STARGATE-X: a Python package for statistical analysis on the REACTOME network.

IF 1.5 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Journal of Integrative Bioinformatics Pub Date : 2023-09-21 eCollection Date: 2023-09-01 DOI:10.1515/jib-2022-0029
Andrea Marino, Blerina Sinaimeri, Enrico Tronci, Tiziana Calamoneri
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Abstract

Many important aspects of biological knowledge at the molecular level can be represented by pathways. Through their analysis, we gain mechanistic insights and interpret lists of interesting genes from experiments (usually omics and functional genomic experiments). As a result, pathways play a central role in the development of bioinformatics methods and tools for computing predictions from known molecular-level mechanisms. Qualitative as well as quantitative knowledge about pathways can be effectively represented through biochemical networks linking the biochemical reactions and the compounds (e.g., proteins) occurring in the considered pathways. So, repositories providing biochemical networks for known pathways play a central role in bioinformatics and in systems biology. Here we focus on Reactome, a free, comprehensive, and widely used repository for biochemical networks and pathways. In this paper, we: (1) introduce a tool StARGate-X (STatistical Analysis of the Reactome multi-GrAph Through nEtworkX) to carry out an automated analysis of the connectivity properties of Reactome biochemical reaction network and of its biological hierarchy (i.e., cell compartments, namely, the closed parts within the cytosol, usually surrounded by a membrane); the code is freely available at https://github.com/marinoandrea/stargate-x; (2) show the effectiveness of our tool by providing an analysis of the Reactome network, in terms of centrality measures, with respect to in- and out-degree. As an example of usage of StARGate-X, we provide a detailed automated analysis of the Reactome network, in terms of centrality measures. We focus both on the subgraphs induced by single compartments and on the graph whose nodes are the strongly connected components. To the best of our knowledge, this is the first freely available tool that enables automatic analysis of the large biochemical network within Reactome through easy-to-use APIs (Application Programming Interfaces).

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STARGATE-X:一个用于REACTOME网络统计分析的Python包。
分子水平上生物学知识的许多重要方面可以用途径来表示。通过他们的分析,我们获得了机制上的见解,并解释了实验(通常是组学和功能基因组实验)中有趣的基因列表。因此,通路在生物信息学方法和工具的发展中发挥着核心作用,这些方法和工具用于从已知的分子水平机制计算预测。关于途径的定性和定量知识可以通过连接所考虑的途径中发生的生物化学反应和化合物(例如蛋白质)的生物化学网络来有效地表示。因此,为已知途径提供生物化学网络的存储库在生物信息学和系统生物学中发挥着核心作用。在这里,我们关注Reactome,一个免费、全面、广泛使用的生化网络和途径库。在本文中,我们:(1)介绍了一种工具StARGate-X(通过nEtorkX对反应体多GrAph的统计分析),用于对反应体生物化学反应网络的连接特性及其生物层次(即细胞隔室,即胞质溶胶内的封闭部分,通常被膜包围)进行自动分析;该代码可在https://github.com/marinoandrea/stargate-x;(2) 通过对Reactome网络的中心性度量以及输入和输出程度进行分析,展示了我们工具的有效性。作为StARGate-X使用的一个例子,我们提供了Reactome网络在中心性测量方面的详细自动化分析。我们既关注由单格诱导的子图,也关注其节点是强连通分量的图。据我们所知,这是第一个免费提供的工具,可以通过易于使用的API(应用程序编程接口)自动分析Reactome内的大型生化网络。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Integrative Bioinformatics
Journal of Integrative Bioinformatics Medicine-Medicine (all)
CiteScore
3.10
自引率
5.30%
发文量
27
审稿时长
12 weeks
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