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

Maximum A posteriori classification of DNA structure from sequence information

D M Loewenstern1, H M Berman, H Hirsh

  • 1Department of Computer Science, Rutgers University, Piscataway, NJ 08855, USA. loewenst@paul.rutgers.edu

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|August 11, 1998
PubMed
Summary

We developed LLLAMA, an algorithm for sequence entropy estimation, which accurately predicts DNA helical conformation with a low 3.6% error rate. This method is particularly useful for biological sequence analysis and classification tasks.

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

Conformations of the sugar-phosphate backbone in helical DNA crystal structures.

Biopolymers·2009
Same author

Indirect readout of DNA sequence at the primary-kink site in the CAP-DNA complex: DNA binding specificity based on energetics of DNA kinking.

Journal of molecular biology·2001
Same author

Indirect readout of DNA sequence at the primary-kink site in the CAP-DNA complex: alteration of DNA binding specificity through alteration of DNA kinking.

Journal of molecular biology·2001
Same author

A standard reference frame for the description of nucleic acid base-pair geometry.

Journal of molecular biology·2001
Same author

The crystal and molecular structure of a collagen-like peptide with a biologically relevant sequence.

Journal of molecular biology·2001
Same author

Checking nucleic acid crystal structures.

Acta crystallographica. Section D, Biological crystallography·2001

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Biological sequence analysis often involves identifying patterns with minor variations.
  • Accurate entropy estimation is crucial for sequence modeling and classification.
  • Traditional methods may not fully capture the nuances of biological sequences.

Purpose of the Study:

  • To introduce LLLAMA, a novel algorithm for sequence entropy estimation.
  • To apply LLLAMA for classifying three-dimensional structures of short DNA sequences.
  • To evaluate the performance of LLLAMA against established classification methods.

Main Methods:

  • LLLAMA combines simple pattern recognizers to model sequences.
  • Each recognizer identifies partial matches within subsequences.

Related Experiment Videos

  • Two variants, LLLAMA-length and LLLAMA-alone, use entropy estimates for classification.

    • Main Results:

    • LLLAMA demonstrated high suitability for biological sequence domains.
    • Achieved a low 3.6% error rate in predicting helical conformation of oligonucleotides.
    • Outperformed traditional methods in specific DNA structure classification tasks.

    Conclusions:

    • LLLAMA provides a robust method for sequence entropy estimation and classification.
    • The algorithm shows significant promise for analyzing biological sequences, especially DNA.
    • LLLAMA offers a competitive alternative to existing automated classifier generation techniques.