Bridging the gap between R and Python in bulk transcriptomic data analysis with InMoose
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Updated: Aug 6, 2026

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
Published on: May 28, 2021
Maximilien Colange1, Guillaume Appé2, Léa Meunier2
1Epigene Labs, Paris, France. maximilien@epigenelabs.com.
Abstract:
We introduce InMoose, an open-source Python environment aimed at omic data analysis. We illustrate its capabilities for bulk transcriptomic data analysis. Due to its wide adoption, Python has grown as a de facto standard in fields increasingly important for bioinformatic pipelines, such as data science, machine learning, or artificial intelligence (AI). As a general-purpose language, Python is also recognized for its versatility and scalability. InMoose aims at bringing state-of-the-art tools, historically written in R, to the Python ecosystem. InMoose focuses on providing drop-in replacements for R tools, to ensure consistency and reproducibility between R-based and Python-based pipelines. The first development phase has focused on bulk transcriptomic data, with current capabilities encompassing data simulation, batch effect correction, and differential analysis and meta-analysis.
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