Multi-omics experimental approaches are becoming common practice in biological and medical sciences underlining the need to design new integrative techniques and applications to enable the multi-scale characterization of biological systems. The integrative analysis of heterogeneous datasets generally allows to acquire additional insights and generate novel hypotheses about a given biological system. However, it can become challenging given the often-large size of omics datasets and the diversity of existing techniques. Moreover, visualization tools for interpretation are usually non-accessible to biologists without programming skills. Here, we present MiBiOmics, a web-based and standalone application that facilitates multi-omics data visualization, exploration, integration, and analysis by providing easy access to dedicated and interactive protocols. It implements classical ordination techniques and the inference of omics-based (multilayer) networks to mine complex biological systems, and identify robust biomarkers linked to specific contextual parameters or biological states. MiBiOmics provides easy-access to exploratory ordination techniques and to a network-based approach for integrative multi-omics analyses through an intuitive and interactive interface. MiBiOmics is currently available as a Shiny app at https://shiny-bird.univ-nantes.fr/app/Mibiomics and as a standalone application at https://gitlab.univ-nantes.fr/combi-ls2n/mibiomics .

中文翻译：

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MiBiOmics：用于多组学数据探索和集成的交互式Web应用程序

多组学实验方法正在成为生物学和医学科学中的普遍实践，强调需要设计新的集成技术和应用程序以实现生物系统的多尺度表征。异类数据集的综合分析通常可以获取更多的见解并生成有关给定生物系统的新颖假设。但是，由于组学数据集的规模通常很大且现有技术的多样性，这可能会变得充满挑战。此外，没有编程技能的生物学家通常无法获得用于解释的可视化工具。在这里，我们介绍MiBiOmics，这是一个基于网络的独立应用程序，可促进多组学数据的可视化，探索，集成，通过轻松访问专用和交互式协议进行分析。它采用经典的排序技术和基于组学（多层）网络的推理来挖掘复杂的生物系统，并识别与特定上下文参数或生物学状态相关的强大生物标记。MiBiOmics通过直观的交互式界面，可轻松访问探索性排序技术以及基于网络的方法，以进行多组学综合分析。MiBiOmics当前可通过https://shiny-bird.univ-nantes.fr/app/Mibiomics上的Shiny应用程序获得，也可作为https://gitlab.univ-nantes.fr/combi-ls2n/mibiomics上的独立应用程序获得。并确定与特定背景参数或生物学状态相关的健壮生物标记。MiBiOmics通过直观的交互式界面，可以轻松访问探索性排序技术以及基于网络的方法，以进行集成多组学分析。MiBiOmics当前可通过https://shiny-bird.univ-nantes.fr/app/Mibiomics上的Shiny应用程序获得，也可作为https://gitlab.univ-nantes.fr/combi-ls2n/mibiomics上的独立应用程序获得。并确定与特定背景参数或生物学状态相关的健壮生物标记。MiBiOmics通过直观的交互式界面，可以轻松访问探索性排序技术以及基于网络的方法，以进行集成多组学分析。MiBiOmics当前可通过https://shiny-bird.univ-nantes.fr/app/Mibiomics上的Shiny应用程序获得，也可作为https://gitlab.univ-nantes.fr/combi-ls2n/mibiomics上的独立应用程序获得。