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

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

488
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
488
Causality in Epidemiology01:21

Causality in Epidemiology

1.6K
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
1.6K
Genomics02:02

Genomics

40.6K
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...
40.6K
Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.5K
Network Covalent Solids02:18

Network Covalent Solids

16.2K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.2K
Theory of Attribution I: Correspondent Inference Theory01:15

Theory of Attribution I: Correspondent Inference Theory

561
Correspondent inference theory, proposed by Jones and Davis in 1965, seeks to explain how individuals infer stable personality traits from observed behaviors. It suggests that people attribute actions to underlying dispositions rather than external circumstances, particularly when the behavior appears intentional and socially significant.Voluntary Behavior and Dispositional AttributionAccording to this theory, individuals are more likely to attribute behavior to personal traits when it appears...
561

You might also read

Related Articles

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

Sort by
Same author

Probing the Hall anomaly and electronic structure in kagome metal RbV<sub>3</sub>Sb<sub>5</sub> under hydrostatic pressure.

Science and technology of advanced materials·2026
Same author

High-throughput characterization of local structural imperfections in freestanding oxide membranes by lock-in thermography.

Science bulletin·2026
Same author

Strain-Preserving Transfer of Freestanding Oxide Membranes for Tunable Magnetic Anisotropy.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Room-Temperature Skyrmionic Synapse in 2D Ferromagnet Fe<sub>3</sub>GaTe<sub>2</sub> Operating via Collective Spin Texture Transformation.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

2D NMR assessment of structural consistency and functional relationships in monoclonal antibodies.

mAbs·2026
Same author

Recommendations and considerations for hydroxyl radical protein footprinting-mass spectrometry.

Nature methods·2026
Same journal

Isolation of Mesenchymal Stem Cell-Derived Extracellular Vesicles.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Modeling Melanoma Immune Surveillance by CAR-T Cells in Human Skin Organoids.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Stepwise Optimization of a Matrigel-Based In Vitro Angiogenesis Assay for Reproducible and Quantifiable 2D-Tube Formation Using HUVECs.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Quantifying Mechanical Properties of Fresh Ovarian Tissue with Optical Brillouin Microscopy.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

3D Chromatin Architecture During Early Development: New Methods and New Findings.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Metabolic Plasticity in Embryogenesis Throughout the Lens of NAD<sup></sup>.

Methods in molecular biology (Clifton, N.J.)·2026
See all related articles

Related Experiment Video

Updated: Feb 1, 2026

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project
10:19

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Published on: April 8, 2017

18.1K

Whole-Transcriptome Causal Network Inference with Genomic and Transcriptomic Data.

Lingfei Wang1, Tom Michoel2,3

  • 1Division of Genetics and Genomics, The Roslin Institute, The University of Edinburgh, Midlothian, Scotland, UK. lingfei@broadinstitute.org.

Methods in Molecular Biology (Clifton, N.J.)
|December 15, 2018
PubMed
Summary
This summary is machine-generated.

Causal gene network reconstruction using genetic variations can identify gene regulators. This study demonstrates the Findr program for whole-transcriptome analysis, revealing key gene hubs.

Keywords:
Causal gene networkCausal inferenceGenome–transcriptome variationWhole-transcriptome network

More Related Videos

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

4.8K
Obtaining High-Quality Transcriptome Data from Cereal Seeds by a Modified Method for Gene Expression Profiling
07:18

Obtaining High-Quality Transcriptome Data from Cereal Seeds by a Modified Method for Gene Expression Profiling

Published on: May 21, 2020

7.9K

Related Experiment Videos

Last Updated: Feb 1, 2026

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project
10:19

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Published on: April 8, 2017

18.1K
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

4.8K
Obtaining High-Quality Transcriptome Data from Cereal Seeds by a Modified Method for Gene Expression Profiling
07:18

Obtaining High-Quality Transcriptome Data from Cereal Seeds by a Modified Method for Gene Expression Profiling

Published on: May 21, 2020

7.9K

Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • Causal gene network reconstruction is crucial for understanding gene regulation and reducing false positives.
  • Advances in computational methods now enable whole-transcriptome causal network inference on personal computers.

Purpose of the Study:

  • To demonstrate the application of a fast and accurate causal network inference technique.
  • To identify major hub genes within a reconstructed gene network.

Main Methods:

  • Utilized the Findr program for causal network inference.
  • Applied the technique to a subset of 3000 genes from the Geuvadis dataset.
  • Integrated genetic variations for network reconstruction.

Main Results:

  • Successfully reconstructed a causal gene network.
  • Identified several major hub genes within the network.
  • Demonstrated the feasibility of whole-transcriptome analysis on a personal computer.

Conclusions:

  • The Findr program is effective for causal gene network reconstruction.
  • The identified hub genes represent important regulatory elements.
  • This approach facilitates the study of gene regulatory mechanisms.