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

RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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.

Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

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...

Types of Genetic Transfer Between Organisms

Types of Genetic Transfer Between Organisms

Genetic transfer occurs when genetic information is passed from one organism to another. It occurs via two mechanisms: vertical gene transfer and horizontal gene transfer. Vertical gene transfer occurs when genetic information is transferred from one generation to the next, which happens much more frequently than horizontal gene transfer. Both sexual and asexual reproduction are forms of vertical gene transfer, where one or more organisms pass some or all of their genome onto their progeny.

Genomics

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

You might also read

Related Articles

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

Sort by

Same author

Building Towards Initiation, Moderation, De-Escalation and Cessation of Disease-Modifying Treatments for Multiple Sclerosis in Greece: An Expert Panel Consensus Meeting.

Brain sciences·2026

Same author

GBSC: Graph-Based Sequence Clustering method for similar short tandem repeats in protein sequences.

Bioinformatics (Oxford, England)·2026

Same author

CV19global: harmonized epidemiology, policy, mobility and weather indicators from two years and 54 countries.

Scientific data·2026

Same author

The total eclipse of bioinformatics: From disruption to convention, and a gentle warning.

PLoS computational biology·2026

Same author

Decoding extremophiles: insights from bioinformatics, machine learning, and data-driven approaches.

Briefings in bioinformatics·2026

Same author

Asymmetric divergence of genome organization and transcription beyond LUCA.

Bio Systems·2026

Same journal

Mapping the 3D Chromosome Organization of a Biosynthetic Gene Cluster by Capture Hi-C (CHi-C).

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Mapping the 3D Chromosome Organization of Streptomyces by Hi-C.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

CUT&Tag Epigenomic Profiling of Biosynthetic Gene Clusters in Arabidopsis thaliana.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Rhizobium rhizogenes-Mediated Hairy Root Transformation Protocol for Lotus japonicus and Other Legumes.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Characterization of Bioactive Saponins from Sea Cucumbers.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Methods for Functional Validation of Terpenoid Metabolic Clusters in Nicotiana benthamiana and Aspergillus oryzae.

Methods in molecular biology (Clifton, N.J.)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 30, 2026

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

Biological information extraction and co-occurrence analysis.

Georgios A Pavlopoulos¹, Vasilis J Promponas, Christos A Ouzounis

¹Division of Basic Sciences, University of Crete Medical School, Heraklion, 71110, Greece.

Methods in Molecular Biology (Clifton, N.J.)

|May 3, 2014

Summary

This summary is machine-generated.

This study reviews co-occurrence analysis methods for detecting relationships between biological entities in biomedical texts. It highlights tools like STRING and BioTextQuest for systems biology knowledge inference.

More Related Videos

A Rapid Method to Confine and Safely Handle Bees in the Field

A Rapid Method to Confine and Safely Handle Bees in the Field

Published on: August 23, 2024

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Related Experiment Videos

Last Updated: Apr 30, 2026

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

A Rapid Method to Confine and Safely Handle Bees in the Field

A Rapid Method to Confine and Safely Handle Bees in the Field

Published on: August 23, 2024

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Area of Science:

Biomedical Informatics
Systems Biology
Bioinformatics

Background:

Identifying biological entities in biomedical text is feasible.
Detecting relationships between these biological entities is crucial.
Co-occurrence analysis is a common method for inferring these relationships.

Purpose of the Study:

To review current methods for co-occurrence analysis in biomedical text.
To focus on data storage, analysis, and representation techniques.
To highlight tools for information extraction and knowledge inference in systems biology.

Main Methods:

Review of co-occurrence analysis techniques.
Examination of data storage, analysis, and representation strategies.
Illustration of practical applications using STRING and BioTextQuest.

Main Results:

Co-occurrence analysis reveals associations between bioentities.
These associations form networks with nodes (bioentities) and weighted edges (relationships).
Tools like STRING and BioTextQuest demonstrate practical utility in systems biology.

Conclusions:

Co-occurrence analysis is vital for inferring biological relationships from text.
The chapter discusses current challenges and future directions in the field.
Effective methods and tools are essential for advancing systems biology research.