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

Discovering sequence similarity by the algorithmic significance method

A Milosavljević1

  • 1Biological and Medical Research Division, Argonne National Laboratory, Illinois 60439-4833, USA.

Proceedings. International Conference on Intelligent Systems for Molecular Biology
|January 1, 1993
PubMed
Summary

Sequence similarity is determined by minimal-length encoding, identifying common subwords efficiently. This approach precisely calculates the probability of chance similarity, aiding DNA sequence identification.

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

Ultrafast dynamics in core-excited states probed by resonant Auger spectroscopy: pyrrole.

Chemical science·2026
Same author

Aspirin delays the metabolic clock of gestation in women at risk of preeclampsia: abridged secondary publication.

Hong Kong medical journal = Xianggang yi xue za zhi·2024
Same author

Morphological and functional characteristics of satellite glial cells in the peripheral nervous system.

Folia morphologica·2020
Same author

First Report of Cercospora carotae, Causal Agent of Cercospora Leaf Spot of Carrot, in Serbia.

Plant disease·2019
Same author

First Report of Cercospora apii, Causal Agent of Cercospora Early Blight of Celery, in Serbia.

Plant disease·2019
Same author

Melatonin levels in human diabetic dental pulp tissue and its effects on dental pulp cells under hyperglycaemic conditions.

International endodontic journal·2018

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Defining sequence similarity is crucial for biological data analysis.
  • Traditional methods often rely on arbitrary thresholds.
  • Identifying common subwords is key to understanding sequence relationships.

Purpose of the Study:

  • To introduce a novel approach for defining sequence similarity using minimal-length encoding.
  • To develop an algorithmic method for calculating the probability of chance sequence similarity.
  • To demonstrate the effectiveness of this method in identifying DNA sequences.

Main Methods:

  • Utilizing minimal-length encoding based on common subwords.
  • Employing a dynamic programming strategy and directed acyclic word graphs for efficient encoding.

Related Experiment Videos

  • Implementing an algorithmic significance method to determine the probability of chance similarity.

    • Main Results:

    • Minimal-length encoding accurately captures sequence similarity by exploiting common subwords of any length.
    • The algorithmic significance method provides an exact upper bound for chance similarity, removing the need for arbitrary thresholds.
    • Preliminary experiments show that a few keywords can effectively identify DNA sequences, relevant for partial sequencing by hybridization.

    Conclusions:

    • Minimal-length encoding offers a robust and threshold-independent method for assessing sequence similarity.
    • The algorithmic significance approach enhances the reliability of sequence identification in bioinformatics.
    • This method holds significant potential for applications in genomics and partial sequencing technologies.