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

Towards a better solution to the shortest common supersequence problem: the deposition and reduction algorithm.

Kang Ning1, Hon Wai Leong

  • 1Department of Computer Science, National University of Singapore, Science Drive, Singapore 117543, Singapore. ningkang@comp.nus.edu.sg

BMC Bioinformatics
|January 16, 2007
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

A pan-cancer single-cell atlas uncovers the role of sex hormones and chromosomes in sex-divergent reprogramming of the tumor microenvironment.

Cell communication and signaling : CCS·2026
Same author

Sulfated bile acid produced by a human gut commensal alleviates paediatric sepsis in mice.

Nature microbiology·2026
Same author

CRISPR Screening Reveals SAA1-Driven Neutrophil Extracellular Traps Promote CD8⁺ T Cell Dysfunction in Renal Cell Carcinoma.

Cancer research·2026
Same author

Global Soil Water Stable Isotope Dataset.

Scientific data·2026
Same author

Unraveling the colonization process of microeukaryotic communities on artificial micro-ecological islands.

Environmental microbiome·2026
Same author

Identification of Human Transferrin Receptor as an Entry Co-receptor for Parvovirus B19 Infection of Human Erythroid Progenitor Cells.

bioRxiv : the preprint server for biology·2026
Same journal

Research on multi-trait genome association study method based on Shannon information entropy.

BMC bioinformatics·2026
Same journal

A multi-view feature fusion framework with interpretable graph convolution for predicting microbe-drug associations.

BMC bioinformatics·2026
Same journal

Covariance decomposition for distance based species tree estimation.

BMC bioinformatics·2026
Same journal

SNPio: a Python interface for population genomic data processing.

BMC bioinformatics·2026
Same journal

SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.

BMC bioinformatics·2026
Same journal

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

BMC bioinformatics·2026
See all related articles

A new Deposition and Reduction (DR) algorithm efficiently solves the Shortest Common Supersequence (SCS) problem for large biological sequence sets. This method outperforms existing heuristics on complex, lengthy sequence instances.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Algorithm Design

Background:

  • The Shortest Common Supersequence (SCS) problem is crucial for biological sequence analysis but is NP-complete.
  • Existing heuristic algorithms struggle with large instances involving many long sequences.
  • There is a need for efficient algorithms that perform well on large-scale SCS problems.

Purpose of the Study:

  • To introduce a novel Deposition and Reduction (DR) algorithm for solving large SCS instances.
  • To address the limitations of current heuristics in handling numerous long biological sequences.
  • To provide an efficient and practical solution for complex SCS problems.

Main Methods:

  • The Deposition and Reduction (DR) algorithm comprises two main processes: deposition and reduction.

Related Experiment Videos

  • The deposition process generates a reduced set of common supersequences.
  • The reduction process refines these supersequences by removing non-essential characters while maintaining the supersequence property.

    • Main Results:

    • The DR algorithm consistently yields superior results compared to established heuristic algorithms on simulated and real biological sequence data.
    • The algorithm demonstrates exceptional performance on large-scale SCS instances.
    • Evaluation confirms the DR algorithm's effectiveness on DNA and protein sequences.

    Conclusions:

    • The DR algorithm offers a practical solution to the challenge of finding SCS for many long sequences.
    • It provides a bounded approximation ratio, ensuring result quality.
    • The algorithm is efficient in terms of time and space complexity, making it suitable for large biological datasets.