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

A dynamic programming algorithm for binning microbial community profiles.

Quansong Ruan1, Joshua A Steele, Michael S Schwalbach

  • 1Department of Mathematics, University of Southern California 3620 South Vermont Avenue, KAP 108, Los Angeles, California 90089-253, USA.

Bioinformatics (Oxford, England)
|March 29, 2006
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

The dynamic mesoscale sink and source niches for eukaryotic phytoplankton in a subtropical gyre.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Converting relative amplicon abundances to absolute abundances via flow cytometry: metagenomic validation and application to long ocean transects.

ISME communications·2026
Same author

A multidomain lens on the temporal dynamics of surface microbial communities in the Southern Ocean (2013-2019).

Marine environmental research·2026
Same author

Metagenomic Hi‑C Protocols for Viral Genome Binning, Taxonomic Annotation, and Interaction Network Visualization.

Current protocols·2026
Same author

Benchmarking alignment strategies for Hi-C reads in metagenomic Hi-C data.

Genome biology·2026
Same author

A Beginner's Guide to Using DeepVirFinder for Viral Sequence Identification From Metagenomic Datasets.

Current protocols·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026
Same journal

Informative Relational Learning for Adverse Reaction Prediction with Enhanced Generalization to Novel Drugs.

Bioinformatics (Oxford, England)·2026
Same journal

An interpretable deep learning framework uncovers features governing CRISPR-Cas9 genome-editing efficiency.

Bioinformatics (Oxford, England)·2026
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
See all related articles

Automated Ribosomal Intergenic Spacer Analysis (ARISA) data requires preprocessing. A new dynamic programming algorithm improves microbial community analysis by minimizing replicate differences and identifying errors.

Area of Science:

  • Environmental microbiology
  • Molecular ecology

Background:

  • Automated Ribosomal Intergenic Spacer Analysis (ARISA) is a common technique for studying microbial community composition.
  • Directly analyzing ARISA fragment size data is unreliable due to sampling, PCR, and equipment variations.
  • Optimal data preprocessing methods for ARISA are lacking, with binning being a common approach.

Purpose of the Study:

  • To develop an improved data preprocessing method for ARISA analysis.
  • To create a dynamic programming algorithm-based binning method to minimize replicate differences in ARISA data.

Main Methods:

  • Developed a dynamic programming algorithm for binning ARISA data.
  • Applied the algorithm to minimize variations between replicate samples from the same location and time.

Related Experiment Videos

  • Utilized the preprocessed data for downstream statistical analysis and biodiversity assessment.

    • Main Results:

    • The dynamic programming binning method effectively minimized differences between ARISA replicates.
    • Data preprocessing identified outliers caused by systematic errors in an ocean time series dataset.
    • Clustering analysis on binned ARISA data revealed key microbial biodiversity features.

    Conclusions:

    • The developed dynamic programming algorithm offers a robust method for ARISA data preprocessing.
    • This approach enhances the reliability of microbial community profiling using ARISA.
    • Improved ARISA data analysis facilitates a better understanding of microbial community dynamics.