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

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...
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.
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...
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-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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.

You might also read

Related Articles

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

Sort by
Same author

Synergistic activity of rifampicin and polymyxin B against intracellular Gram-negative ESKAPE pathogens involves bacterial membrane alterations and enhanced oxidative damages.

Antimicrobial agents and chemotherapy·2025
Same author

Systematic mapping of antibiotic cross-resistance and collateral sensitivity with chemical genetics.

Nature microbiology·2024
Same author

ChemGAPP: a tool for chemical genomics analysis and phenotypic profiling.

Bioinformatics (Oxford, England)·2023
Same author

Bacterial retrons encode phage-defending tripartite toxin-antitoxin systems.

Nature·2022
Same author

Structure-Function Characterization of the Conserved Regulatory Mechanism of the Escherichia coli M48 Metalloprotease BepA.

Journal of bacteriology·2020
Same author

Real-Time 3-D Imaging Using an Air-Coupled Ultrasonic Phased-Array.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control·2020
Same journal

Bayesian inference for biomarker discovery in proteomics: an analytic solution.

EURASIP journal on bioinformatics & systems biology·2017
Same journal

Review of stochastic hybrid systems with applications in biological systems modeling and analysis.

EURASIP journal on bioinformatics & systems biology·2017
Same journal

Using multi-step proposal distribution for improved MCMC convergence in Bayesian network structure learning.

EURASIP journal on bioinformatics & systems biology·2017
Same journal

On biometric systems: electrocardiogram Gaussianity and data synthesis.

EURASIP journal on bioinformatics & systems biology·2017
Same journal

Biomedical informatics with optimization and machine learning.

EURASIP journal on bioinformatics & systems biology·2017
Same journal

Autism spectrum disorder detection from semi-structured and unstructured medical data.

EURASIP journal on bioinformatics & systems biology·2017
See all related articles

Related Experiment Video

Updated: Jun 29, 2026

Large-Scale Screens of Metagenomic Libraries
16:05

Large-Scale Screens of Metagenomic Libraries

Published on: May 28, 2007

Learning directed acyclic graphs from large-scale genomics data.

Fabio Nikolay1, Marius Pesavento2, George Kritikos3

  • 1Communication Systems Group, TU Darmstadt, Merckstr. 25, Darmstadt, Germany. nikolay@nt.tu-darmstadt.de.

EURASIP Journal on Bioinformatics & Systems Biology
|September 22, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces GENIE, a novel method for mapping gene interactions from noisy data. GENIE accurately identifies genetic dependencies and network topology, outperforming existing techniques.

Keywords:
Big dataDiscrete optimizationGenetic interaction analysisGraph learningLarge-scale gene networksMultiple hypothesis test

More Related Videos

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

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

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: Jun 29, 2026

Large-Scale Screens of Metagenomic Libraries
16:05

Large-Scale Screens of Metagenomic Libraries

Published on: May 28, 2007

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

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

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

Published on: October 13, 2023

Area of Science:

  • Computational Biology
  • Systems Biology
  • Genetics

Background:

  • Understanding gene interactions is crucial for deciphering complex biological processes.
  • Existing methods struggle with noisy genetic data and accurately reconstructing gene networks.
  • Double-knockout (DK) data offers insights but requires sophisticated analysis.

Purpose of the Study:

  • To develop a robust computational method for learning genetic interaction maps from noisy DK data.
  • To accurately detect and classify gene interactions and infer the underlying directed acyclic graph (DAG) topology.
  • To enhance detection performance by integrating genetic interaction profile (GI-profile) data.

Main Methods:

  • Proposed a linear integer optimization program, Genetic-Interactions-Detector (GENIE), for identifying gene dependencies and DAG topology.
  • Extended GENIE by incorporating GI-profile data (GI-GENIE) to improve detection accuracy.
  • Developed a sequential scalability technique for analyzing large gene sets and ensuring statistically significant results.

Main Results:

  • GENIE accurately identifies complex biological dependencies and computes optimal DAG topologies matching DK measurements.
  • GI-GENIE demonstrated superior performance in detecting gene interactions compared to standard methods.
  • The sequential scalability technique provided statistically significant results for real-world gene expression data.

Conclusions:

  • GENIE and GI-GENIE significantly outperform conventional techniques for genetic interaction mapping.
  • The proposed methods offer accurate and scalable solutions for reconstructing gene regulatory networks from noisy biological data.
  • The sequential scalability technique enables robust analysis of large-scale genetic interaction datasets.