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

The stationary statistical properties of human coding sequences.

D C Torney1, C C Whittaker, G Xie

  • 1Theoretical Division and U.S.D.O.E. Joint Genome Institute, Mail Stop K710, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. dct@lan1.gov

Journal of Molecular Biology
|March 5, 1999
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

Observation of Charmonium Sequential Suppression in Heavy-Ion Collisions at the Relativistic Heavy Ion Collider.

Physical review letters·2026
Same author

Energy Independence of the Collins Asymmetry in p^{↑}p Collisions.

Physical review letters·2026
Same author

Precision Measurement of Net-Proton-Number Fluctuations in Au+Au Collisions at RHIC.

Physical review letters·2025
Same author

Measurement of Two-Point Energy Correlators within Jets in p+p Collisions at sqrt[s]=200  GeV.

Physical review letters·2025
Same author

Onset of Constituent Quark Number Scaling in Heavy-Ion Collisions at RHIC.

Physical review letters·2025
Same author

Using Behaviour Change Frameworks and Bayesian Network Modelling to Support Marine Biosecurity Practices: A New South Wales Waterways Case Study.

Environmental management·2025
Same journal

Clinical inflammasome biomarkers: Progress and prospects.

Journal of molecular biology·2026
Same journal

Biologically Relevant, Cationic Residues in Human Rhinovirus Stabilize Capsid-Bound RNA Duplexes, and Restrict Capsid Flexibility.

Journal of molecular biology·2026
Same journal

Cryo-EM structures of phage T4 infection intermediate.

Journal of molecular biology·2026
Same journal

A classic fold with a twist: Structural architecture of Dhillonvirus phage Bas18.

Journal of molecular biology·2026
Same journal

Tesorai Search: cloud-based database search engine boosts identifications for mass spectrometry proteomics with a pretrained peptide-spectrum deep-learning model.

Journal of molecular biology·2026
Same journal

Characterization of diverse functions of NRF1 nuclear localization sequence.

Journal of molecular biology·2026
See all related articles

We developed a new method to analyze biological sequences using binary encoding. This approach reveals statistical properties of sequence classes, including human coding sequences.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Genetics

Background:

  • Understanding the statistical properties of biological sequences is crucial for deciphering their function and evolution.
  • Existing methods may not comprehensively capture all characteristic statistical properties of sequence classes.

Purpose of the Study:

  • To introduce a generally applicable method for discovering and quantifying statistical properties of biological sequence classes.
  • To illustrate the method's utility by characterizing human coding sequences.

Main Methods:

  • A reversible binary encoding scheme converting sequences into binary digits (-1 and +1).
  • Utilizing sample cumulants on subsets of digit positions to represent statistical properties.
  • Application to human coding sequences to characterize stationary statistical properties.

Related Experiment Videos

Main Results:

  • Demonstrated that sample cumulants on digit positions reveal all statistical properties of a sequence class.
  • Provided a complete characterization of stationary statistical properties for human coding sequences.
  • Identified several significant sample cumulants that describe these properties.

Conclusions:

  • The proposed method offers a robust framework for comprehensive statistical analysis of biological sequences.
  • This approach facilitates deeper insights into sequence characteristics and their biological implications.
  • The characterization of human coding sequences serves as a practical demonstration of the method's effectiveness.