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 Concept Videos

Methods to Assess Microbial Communities01:19

Methods to Assess Microbial Communities

Microbial communities, comprising bacteria, archaea, and eukaryotic microorganisms, inhabit diverse ecosystems and play crucial roles in environmental and biological processes. Their diversity is defined by three main parameters: species richness (the number of distinct species), species abundance (the relative quantity of each species), and species evenness (how uniformly individual species are distributed in various locations). These factors together shape the structure and ecological balance...
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Environmental fungi modulate the vaginal mycobiome and cervical disease progression in Hispanic women.

mSystemsยท2026
Same author

Toxic metals impact gut microbiota and metabolic risk in five African-origin populations.

Gut microbes reportsยท2026
Same author

Environmental microbiota transfer from forest soil into urban homes: a proof-of-principle study.

Microbiomeยท2026
Same author

A human-derived Bacteroides strain attenuates depressive-like behavior in a rat model of social defeat-induced stress.

BMC medicineยท2026
Same author

A high fermentable fiber Western diet reduces indole levels.

bioRxiv : the preprint server for biologyยท2026
Same author

Longitudinal changes in gut microbiota across reproductive states in wild baboons.

Research squareยท2026
Same journal

Efficient evidence-based genome annotation with EviAnn.

Nature methodsยท2026
Same journal

ClairS: a deep-learning method for long-read tumor-normal pair somatic small variant calling.

Nature methodsยท2026
Same journal

RNAbpFlow: base pair-augmented SE(3) flow matching for conditional RNA 3D structure generation.

Nature methodsยท2026
Same journal

Spatio-DARLIN enables robust and efficient in situ lineage tracing in mice at single-cell resolution.

Nature methodsยท2026
Same journal

EasyGrid: a versatile platform for automated cryo-EM sample preparation and quality control.

Nature methodsยท2026
Same journal

Cloud-based microscope enables live neuroimaging for 24 h and beyond with worldwide access.

Nature methodsยท2026
See all related articles

Related Experiment Video

Updated: May 23, 2026

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
09:47

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

Predicting bacterial community assemblages using an artificial neural network approach.

Peter E Larsen1, Dawn Field, Jack A Gilbert

  • 1Argonne National Laboratory Biosciences Division, Illinois, USA.

Nature Methods
|April 17, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a novel bioclimatic modeling approach using artificial neural networks to predict microbial community structure. The method accurately forecasts microbial ecosystems by considering environmental factors and biotic interactions.

More Related Videos

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
14:06

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

Related Experiment Videos

Last Updated: May 23, 2026

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
09:47

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
14:06

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

Area of Science:

  • Microbial ecology
  • Bioclimatic modeling
  • Computational biology

Background:

  • Understanding Earth's microbiome interactions with its environment is crucial.
  • Existing models often lack biotic interaction considerations.
  • Microbial community structure prediction is vital for ecological studies.

Purpose of the Study:

  • To develop and validate a bioclimatic modeling approach for predicting microbial community structure.
  • To incorporate environmental parameters and microbial interactions into predictive models.
  • To create a generalized method for diverse microbial ecosystems.

Main Methods:

  • Utilized artificial neural networks for bioclimatic modeling.
  • Incorporated environmental parameters and biotic interactions.
  • Validated predictions against observed community structures using Bray-Curtis similarity.

Main Results:

  • The artificial neural network model outperformed single-species models in predicting community structure.
  • Achieved an average Bray-Curtis similarity of 89.7 for temporal interpolations and extrapolations.
  • Generated the first geographically extrapolated microbial map from single-point observations.

Conclusions:

  • The developed bioclimatic modeling approach effectively predicts microbial community structure.
  • This method offers a powerful tool for ecological studies, especially with increasing taxonomic data.
  • The approach is generalizable to various microbial ecosystems for predictive mapping.