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MiBiOmics:一个交互式的多组学数据探索和整合的网络应用程序。

MiBiOmics: an interactive web application for multi-omics data exploration and integration.

机构信息

INSERM, TENS, Université de Nantes, Nantes, France.

CHU Nantes, Inserm, CNRS, SFR Santé, Inserm UMS016, CNRS UMS 3556, Université de Nantes, 44000, Nantes, France.

出版信息

BMC Bioinformatics. 2021 Jan 6;22(1):6. doi: 10.1186/s12859-020-03921-8.

DOI:10.1186/s12859-020-03921-8
PMID:33407076
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7789220/
Abstract

BACKGROUND

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.

RESULTS

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.

CONCLUSIONS

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 .

摘要

背景

多组学实验方法在生物和医学科学中已变得普遍,这凸显了设计新的整合技术和应用程序以实现生物系统多尺度特征描述的必要性。异质数据集的综合分析通常可以获得关于给定生物系统的额外见解并生成新的假设。然而,由于组学数据集通常较大且现有技术多样化,这可能具有挑战性。此外,没有编程技能的生物学家通常无法访问用于解释的可视化工具。

结果

在这里,我们介绍了 MiBiOmics,这是一个基于网络的独立应用程序,通过提供对专用和交互式协议的轻松访问,促进多组学数据的可视化、探索、集成和分析。它实现了经典的排序技术和基于组学的(多层)网络推断,以挖掘复杂的生物系统,并识别与特定上下文参数或生物状态相关的稳健生物标志物。

结论

MiBiOmics 通过直观和交互式界面提供了对探索性排序技术和基于网络的综合多组学分析方法的便捷访问。MiBiOmics 目前可在 https://shiny-bird.univ-nantes.fr/app/Mibiomics 作为 Shiny 应用程序使用,也可在 https://gitlab.univ-nantes.fr/combi-ls2n/mibiomics 作为独立应用程序使用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba90/7789220/9d1c154b9321/12859_2020_3921_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba90/7789220/6116be6f9313/12859_2020_3921_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba90/7789220/0327acbdb3e6/12859_2020_3921_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba90/7789220/50a0128c7113/12859_2020_3921_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba90/7789220/b59741767a2b/12859_2020_3921_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba90/7789220/9d1c154b9321/12859_2020_3921_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba90/7789220/6116be6f9313/12859_2020_3921_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba90/7789220/0327acbdb3e6/12859_2020_3921_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba90/7789220/50a0128c7113/12859_2020_3921_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba90/7789220/b59741767a2b/12859_2020_3921_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba90/7789220/9d1c154b9321/12859_2020_3921_Fig5_HTML.jpg

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