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OmicsNet 2.0:一个基于网络的多组学整合和网络可视化分析平台。

OmicsNet 2.0: a web-based platform for multi-omics integration and network visual analytics.

机构信息

Institute of Parasitology, McGill University, Quebec, Canada.

Department of Microbiology and Immunology, McGill University, Quebec, Canada.

出版信息

Nucleic Acids Res. 2022 Jul 5;50(W1):W527-W533. doi: 10.1093/nar/gkac376.

Abstract

Researchers are increasingly seeking to interpret molecular data within a multi-omics context to gain a more comprehensive picture of their study system. OmicsNet (www.omicsnet.ca) is a web-based tool developed to allow users to easily build, visualize, and analyze multi-omics networks to study rich relationships among lists of 'omics features of interest. Three major improvements have been introduced in OmicsNet 2.0, which include: (i) enhanced network visual analytics with eleven 2D graph layout options and a novel 3D module layout; (ii) support for three new 'omics types: single nucleotide polymorphism (SNP) list from genetic variation studies; taxon list from microbiome profiling studies, as well as liquid chromatography-mass spectrometry (LC-MS) peaks from untargeted metabolomics; and (iii) measures to improve research reproducibility by coupling R command history with the release of the companion OmicsNetR package, and generation of persistent links to share interactive network views. We performed a case study using the multi-omics data obtained from a recent large-scale investigation on inflammatory bowel disease (IBD) and demonstrated that OmicsNet was able to quickly create meaningful multi-omics context to facilitate hypothesis generation and mechanistic insights.

摘要

研究人员越来越希望在多组学背景下解释分子数据,以更全面地了解他们的研究系统。OmicsNet(www.omicsnet.ca)是一个基于网络的工具,旨在使用户能够轻松构建、可视化和分析多组学网络,以研究感兴趣的“组学特征列表”之间的丰富关系。OmicsNet 2.0 引入了三个主要改进,包括:(i)增强的网络可视化分析,具有 11 种 2D 图形布局选项和新颖的 3D 模块布局;(ii)支持三种新的“组学”类型:来自遗传变异研究的单核苷酸多态性 (SNP) 列表;来自微生物组分析研究的分类单元列表,以及来自非靶向代谢组学的液相色谱-质谱 (LC-MS) 峰;以及(iii)通过将 R 命令历史记录与配套的 OmicsNetR 包的发布以及生成持久链接以共享交互式网络视图,来提高研究可重复性的措施。我们使用最近大规模炎症性肠病 (IBD) 研究中获得的多组学数据进行了案例研究,表明 OmicsNet 能够快速创建有意义的多组学背景,以促进假设生成和机制见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b20d/9252810/508711d9a612/gkac376figgra1.jpg

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