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

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...

Gene Evolution - Fast or Slow?

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...

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

Temporal phenotyping and prognostic stratification of patients with sepsis through longitudinal clustering.

BioData mining·2025

Same author

Multivariate longitudinal clustering reveals neuropsychological factors as dementia predictors in an Alzheimer's disease progression study.

BioData mining·2025

Same author

Behavior and Task Classification Using Wearable Sensor Data: A Study across Different Ages.

Sensors (Basel, Switzerland)·2023

Same author

EEG-Based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review.

Sensors (Basel, Switzerland)·2023

Same author

Comparing online cognitive load on mobile versus PC-based devices.

Personal and ubiquitous computing·2023

Same author

Stardust: improving spatial transcriptomics data analysis through space-aware modularity optimization-based clustering.

GigaScience·2022

Same journal

Interpretable machine learning for Parkinson's disease diagnosis, staging, and biological mechanism exploration: a multicenter analysis.

BioData mining·2026

Same journal

Learning a distance for the clustering of patients with amyotrophic lateral sclerosis.

BioData mining·2026

Same journal

Multi-domain feature fusion with variational mode decomposition and hybrid LightGBM-Logistic Regression for multi-class seizure classification.

BioData mining·2026

Same journal

Large-scale transcriptomic data mining using explainable XGBoost and SHAP reveals shared biomarkers and molecular mechanisms between type-2 diabetes and triple-negative breast cancer for drug repurposing.

BioData mining·2026

Same journal

AVSeg-XAI: Deep learning framework for A/V segmentation with vascular features reveals retinal oculomics as biomarker for cardiovascular disease.

BioData mining·2026

Same journal

Navigating the uncharted: AI-driven advances in protein structure, dynamics, interactions and ligand interactions for understudied families.

BioData mining·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 14, 2025

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Seven quick tips for gene-focused computational pangenomic analysis.

Vincenzo Bonnici¹, Davide Chicco^2,3

¹Dipartimento di Scienze Matematiche Fisiche e Informatiche, Università di Parma, Parma, Italy. vincenzo.bonnici@unipr.it.

|September 3, 2024

Summary

This summary is machine-generated.

Computational pangenomics analyzes all genomes within a clade. This guide offers best practices for accurate bacterial pangenomics analysis, helping researchers avoid common errors and achieve reliable results.

Keywords:

Bioinformatics Computational biology Genomics Guidelines Pangenome Pangenomics Quick tips Recommendations

More Related Videos

Author Spotlight: Finding New Therapeutic Targets for Malignant Peripheral Nerve Sheath Tumor Through Genome-Scale shRNA Screens

Author Spotlight: Finding New Therapeutic Targets for Malignant Peripheral Nerve Sheath Tumor Through Genome-Scale shRNA Screens

Published on: August 25, 2023

Metagenomic Analysis of Silage

Metagenomic Analysis of Silage

Published on: January 13, 2017

Related Experiment Videos

Last Updated: Jun 14, 2025

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Author Spotlight: Finding New Therapeutic Targets for Malignant Peripheral Nerve Sheath Tumor Through Genome-Scale shRNA Screens

Author Spotlight: Finding New Therapeutic Targets for Malignant Peripheral Nerve Sheath Tumor Through Genome-Scale shRNA Screens

Published on: August 25, 2023

Metagenomic Analysis of Silage

Metagenomic Analysis of Silage

Published on: January 13, 2017

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Pangenomics studies the complete set of genes within a clade.
Modern bioinformatics enables computational pangenomics analysis.
Increased accessibility to pangenomics tools risks errors, especially for novices.

Purpose of the Study:

To provide guidance for accurate computational pangenomics.
To highlight common mistakes and best practices in bacterial pangenomics.
To improve the robustness and reliability of pangenomics studies.

Main Methods:

Focus on computational approaches for pangenomics data.
Emphasis on bacterial pangenomics analysis.
Description of common pitfalls and expert recommendations.

Main Results:

Identification of frequent errors in computational pangenomics.
Provision of actionable best practices for researchers.
Framework for enhancing the validity of pangenomics findings.

Conclusions:

Adherence to recommended practices ensures sound pangenomics analysis.
Expert guidance minimizes errors and enhances result reliability.
This work supports the advancement of computational pangenomics in bacteria.