Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Improving Translational Accuracy

Improving Translational Accuracy

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

Genome Annotation and Assembly

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.

Applications of Molecular Taxonomy

Applications of Molecular Taxonomy

Molecular taxonomy has revolutionized the understanding and classification of bacteria, providing precise insights into their diversity, evolutionary relationships, and ecological roles. By utilizing molecular techniques such as DNA sequencing and fingerprinting, researchers have made significant strides in various fields related to bacterial studies.Resolving Taxonomic AmbiguitiesMolecular taxonomy has been instrumental in distinguishing closely related bacterial species initially thought to...

Gene Evolution - Fast or Slow?

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...

Gene Evolution - Fast or Slow?

Gene Evolution - Fast or Slow?

You might also read

Related Articles

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

Sort by

Same author

Interpretable evaluation of physiological signals for biometric identification.

Computers in biology and medicine·2026

Same author

Balancing privacy and health integrity: A novel framework for ECG signal analysis in immersive environments.

Computers in biology and medicine·2025

Same author

Humans depart from optimal computational models of interactive decision-making during competition under partial information.

Scientific reports·2022

Same author

Theory of mind and decision science: Towards a typology of tasks and computational models.

Neuropsychologia·2020

Same author

A framework for ontology-based question answering with application to parasite immunology.

Journal of biomedical semantics·2015

Same author

A semantic problem solving environment for integrative parasite research: identification of intervention targets for Trypanosoma cruzi.

PLoS neglected tropical diseases·2012

Same journal

Hootation: A GUI and API library for ontology validation and verbalization.

Proceedings. IEEE International Conference on Semantic Computing·2026

Same journal

Developing a high-performing network computation of big bipartite network data toward alcohol use disorder treatment referrals.

Proceedings. IEEE International Conference on Semantic Computing·2025

Same journal

Visual Enhancement and Semantic Segmentation of Murine Tissue Scans with Pulsed THz Spectroscopy.

Proceedings. IEEE International Conference on Semantic Computing·2024

Same journal

Extracting Semantics from Census-based Reference Data.

Proceedings. IEEE International Conference on Semantic Computing·2022

Same journal

Adopting Graph Traversal Techniques for Context-Driven Value Sets Extraction from Biomedical Knowledge Sources.

Proceedings. IEEE International Conference on Semantic Computing·2011

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 20, 2026

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Published on: June 28, 2018

Speeding up Batch Alignment of Large Ontologies Using MapReduce.

Uthayasanker Thayasivam¹, Prashant Doshi²

¹THINC Lab, Dept. of Computer Science, University of Georgia, Athens, GA 30602, USA, uthayasa@cs.uga.edu.

Proceedings. IEEE International Conference on Semantic Computing

|November 18, 2014

Summary

This summary is machine-generated.

Large-scale ontology alignment is challenging. This study introduces a MapReduce-based approach for batch alignment, significantly speeding up the process for large ontology pairs using various alignment algorithms.

More Related Videos

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Published on: May 22, 2018

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Published on: January 9, 2020

Related Experiment Videos

Last Updated: Apr 20, 2026

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Published on: June 28, 2018

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Published on: May 22, 2018

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Published on: January 9, 2020

Area of Science:

Computer Science
Artificial Intelligence
Data Management

Background:

Real-world ontologies are large, often containing thousands of entities.
Ontology repositories frequently compute alignments between ontologies.
Efficiently aligning numerous ontology pairs is a significant computational challenge.

Purpose of the Study:

To address the challenge of batch alignment for large ontologies.
To adapt ontology alignment algorithms for distributed computing environments.
To demonstrate the effectiveness of MapReduce for accelerating ontology alignment.

Main Methods:

Framing the problem as batch alignment.
Utilizing the MapReduce distributed computing paradigm.
Implementing a flexible architecture to support various alignment algorithms.

Main Results:

Achieved flexible and significant speedup for batch alignment of large ontology pairs.
Demonstrated the applicability of the MapReduce approach across four representative alignment algorithms.
Validated the efficiency of the proposed method on large-scale datasets.

Conclusions:

MapReduce provides an effective solution for the batch alignment of large ontologies.
The proposed approach enhances the scalability and performance of ontology repositories.
This method enables faster alignment computation, crucial for dynamic ontology ecosystems.