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

DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
Real Time RT-PCR02:57

Real Time RT-PCR

Real-time reverse transcription-polymerase chain reaction, or Real-time RT-PCR, is an analytical tool used to determine the expression level of target genes. The method involves converting mRNA to complementary DNA with the help of an enzyme known as reverse transcriptase, followed by the PCR amplification of the cDNA. These two processes can be performed simultaneously in a single tube or separately as a two-step reaction.
The real-time quantification of the number of amplified products is...
Proteomics01:33

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...

You might also read

Related Articles

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

Sort by
Same author

Deciphering barley's stress response: metabolomic strategies and phenotypic implications under multiple abiotic stresses.

Metabolomics : Official journal of the Metabolomic Society·2026
Same author

Effect of developmental dynamics on WRKY expression in barley with varying phenologies and trichome micromorphologies.

BMC plant biology·2025
Same author

Dual omics comparison: how Agrobacterium tumefaciens and Agrobacterium rhizogenes modulate gene expression and metabolism in Hypericum perforatum L.

BMC genomics·2025
Same author

Statistical analysis of the measured strength parameters of the fresh main intracranial arteries.

Frontiers in bioengineering and biotechnology·2025
Same author

Vegetative to generative1 (Vgt1) is an enhancer affecting flowering time and jasmonate signaling in maize by promoting the expression of Zea mays Related to APETALA 2.7.

Plant physiology·2025
Same author

Reassessing data management in increasingly complex phenotypic datasets.

Trends in plant science·2025
Same journal

Assessment of lower incisor position and symphysis dimensions among different skeletal patterns in the Chhattisgarh population.

Bioinformation·2026
Same journal

Low T3 syndrome and short-term outcomes in patients with acute decompensated heart failure: A retrospective observational study.

Bioinformation·2026
Same journal

Cardiovascular risk prevention awareness and practices in type 2 diabetes: Linking HbA1c and lipid levels.

Bioinformation·2026
Same journal

Assessment of periodontal condition using basic periodontal examination scores: A retrospective clinical study.

Bioinformation·2026
Same journal

Comparative evaluation of osseointegration among different surface modification techniques in dental implants.

Bioinformation·2026
Same journal

Micro-osteoperforations' impact on orthodontic tooth movement rate: Split mouth research.

Bioinformation·2026
See all related articles

Related Experiment Video

Updated: Jun 28, 2026

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
07:30

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

12.6K

MASHt: Software for statistical analysis of transcriptomes' qualitative features in factorial experiments.

Maria Nuc1, Michal Stanoch1, Hanna Cwiek-Kupczynska1,2

  • 1Institute of Plant Genetics, Polish Academy of Sciences, Strzeszynska 34, 60-479 Poznan, Poland.

Bioinformation
|December 30, 2025
PubMed
Summary
This summary is machine-generated.

The MASHt toolkit analyzes qualitative transcriptome differences in multifactorial experiments using Mash distances and statistical analyses. This approach enhances sequencing data interpretation beyond gene expression levels.

Keywords:
Transcriptomicsfactorial experimentssequence variationsoftware tools

More Related Videos

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
09:58

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

3.1K
Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

3.9K

Related Experiment Videos

Last Updated: Jun 28, 2026

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
07:30

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

12.6K
Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
09:58

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

3.1K
Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

3.9K

Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Next-Generation Sequencing (NGS) data analysis often focuses on gene expression levels.
  • Qualitative differences between transcriptomes are frequently overlooked in multifactorial experiments.

Purpose of the Study:

  • To introduce the MASHt toolkit for analyzing qualitative variation in sequencing data.
  • To provide a method for exploring transcriptome differences beyond quantitative expression levels.

Main Methods:

  • Computing pairwise Mash distances between sequencing datasets.
  • Performing principal coordinate analysis on the distance matrix.
  • Applying univariate and multivariate analyses of variance to principal coordinates.
  • Supporting analysis on data subsets annotated by specific ontologies.

Main Results:

  • Demonstrated MASHt's utility in analyzing multifactorial sequencing data.
  • Successfully applied MASHt to study the impact of temperature on barley transcriptomes across different genotypes.

Conclusions:

  • MASHt offers a novel approach to assess qualitative transcriptome variation.
  • The toolkit facilitates deeper insights into biological responses in complex experimental designs.