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

36.4K
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...
36.4K
Cell Signaling in Plants01:25

Cell Signaling in Plants

5.7K
Plant cells communicate to coordinate their cycle of growth, flowering and fruiting, and activities in roots, shoots, and leaves in response to the changing environmental conditions. Plant signaling is distinct from animal signaling. Plants primarily utilize enzyme-linked receptors, whereas the largest class of cell-surface receptors in animals are G-protein coupled receptors (GPCRs). Unlike animals, receptor tyrosine kinases are rare in plants. Instead, plants have a diverse class of...
5.7K
Protein Networks02:26

Protein Networks

4.0K
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,...
4.0K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

127
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
127
Transgenic Plants02:50

Transgenic Plants

7.3K
Recombinant DNA technology called transgenesis is often used to add a foreign gene or remove a detrimental gene from an organism. Such genetically modified organisms are called transgenic organisms.
The first-ever transgenic plant was a tobacco plant developed in 1983 that showed resistance against the tobacco mosaic virus. Since then, many transgenic plants have been developed and commercialized for improving the agricultural, ornamental, and horticultural value of a crop plant. Transgenic...
7.3K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

5.8K
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...
5.8K

You might also read

Related Articles

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

Sort by
Same author

Sample Preparation for Quantitative Proteome and Phosphoproteome Profiling of Maize Tissues.

Cold Spring Harbor protocols·2026
Same author

Emerging Trends in Mass Spectrometry-Based Quantitative Proteome and Phosphoproteome Profiling in Maize.

Cold Spring Harbor protocols·2026
Same author

A four-dimensional spatial transcriptome atlas of barley caryopsis development and germination.

The Plant cell·2026
Same author

Salicylic acid modulates its catabolic enzymes via proteasomal degradation linked to SCF-associated proximity networks.

Nature communications·2026
Same author

Transcriptome assemblies for two drug-type cannabis chemotypes by long-read RNA sequencing.

The plant genome·2026
Same author

Multi-omic responses to acute exercise in abdominal subcutaneous adipose tissue of sedentary adults: findings from MoTrPAC.

bioRxiv : the preprint server for biology·2026

Related Experiment Video

Updated: Jul 17, 2025

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics
08:09

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Published on: June 17, 2012

19.7K

A Practical Guide to Inferring Multi-Omics Networks in Plant Systems.

Natalie M Clark1, Bhavna Hurgobin2,3, Dior R Kelley4

  • 1Proteomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA. nclark@broadinstitute.org.

Methods in Molecular Biology (Clifton, N.J.)
|September 8, 2023
PubMed
Summary

This study presents a new method for inferring gene regulatory networks in plants by integrating multiple omics data types, like transcriptomics and proteomics. This approach enhances understanding of complex biological processes and identifies novel regulators.

Keywords:
Gene regulatory networksMulti-omicsNetwork inferencePlant signalingProteomicsTranscriptomics

More Related Videos

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.2K
A Multi-Omics Extraction Method for the In-Depth Analysis of Synchronized Cultures of the Green Alga Chlamydomonas reinhardtii
07:51

A Multi-Omics Extraction Method for the In-Depth Analysis of Synchronized Cultures of the Green Alga Chlamydomonas reinhardtii

Published on: August 8, 2019

7.7K

Related Experiment Videos

Last Updated: Jul 17, 2025

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics
08:09

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Published on: June 17, 2012

19.7K
JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.2K
A Multi-Omics Extraction Method for the In-Depth Analysis of Synchronized Cultures of the Green Alga Chlamydomonas reinhardtii
07:51

A Multi-Omics Extraction Method for the In-Depth Analysis of Synchronized Cultures of the Green Alga Chlamydomonas reinhardtii

Published on: August 8, 2019

7.7K

Area of Science:

  • Plant Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Gene regulatory networks (GRNs) are crucial for understanding plant biological processes.
  • Previous GRN inference often relied on single omics data (e.g., transcriptomics).
  • Multi-omics integration, combining data like transcriptomics and (phospho)proteomics, offers a more comprehensive approach.

Purpose of the Study:

  • To describe a state-of-the-art method for integrating multi-omics data for GRN inference in plants.
  • To uncover novel regulators and signaling pathways by building comprehensive networks.
  • To provide a practical guide for analyzing multi-omics data in plant biology.

Main Methods:

  • Downloading and processing transcriptomics and (phospho)proteomics data.
  • Applying network inference algorithms to integrated multi-omics datasets.
  • Utilizing visualization and analysis tools for the resulting integrative networks.

Main Results:

  • Demonstration of a protocol for multi-omics GRN inference using plant hormone signaling data.
  • Identification of molecular connections and potential novel regulators within the integrated network.
  • Validation of the approach's utility for analyzing complex plant signaling pathways.

Conclusions:

  • Integrating multi-omics data significantly enhances GRN inference in plants.
  • The described protocol provides a valuable resource for bioinformaticians to analyze complex biological systems.
  • This approach facilitates a deeper understanding of plant molecular mechanisms and signaling.