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

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.
Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
Evolution of Microbial Genome01:08

Evolution of Microbial Genome

Microbial genome evolution is a highly dynamic process shaped by continual gene gain and loss across species and strains. This genomic flexibility allows microorganisms to adapt rapidly to environmental pressures and interactions with other organisms. Central to understanding this diversity is the distinction between the core and pan genomes.The core genome comprises the genes shared by all sampled strains of a species, representing essential functions needed for fundamental cellular processes.
RNA-seq03:21

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 microarray-based...
Evolutionary Relationships through Genome Comparisons02:54

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...

You might also read

Related Articles

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

Sort by
Same author

PyamilySeq: exposing the fragility of conventional gene (re)clustering and prokaryotic pangenomic inference methods.

NAR genomics and bioinformatics·2026
Same author

Spatial transcriptomics reveals expression gradients in developing wheat inflorescences at cellular resolution.

The Plant cell·2025
Same author

A dataset of tissue-specific gene expression dynamics during seed development in Brassica.

Scientific data·2025
Same author

Genomic and genetic insights into Mendel's pea genes.

Nature·2025
Same author

Lineage-specific microbial protein prediction enables large-scale exploration of protein ecology within the human gut.

Nature communications·2025
Same author

Heuristics for the run-length encoded Burrows-Wheeler transform alphabet ordering problem.

Journal of heuristics·2025

Related Experiment Video

Updated: Jul 5, 2026

Tracking and Quantifying Developmental Processes in C. elegans Using Open-source Tools
10:41

Tracking and Quantifying Developmental Processes in C. elegans Using Open-source Tools

Published on: December 16, 2015

Genome assemblies and annotations are not static and need support for tracking their evolution.

Nicholas J Dimonaco1,2, Amanda Clare2, Martin Vickers3

  • 1Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, BT9 5DL Belfast, United Kingdom.

Briefings in Bioinformatics
|July 3, 2026
PubMed
Summary
This summary is machine-generated.

Genomic data formats FASTA and GFF lack version control, hindering scientific progress. Adopting software engineering

Keywords:
annotationsfile formatsgenomicsversion control

More Related Videos

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies
12:08

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies

Published on: August 20, 2021

Related Experiment Videos

Last Updated: Jul 5, 2026

Tracking and Quantifying Developmental Processes in C. elegans Using Open-source Tools
10:41

Tracking and Quantifying Developmental Processes in C. elegans Using Open-source Tools

Published on: December 16, 2015

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies
12:08

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies

Published on: August 20, 2021

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Genomic data has historically relied on FASTA and GFF formats for sequence and annotation storage.
  • Advancements in sequencing and assembly enable complex, chromosome-level genome data.
  • Current genome annotation updates lack systematic versioning, creating data inconsistencies.

Purpose of the Study:

  • To highlight the limitations of current genomic file formats for evolving data.
  • To advocate for the adoption of version control in genomics.
  • To propose requirements for future genomic data formats.

Main Methods:

  • Analysis of existing genomic file format limitations (FASTA, GFF).
  • Examination of the impact of unversioned genomic data on scientific reproducibility.
  • Review of version control principles from software engineering.

Main Results:

  • Current formats (FASTA, GFF) are inadequate for tracking incremental changes in genome assemblies and annotations.
  • Lack of versioning prevents computational comparison of different annotation versions.
  • Manual version tracking is unsustainable for large-scale genomics projects.

Conclusions:

  • Genomics must implement systematic version control for data and annotations.
  • Machine-readable formats are needed to capture changes and maintain provenance.
  • Version control is essential for reproducible, large-scale biological research.