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

19.4K
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.
19.4K

You might also read

Related Articles

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

Sort by
Same author

Macrophage heterogeneity influences cellular response to HIV infection and latency modulation.

Journal of leukocyte biology·2026
Same author

Ten common mistakes that could ruin your enrichment analysis.

PLoS computational biology·2026
Same author

Characterisation of vaginal <i>Lactobacillus</i> isolates from South African women towards the development of a biotherapeutic to optimise the vaginal microbiome.

bioRxiv : the preprint server for biology·2026
Same author

Metabolic pathways fuelling devil facial tumour diseases.

The FEBS journal·2026
Same author

Site-Specific Biomarkers in Keloid Disease Differentiate Keloid Scars From Normal Skin, DFSP, and Fibrosarcoma: Insights From Cell and Tissue Analysis.

The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society·2025
Same author

Purification of mitochondria from skeletal muscle tissue for transcriptomic analyses reveals localization of nuclear-encoded noncoding RNAs.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology·2024
Same journal

Another 10 years of PLOS Computational Biology: A data-driven reflection on trends in genomics research.

PLoS computational biology·2026
Same journal

Mobility data resolution needed to inform predictive models of spatial epidemic spread from mobile phone data.

PLoS computational biology·2026
Same journal

DeepMethylation: A deep learning framework for tissue-specific DNA methylation prediction and functional variant annotation.

PLoS computational biology·2026
Same journal

Redefining and estimating the early-phase reproduction ratio for epidemic outbreaks in spatially structured populations.

PLoS computational biology·2026
Same journal

Optimized phenotype definitions boost GWAS power.

PLoS computational biology·2026
Same journal

Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.

PLoS computational biology·2026
See all related articles

Related Experiment Video

Updated: Oct 1, 2025

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

4.4K

Urgent need for consistent standards in functional enrichment analysis.

Kaumadi Wijesooriya1, Sameer A Jadaan2, Kaushalya L Perera1

  • 1Deakin University, School of Life and Environmental Sciences, Geelong, Australia.

Plos Computational Biology
|March 9, 2022
PubMed
Summary
This summary is machine-generated.

Most gene set enrichment analyses in published studies contain critical errors, such as incorrect background gene lists and lack of p-value correction. These flaws in functional enrichment analysis compromise result reliability and necessitate improved standards.

More Related Videos

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

18.0K
Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.3K

Related Experiment Videos

Last Updated: Oct 1, 2025

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

4.4K
A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

18.0K
Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.3K

Area of Science:

  • Computational biology
  • Bioinformatics
  • Genomics

Background:

  • Gene set enrichment tests, also known as functional enrichment analysis, are widely used in computational biology.
  • Concerns exist regarding the incorrect application of these methods, leading to unreliable results in peer-reviewed publications.

Purpose of the Study:

  • To assess the frequency of methodological flaws in published functional enrichment analyses.
  • To evaluate the impact of these flaws on study outcomes.

Main Methods:

  • A systematic screen of 186 open-access research articles reporting functional enrichment results was conducted.
  • Seven independent RNA-seq datasets were used to demonstrate the effect of enrichment tool misuse on results.

Main Results:

  • 95% of over-representation tests lacked appropriate background gene lists or adequate description.
  • 43% of analyses failed to perform p-value correction for multiple testing.
  • Methodological deficiencies were not linked to journal metrics, and misuse of tools significantly altered results.

Conclusions:

  • The majority of published functional enrichment studies exhibit significant methodological flaws.
  • There is a critical need for establishing and enforcing stronger standards for conducting and reporting functional enrichment analyses.