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

Genomics02:02

Genomics

36.5K
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...
36.5K

You might also read

Related Articles

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

Sort by
Same author

A novel Vector-Symbolic Architecture for graph encoding and its application to viral pangenome-based species classification.

BioData mining·2026
Same author

Knowledge preservation in the era of big science and AI: strategies for sustainable scientific research.

Nature communications·2026
Same author

DBSCAN applied to EHRs data from patients with glioblastoma clusters patients based on cytosolic Hsp70 protein, sex, and brain subventricular zone.

BioData mining·2026
Same author

Data-driven probabilistic mapping of the spatial and molecular landscape of glioma.

Brain communications·2026
Same author

Multicomponent interventions and technologies to reduce the burden of frailty, functional, and cognitive decline: insights from the Age-It Research Program.

The journals of gerontology. Series B, Psychological sciences and social sciences·2025
Same author

Comment on "Using genomic data and machine learning to predict antibiotic resistance: A tutorial paper".

PLoS computational biology·2025
Same journal

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

PLoS computational biology·2026
Same journal

Exploring the structural lexicon of the Proteome via Metric Geometry.

PLoS computational biology·2026
Same journal

Linking retinal sampling in neural encoding models to temporal profiles of visual processing in humans.

PLoS computational biology·2026
Same journal

CAdir: Joint clustering of cells and genes for single-cell transcriptomics with visualization-driven cluster quality assessment.

PLoS computational biology·2026
Same journal

Systematic design of auxotrophic strains and media conditions to probe metabolic functions in E. coli.

PLoS computational biology·2026
Same journal

Neuronal excitability and parameter variability in the Hodgkin-Huxley model.

PLoS computational biology·2026
See all related articles

Related Experiment Video

Updated: Jul 24, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.3K

Ten quick tips for avoiding pitfalls in multi-omics data integration analyses.

Davide Chicco1, Fabio Cumbo2, Claudio Angione3

  • 1Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Ontario, Canada.

Plos Computational Biology
|July 6, 2023
PubMed
Summary
This summary is machine-generated.

This study offers ten practical tips for accurately integrating diverse omics data, crucial for advancing bioinformatics research and avoiding common pitfalls in data analysis.

More Related Videos

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

3.7K
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

2.2K

Related Experiment Videos

Last Updated: Jul 24, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.3K
Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

3.7K
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

2.2K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Bioinformatics data analysis is key to biological discovery and medical advancements.
  • Integrating high-throughput omics data (proteomics, transcriptomics, etc.) provides complementary insights.
  • Heterogeneity in omics data often leads to errors during integration.

Purpose of the Study:

  • To provide ten guidelines for correct omics data integration.
  • To help researchers avoid common mistakes in bioinformatics studies.
  • To simplify omics data integration for beginners and experts.

Main Methods:

  • Development of ten practical tips for omics data integration.
  • Focus on avoiding common errors observed in published research.
  • Use of accessible language for broad understanding.

Main Results:

  • A set of ten actionable recommendations for omics data integration.
  • Identification of frequent errors to prevent in bioinformatics workflows.
  • Guidelines applicable to both novice and experienced bioinformaticians.

Conclusions:

  • Correct omics data integration is essential for robust bioinformatics research.
  • Adhering to these guidelines can improve the accuracy and reliability of biological insights.
  • The tips aim to enhance the overall quality of omics data analysis.