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

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

18.0K
Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
18.0K
Ribosome Profiling02:24

Ribosome Profiling

3.6K
Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
3.6K
DNA Microarrays02:34

DNA Microarrays

18.7K
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...
18.7K

You might also read

Related Articles

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

Sort by
Same author

mTOR controls ependymal cell differentiation by targeting the alternative cell cycle and centrosomal proteins.

EMBO reports·2025
Same author

Dynamics of influenza transmission in vampire bats revealed by longitudinal monitoring and a large-scale anthropogenic perturbation.

Science advances·2025
Same author

pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods.

BMC bioinformatics·2023
Same author

Choroid plexuses carry nodal-like cilia that undergo axoneme regression from early adult stage.

Developmental cell·2023
Same author

We are what we eat, plus some per mill: Using stable isotopes to estimate diet composition in <i>Gyps</i> vultures over space and time.

Ecology and evolution·2022
Same author

A Minimal yet Flexible Likelihood Framework to Assess Correlated Evolution.

Systematic biology·2021

Related Experiment Video

Updated: Sep 18, 2025

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

4.4K

Differential expression analysis with inmoose, the integrated multi-omic open-source environment in Python.

Maximilien Colange1, Guillaume Appé2, Léa Meunier2

  • 1Epigene Labs, Paris, France. maximilien@epigenelabs.com.

BMC Bioinformatics
|June 23, 2025
PubMed
Summary
This summary is machine-generated.

InMoose is a new Python tool that provides differential gene expression analysis for bulk transcriptomic data. It offers nearly identical results to established R tools, enhancing bioinformatics pipeline reproducibility.

Keywords:
Differential gene expression analysisMicroarrayOpen sourcePythonRNA-Seq

More Related Videos

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

3.7K
Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

38.6K

Related Experiment Videos

Last Updated: Sep 18, 2025

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

4.4K
Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

3.7K
Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

38.6K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Differential gene expression analysis is crucial for identifying genetic features linked to phenotypes.
  • Limma, edgeR, and DESeq2 are widely used R tools for analyzing bulk transcriptomic data (microarray and RNA-Seq).

Purpose of the Study:

  • To introduce InMoose, a Python implementation of the R tools Limma, edgeR, and DESeq2.
  • To ensure seamless integration and reproducibility in bioinformatics workflows involving both R and Python.

Main Methods:

  • Developed InMoose as a Python-based alternative to established R packages for differential gene expression.
  • Validated InMoose's performance against Limma, edgeR, and DESeq2.

Main Results:

  • InMoose provides differential expression analysis results that are nearly identical to the original R tools.
  • The software functions as a direct, drop-in replacement, maintaining analytical consistency.

Conclusions:

  • InMoose enhances interoperability between R and Python in bioinformatics.
  • This Python implementation improves reproducibility for differential gene expression analysis of bulk transcriptomic data.