iTraNet：一个基于网络的用于整合跨组学网络可视化与分析的平台。

iTraNet: a web-based platform for integrated trans-omics network visualization and analysis.

作者信息

Sugimoto Hikaru, Morita Keigo, Li Dongzi, Bai Yunfan, Mattanovich Matthias, Kuroda Shinya

机构信息

Department of Biochemistry and Molecular Biology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan.

Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.

出版信息

Bioinform Adv. 2024 Sep 30;4(1):vbae141. doi: 10.1093/bioadv/vbae141. eCollection 2024.

DOI:10.1093/bioadv/vbae141

PMID:39440006

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11493990/

Abstract

MOTIVATION

Visualization and analysis of biological networks play crucial roles in understanding living systems. Biological networks include diverse types, from gene regulatory networks and protein-protein interactions to metabolic networks. Metabolic networks include substrates, products, and enzymes, which are regulated by allosteric mechanisms and gene expression. However, the analysis of these diverse omics types is challenging due to the diversity of databases and the complexity of network analysis.

RESULTS

We developed iTraNet, a web application that visualizes and analyses trans-omics networks involving four types of networks: gene regulatory networks, protein-protein interactions, metabolic networks, and metabolite exchange networks. Using iTraNet, we found that in wild-type mice, hub molecules within the network tended to respond to glucose administration, whereas in mice, this tendency disappeared. With its ability to facilitate network analysis, we anticipate that iTraNet will help researchers gain insights into living systems.

AVAILABILITY AND IMPLEMENTATION

iTraNet is available at https://itranet.streamlit.app/.

摘要

动机

生物网络的可视化和分析在理解生命系统中起着至关重要的作用。生物网络包括多种类型，从基因调控网络、蛋白质-蛋白质相互作用到代谢网络。代谢网络包括底物、产物和酶，它们受变构机制和基因表达调控。然而，由于数据库的多样性和网络分析的复杂性，对这些不同组学类型的分析具有挑战性。

结果

我们开发了iTraNet，这是一个网络应用程序，用于可视化和分析涉及四种网络类型的跨组学网络：基因调控网络、蛋白质-蛋白质相互作用、代谢网络和代谢物交换网络。使用iTraNet，我们发现，在野生型小鼠中，网络内的枢纽分子倾向于对葡萄糖给药做出反应，而在[此处原文缺失相关小鼠类型信息]小鼠中，这种倾向消失了。凭借其促进网络分析的能力，我们预计iTraNet将帮助研究人员深入了解生命系统。