Paré Louis, Bordron Philippe, David Laurent, Mahé Maxime, Bihouée Audrey, Eveillard Damien
Nantes Université, Centrale Nantes, CNRS, LS2N, Nantes 44322, France.
Université de Toulouse, INRAE, BioinfOmics, GenoToul Bioinformatics Facility, Castanet-Tolosan 31320, France.
Bioinformatics. 2025 Aug 2;41(8). doi: 10.1093/bioinformatics/btaf448.
Transcriptomic analysis is a key tool for exploring gene expression, but the complexity of biological systems often limits its insights. In particular, the lack of intermodal or multi-layered analysis hinders the ability to fully capture key cellular functions such as metabolism from transcriptomic data alone. Here, we introduce a novel approach that informs transcriptomic data analysis with metabolic network modeling to address this. Unlike traditional methods, HUman MEtabolism Specific Signature (HUMESS) uses genome-scale metabolic modeling and flux analysis to highlight reactions and involved genes based on their metabolic significance, offering a deeper understanding of transcriptomic data. Our computational pipeline, supported by a user-friendly Rshiny application, enhances gene expression analysis by uncovering metabolic phenotypic signatures.
HUMESS is open source and available under GitLab https://gitlab.univ-nantes.fr/bird_pipeline_registry/humess with the complete documentation available at https://gitlab.univ-nantes.fr/bird_pipeline_registry/humess/-/wikis/Home. A zenodo archive is also available at the following DOI: https://doi.org/10.5281/zenodo.15487717. An RShiny application has been developed to facilitate the exploration and analysis of HUMESS's results. The app is available online at the following address: https://shiny-bird.univ-nantes.fr/app/shinymess but can also be installed locally, available under GitLab https://gitlab.univ-nantes.fr/pare-l/shinymess.
转录组分析是探索基因表达的关键工具，但生物系统的复杂性常常限制了其所能提供的见解。特别是，缺乏多模态或多层次分析阻碍了仅从转录组数据中全面捕捉关键细胞功能（如代谢）的能力。在此，我们引入一种新方法，即通过代谢网络建模为转录组数据分析提供信息，以解决这一问题。与传统方法不同，人类代谢特异性特征（HUMESS）使用基因组规模的代谢建模和通量分析，根据反应及其相关基因的代谢意义来突出显示它们，从而更深入地理解转录组数据。我们的计算流程由一个用户友好的Rshiny应用程序支持，通过揭示代谢表型特征来增强基因表达分析。
HUMESS是开源的，可在GitLab上获取，链接为https://gitlab.univ-nantes.fr/bird_pipeline_registry/humess ，完整文档可在https://gitlab.univ-nantes.fr/bird_pipeline_registry/humess/-/wikis/Home查看。还可通过以下DOI获取zenodo存档：https://doi.org/10.5281/zenodo.15487717。已开发了一个Rshiny应用程序，以方便对HUMESS的结果进行探索和分析。该应用程序可在以下网址在线获取：https://shiny-bird.univ-nantes.fr/app/shinymess ，但也可在本地安装，可在GitLab上获取，链接为https://gitlab.univ-nantes.fr/pare-l/shinymess。
