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

• Published in: BMC bioinformatics Vol. 26; no. 1; pp. 160 - 7
• Main Authors: Colange, Maximilien, Appé, Guillaume, Meunier, Léa, Weill, Solène, Nordor, Akpéli, Behdenna, Abdelkader
• Format: Journal Article
• Language: English
• Published: London BioMed Central 23.06.2025; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Artificial intelligence; Automation; Bioinformatics; Biomedical and Life Sciences; Cancer; Computational Biology - methods; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Data mining; Datasets; Differential gene expression analysis; DNA microarrays; Gene expression; Gene Expression Profiling - methods; Generalized linear models; Genetic research; Life Sciences; Methods; Microarray; Microarrays; Multiomics; Open source; Open source software; Ovaries; Phenotypes; Pipelines; Programming Languages; Public software; Python; Python (Programming language); Reproducibility; RNA sequencing; RNA-Seq; Software; Transcriptome; Transcriptomics; France; Differential gene expression analysis; Microarray; Open source; RNA-Seq; Python
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-025-06180-7

Background Differential gene expression analysis is a prominent technique for the analysis of biomolecular data to identify genetic features associated with phenotypes. Limma —for microarray data –, and edgeR and DESeq2 —for RNA-Seq data–, are the most widely used tools for differential gene expression analysis of bulk transcriptomic data. Results We present the differential expression features of InMoose, a Python implementation of R tools limma , edgeR , and DESeq2 . We experimentally show that InMoose stands as a drop-in replacement for those tools, with nearly identical results. This ensures reproducibility when interfacing both languages in bioinformatic pipelines. InMoose is an open source software released under the GPL3 license, available at www.github.com/epigenelabs/inmoose and https://inmoose.readthedocs.io . Conclusions We present a new Python implementation of state-of-the-art tools limma , edgeR , and DESeq2 , to perform differential gene expression analysis of bulk transcriptomic data. This new implementation exhibits results nearly identical to the original tools, improving interoperability and reproducibility between Python and R bioinformatics pipelines.