面向网络的多组学数据分析和集成管道的web服务器。

IF 5.4
Tiberiu Totu, Rafael Riudavets Puig, Lukas Jonathan Häuser, Mattia Tomasoni, Hella Anna Bolck, Marija Buljan
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引用次数: 0

摘要

摘要:组学分析已被证明在确定生物表型和基础医疗条件的关键特征的公正和全面鉴定方面有很大的用处。虽然每个组学图谱有助于表征与所研究表型相关的特定分子成分,但它们的联合评估可以更深入地了解生物系统的整体机制功能。在这里,我们介绍了一种方法,从每个组学剖面获得的代表性特征(例如差异表达元素)开始,我们构建和分析联合相互作用网络。由此产生的网络依赖于生物实体之间相互作用的现有知识。我们使用这些图来识别和描述中心元素,这些元素连接了所研究表型的多个实体特征,我们利用MONET网络分解工具来突出功能连接的网络模块。为了使这种方法得到广泛的应用,我们开发了NOODAI软件平台,通过用户友好的界面实现整合组学分析。分析结果既以原始输出表的形式呈现,也以信息摘要图和书面报告的形式呈现。由于MONET工具能够使用具有强大性能的算法来识别疾病相关模块,因此NOODAI软件平台对于分析临床多组学数据集具有很高的价值。可用性和实施:nodai可在https://omics-oracle.com免费访问。源代码在GPL3下可在:https://github.com/TotuTiberiu/NOODAI获得,DOI: 10.5281/zenodo.17203984。补充信息:补充数据可在生物信息学在线获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
NOODAI: A webserver for network-oriented multi-omics data analysis and integration pipeline.

Summary: Omics profiling has proven of great use for unbiased and comprehensive identification of key features that define biological phenotypes and underlie medical conditions. While each omics profile assists characterization of specific molecular components relevant for the studied phenotype, their joint evaluation can offer deeper insights into the overall mechanistic functioning of biological systems. Here, we introduce an approach where, starting from representative traits (e.g., differentially expressed elements) obtained for each omics profile, we construct and analyze joint interaction networks. The resulting networks rely on the existing knowledge of confident interactions among biological entities. We use these maps to identify and describe central elements, which connect multiple entities characteristic of the studied phenotypes and we leverage MONET network decomposition tool in order to highlight functionally connected network modules. In order to enable broad usage of this approach, we developed the NOODAI software platform, which enables integrative omics analysis through a user-friendly interface. The analysis outcomes are presented both as raw output tables as well as informative summary plots and written reports. Since the MONET tool enables the use of algorithms with strong performance in identifying disease-relevant modules, NOODAI software platform can be of a high value for analyzing clinical multi-omics datasets.

Availability and implementation: NOODAI is freely accessible at https://omics-oracle.com. Source code is available under GPL3 at: https://github.com/TotuTiberiu/NOODAI with the DOI: 10.5281/zenodo.17203984.

Supplementary information: Supplementary data are available at Bioinformatics online.

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