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

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

19.3K
Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
19.3K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

7.3K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
7.3K
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

19.9K
A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
19.9K

You might also read

Related Articles

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

Sort by
Same author

Structural features of endogenous polyphenols in modulating oxidative stability of tiger nut (Cyperus esculentus L.) oil: Insights from Rancimat and density functional theory.

Food chemistry·2026
Same author

Delayed Arousal Response to Sleep Apnea Encodes Mortality.

medRxiv : the preprint server for health sciences·2026
Same author

SERPINE1 drives ferroptosis in acute respiratory distress syndrome by disrupting mitochondrial NAD<sup>+</sup> homeostasis and suppressing Sirt3 activity.

Redox biology·2026
Same author

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

Proceedings. IEEE International Conference on Semantic Computing·2026
Same author

Protocol for detecting genome-wide introgressed genes and evaluating their functional legacy.

STAR protocols·2026
Same author

A layered standards framework for integrating single-cell and spatial omics data into brain cell atlases.

bioRxiv : the preprint server for biology·2026

Related Experiment Video

Updated: Apr 5, 2026

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing
11:11

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Published on: August 24, 2017

17.5K

Mining Relation Reversals in the Evolution of SNOMED CT Using MapReduce.

Shiqiang Tao1, Licong Cui2, Wei Zhu2

  • 1Department of EECS, Case Western Reserve University, Cleveland, OH, USA ; Division of Medical Informatics, Case Western Reserve University, Cleveland, OH, USA.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science
|August 26, 2015
PubMed
Summary

We identified 48 hierarchical relation reversals in 8 versions of SNOMED CT using cloud computing. These changes, mainly in Body Structure, Clinical Finding, and Procedure, highlight areas for ontological modeling improvements.

More Related Videos

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

1.0K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.9K

Related Experiment Videos

Last Updated: Apr 5, 2026

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing
11:11

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Published on: August 24, 2017

17.5K
Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

1.0K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.9K

Area of Science:

  • Medical Informatics
  • Ontology Engineering
  • Computational Linguistics

Background:

  • Ontological systems, like SNOMED CT, are crucial for standardizing medical terminology.
  • Changes between versions of large ontologies can introduce inconsistencies, such as relation reversals.
  • Systematic identification of these reversals is necessary for maintaining ontological integrity.

Purpose of the Study:

  • To systematically extract and quantify hierarchical relation reversals across multiple versions of SNOMED CT.
  • To analyze the characteristics and locations of these relation reversals.
  • To assess the utility of relation reversals for understanding ontological evolution and identifying modeling issues.

Main Methods:

  • Development and application of scalable MapReduce algorithms for computing transitive closure and set operations.
  • Pairwise comparison of 8 SNOMED CT versions (2009-2014) using a 30-node cloud computing environment.
  • Analysis of identified reversals based on affected sub-hierarchies and path lengths.

Main Results:

  • 48 hierarchical relation reversals were identified across 8 SNOMED CT versions within 18 minutes.
  • The vast majority of reversals occurred in the Body Structure, Clinical Finding, and Procedure sub-hierarchies.
  • Reversals predominantly involved paths of length two, with some involving uncoupling before reversal.

Conclusions:

  • Cloud-based computation provides a scalable solution for detecting relation reversals in large ontologies.
  • Identified reversals pinpoint areas requiring attention in SNOMED CT modeling and maintenance.
  • Understanding relation reversals aids in comparative ontology visualization and cycle detection.