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

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.

Gene Families

Gene Families

Gene families consist of groups of genes proposed to have originated from a common ancestor. Typically these arise through events in which a gene or genes are mistakenly duplicated during cell division. Unlike their parent genes (which are subject to selection pressure to maintain function), these gene copies do not need to preserve their sequences and may evolve at a relatively faster rate.
Occasionally these regions can be adapted to take on new roles within the organism, becoming novel genes...

Gene Families

Gene Families

Structure of a Gene

Structure of a Gene

A gene is the fundamental unit of heredity. Every individual has two copies of each gene, one inherited from each parent. Although most people contain the same genes, there is a small fraction that is slightly different amongst people. A gene with a small difference in its sequence of DNA bases forms different alleles, contributing to different phenotypes.
However, only 1% of the DNA is composed of genes that encode proteins; the rest, 99% is non-coding DNA. This non-coding DNA performs...

Organization of Genes

Organization of Genes

Organization of Genes

Organization of Genes

You might also read

Related Articles

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

Sort by

Same author

Extensive variation between chromosomes of North American and European hop.

Nature communications·2026

Same author

A geospatial hybrid platform to support public policy-making and monitoring for community-based food management and security in the context of global climate change: A study protocol.

PloS one·2026

Same author

Dormancy Versus Germination: 3D Protein Modeling and Evolutionary Analyses Define the Roles of Genetic Variants in the Barley MKK3 Enzyme.

International journal of molecular sciences·2026

Same author

Mosquito sex separation using complementation of selectable traits and engineered neo-sex chromosomes.

Nature communications·2025

Same author

Detection of sex chromosomes in Tephritid pests using R-CQ and KAMY, two computational methods to support generic pest management applications.

Insect science·2025

Same author

Postdomestication selection of MKK3 shaped seed dormancy and end-use traits in barley.

Science (New York, N.Y.)·2025

Search research articles

Related Experiment Video

Updated: Mar 27, 2026

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Published on: June 17, 2012

A Factor Graph Approach to Automated GO Annotation.

Flavio E Spetale^1,2, Elizabeth Tapia^1,2, Flavia Krsticevic^1,3

¹CIFASIS-Conicet Institute, Rosario, Argentina.

|January 16, 2016

Summary

This summary is machine-generated.

This study introduces a factor graph approach for automated Gene Ontology (GO) annotation, improving gene function prediction accuracy. The method enhances gene annotation by leveraging GO structure and noisy predictions for better biological insights.

More Related Videos

A Web Tool for Generating High Quality Machine-readable Biological Pathways

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Related Experiment Videos

Last Updated: Mar 27, 2026

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Published on: June 17, 2012

A Web Tool for Generating High Quality Machine-readable Biological Pathways

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Genomic data volume necessitates efficient computational gene annotation.
Automated Gene Ontology (GO) annotation methods are crucial for interpreting gene functions.
Hierarchical ensemble classification offers interpretable GO annotation.

Purpose of the Study:

To present a factor graph approach for automated GO annotation.
To improve the accuracy and interpretability of gene function predictions.
To extend the method for identifying novel gene annotations in plants.

Main Methods:

Developed a factor graph framework for hierarchical ensemble GO annotation.
Enriched the factor graph to handle noisy GO-term predictions.
Employed an iterative message passing algorithm to compute GO-term probabilities.

Main Results:

Achieved significant improvements over competing methods for GO Molecular Function annotation.
Demonstrated effectiveness with diverse species (yeast, Arabidopsis, fruit fly) and varied data quality.
Successfully extended the approach for identifying novel annotations in tomato (Solanum lycopersicum).

Conclusions:

The factor graph approach provides a robust and accurate method for automated GO annotation.
This framework enhances the understanding of gene function, especially for proteins with unknown roles.
The method shows promise for accelerating functional genomics research in plants.