graphs.

Bioinformatics (Oxford, England)·1998
Same author

Selecting optimal oligonucleotide primers for multiplex PCR.

Proceedings. International Conference on Intelligent Systems for Molecular Biology·1997
Same journal

GMSA: A Graph Matching and Point Cloud Registration-Based Method for Spatial Transcriptomics Data Alignment.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Investigations on Multiple Protein Scaffold Filling.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Cell Type Prediction for Single-Cell RNA Sequencing Utilizing Unsupervised Domain Adaptation and Semi-Supervised Learning.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

PPIGAN: Prediction of Protein-Protein Interactions Using Generative Adversarial Networks.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Deep Structure-Enhanced Cell Clustering Model for Single-Cell RNA Sequencing Data.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Asymmetric Drug-Drug Interaction Prediction Based on Generative Adversarial Networks and Knowledge Graph.

Journal of computational biology : a journal of computational molecular cell biology·2026
See all related articles

A new approximate method efficiently calculates protein motif occurrences in large databases. This fast computation is highly accurate, enabling rapid proteome comparisons.

Area of Science:

  • Computational biology
  • Bioinformatics
  • Statistical analysis of biological sequences

Background:

  • Protein motifs are crucial for biological function.
  • Accurate statistical analysis of motif occurrences is essential for understanding protein families.
  • Existing methods for motif statistics can be computationally intensive.

Purpose of the Study:

  • To develop a fast approximate method for calculating the statistics of non-self-overlapping motif matches in random text.
  • To assess the accuracy and efficiency of this method for protein motifs.
  • To apply the method for comparing proteomes of different organisms.

Main Methods:

  • The study introduces a fast approximate computational method.
  • This method operates within the nonuniform Bernoulli model for random text.

Related Experiment Videos

  • The approach is specifically designed for non-self-overlapping motif occurrences.

    • Main Results:

    • The approximate method achieves less than 1% error for 96% of PROSITE motifs compared to exact methods.
    • It accurately computes motif occurrences in a large random database (7 million amino acids).
    • The entire PROSITE database can be processed in approximately 30 seconds.

    Conclusions:

    • The developed approximate method is highly accurate and efficient for statistical analysis of protein motifs.
    • This computational advancement facilitates large-scale proteome comparisons.
    • The method is successfully applied to compare C. elegans and S. cerevisiae proteomes.