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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 R eactome , a free, comprehensive, and widely used repository for biochemical networks and pathways. In this paper, we: (1) introduce a tool S t ARG ate -X ( STatistical Analysis of the  R eactome   multi-GrAph Through   nEtworkX ) to carry out an automated analysis of the connectivity properties of R eactome  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 R eactome  network, in terms of centrality measures, with respect to in- and out-degree. As an example of usage of S t ARG ate -X, we provide a detailed automated analysis of the R eactome  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 R eactome  through easy-to-use APIs ( Application Programming Interfaces ).

S T ARG ATE -X: a Python package for statistical analysis on the REACTOME network