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

Universal sequence map (USM) of arbitrary discrete sequences.

Jonas S Almeida1, Susana Vinga

  • 1Dept Biometry & Epidemiology, Medical Univ South Carolina, 135 Cannon street, Suite 303, PO Box 250835, Charleston, SC 29425, USA. almeidaj@musc.edu

BMC Bioinformatics
|March 16, 2002
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

Automatic metabolic breast cancer staging using [¹⁸F]FDG PET/CT: comparison with nuclear medicine physician-based and clinical staging.

European journal of nuclear medicine and molecular imaging·2026
Same author

Uncovering Latent Structure in Gliomas Using Multi-Omics Factor Analysis.

Genes·2026
Same author

Genomic Characterization of Lung Cancer in Never-Smokers Using Deep Learning.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc·2026
Same author

Leveraging large language models for structured information extraction from pathology reports.

Journal of pathology informatics·2025
Same author

Monitoring sleep duration, timing, and continuity among US youth and adults in NHANES using actigraphy.

Sleep health·2025
Same author

mSigSDK - private computation of mutation signatures.

Research square·2025
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

Universal Sequence Mapping (USM) creates a continuous coordinate space for biological sequences, enabling scale-independent analysis. This method allows sequence similarity to be estimated by map distance, advancing bioinformatics.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Sequence Analysis

Background:

  • Representing biological sequences in continuous space has been a long-standing challenge.
  • Existing methods often require fixed memory lengths, limiting scale-independent analysis.
  • The goal is a representation where sequence similarity is directly computable from coordinates.

Purpose of the Study:

  • To develop a method for bijective mapping of discrete sequences into continuous space.
  • To enable scale-independent sequence analysis without assuming memory length.
  • To provide a tool for inferring sequence homology based on coordinate comparisons.

Main Methods:

  • Developed Universal Sequence Mapping (USM), an iterative function for mapping discrete sequences to continuous space.

Related Experiment Videos

  • USM builds upon Chaos Game Representation (CGR) principles.
  • The method is applicable to sequences of arbitrary length and complexity.

    • Main Results:

    • Successfully implemented USM for bijective mapping of sequences to continuous state space.
    • USM generates a representation where map distance directly estimates sequence similarity.
    • Demonstrated USM's applicability to diverse sequence types, including DNA.

    Conclusions:

    • USM facilitates a statistical mechanics approach to sequence analysis.
    • The scale-independent representation removes the need for pre-defined memory lengths.
    • USM advances the investigation of syntactic rules in biological sequences.