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

Efficient Boolean implementation of universal sequence maps (bUSM).

John Schwacke1, Jonas S Almeida

  • 1Department of Biometry and Epidemiology, Medical University of South Carolina, 135 Cannon Street, Suite 303, PO Box 250835, Charleston, SC 29425, USA. schwacke@musc.edu

BMC Bioinformatics
|October 22, 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

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 author

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

bioRxiv : the preprint server for biology·2025
Same author

Deep learning analysis of hematoxylin and eosin-stained benign breast biopsies to predict future invasive breast cancer.

JNCI cancer spectrum·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

This study refines Universal Sequence Maps (USM) for genomic analysis, improving accuracy and efficiency in identifying sequence similarities. The enhanced algorithm offers practical advantages for computational biology research.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomic Sequence Analysis

Background:

  • Introduces Universal Sequence Maps (USM) as a novel method for discrete sequence representation.
  • Highlights USM's generalization and extension of Chaos Game Representation (CGR) for genomic data.

Purpose of the Study:

  • Address practical implementation challenges of Universal Sequence Maps (USM).
  • Enhance the accuracy and efficiency of USM for genomic sequence analysis.

Main Methods:

  • Developed a modified USM algorithm to eliminate overestimation of similar segment lengths.
  • Incorporated finite word length coordinate representations for identifying long similar segments.
  • Implemented computationally efficient operations and a conversion method for USM coordinates.

Related Experiment Videos

Main Results:

  • The revised algorithm accurately identifies similar sequence segments without overestimation.
  • Successfully identified arbitrarily long similar segments using finite coordinate representations.
  • Demonstrated improved computational performance and efficient coordinate recovery.

Conclusions:

  • Confirmed that desirable USM properties are maintained in a practical implementation.
  • The proposed USM variation significantly increases the speed of local sequence identity determination.
  • Offers a robust and efficient tool for genomic sequence analysis.