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

A greedy strategy for finding motifs from yes-no examples

E Tateishi1, S Miyano

  • 1Department of Information Systems, Kyushu University, Kasuga, Japan. tateishi@rifis.kyushu-u.ac.jp

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|January 1, 1996
PubMed
Summary

A new motif definition and greedy algorithm can identify biological patterns from examples. This method is effective for finding motifs in DNA sequences like splicing sites and promoters.

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

A novel genetic and morphologic phenotype of ARID2-mediated myelodysplasia.

Leukemia·2017
Same author

Donor cell-derived transient abnormal myelopoiesis as a specific complication of umbilical cord blood transplantation.

Bone marrow transplantation·2017
Same author

Frequent somatic mutations in epigenetic regulators in newly diagnosed chronic myeloid leukemia.

Blood cancer journal·2017
Same author

Identification of cell-type-specific mutations in nodal T-cell lymphomas.

Blood cancer journal·2017
Same author

Comprehensive mutational analysis of primary and relapse acute promyelocytic leukemia.

Leukemia·2016
Same author

Long-term outcome of 6-month maintenance chemotherapy for acute lymphoblastic leukemia in children.

Leukemia·2016

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Pattern Recognition

Background:

  • Motifs are crucial for understanding biological sequence function.
  • Existing motif discovery methods have limitations in capturing complex patterns.
  • PROSITE database and regular pattern languages provide context for motif definition.

Purpose of the Study:

  • To introduce a novel definition of motifs as generalized expressions.
  • To develop a greedy algorithm for motif discovery using probabilistic arguments.
  • To evaluate the algorithm's performance on biological sequence data.

Main Methods:

  • Defining motifs as expressions Z1.Z2...Zn, where Zi are sets of strings from a specified family (type).
  • Developing a greedy strategy that utilizes probabilistic arguments to find motifs from positive and negative examples.

Related Experiment Videos

  • Implementing and testing the algorithm on biological datasets.

    • Main Results:

    • The proposed motif definition encompasses existing motif types like those in PROSITE and regular patterns.
    • The greedy algorithm successfully identifies motifs with ambiguity from limited examples.
    • Experimental results demonstrate effectiveness on E. coli promoters and splicing sites.

    Conclusions:

    • The novel motif definition and greedy algorithm offer a powerful approach for biological pattern discovery.
    • This method provides a theoretical and practical framework for motif identification in bioinformatics.
    • The findings have implications for understanding gene regulation and protein function through sequence analysis.