KNeXT: a NetworkX-based topologically relevant KEGG parser.

Automating the recreation of gene and mixed gene-compound networks from Kyoto Encyclopedia of Genes and Genomes (KEGG) Markup Language (KGML) files is challenging because the data structure does not preserve the independent or loosely connected neighborhoods in which they were originally derived, referred to here as its topological environment. Identical accession numbers may overlap, causing neighborhoods to artificially collapse based on duplicated identifiers. This causes current parsers to create misleading or erroneous graphical representations when mixed gene networks are converted to gene-only networks. To overcome these challenges we created a python-based KEGG NetworkX Topological (KNeXT) parser that allows users to accurately recapitulate genetic networks and mixed networks from KGML map data. The software, archived as a python package index (PyPI) file to ensure broad application, is designed to ingest KGML files through built-in APIs and dynamically create high-fidelity topological representations. The utilization of NetworkX's framework to generate tab-separated files additionally ensures that KNeXT results may be imported into other graph frameworks and maintain programmatic access to the original x-y axis positions to each node in the KEGG pathway. KNeXT is a well-described Python 3 package that allows users to rapidly download and aggregate specific KGML files and recreate KEGG pathways based on a range of user-defined settings. KNeXT is platform-independent, distinctive, and it is not written on top of other Python parsers. Furthermore, KNeXT enables users to parse entire local folders or single files through command line scripts and convert the output into NCBI or UniProt IDs. KNeXT provides an ability for researchers to generate pathway visualizations while persevering the original context of a KEGG pathway. Source code is freely available at https://github.com/everest-castaneda/knext.

A method for creating complex real-world networks using ESRI Shapefiles.

A classic optimization problem with many real-world applications is optimal route search in graphs or networks. Graphical networks resembling real world networks are an important requirement for these studies. Python packages NetworkX and OSMnx are probably the most popular approaches in industry for creating and analyzing real world graphical networks using ESRI Shapefiles (Geospatial Vector Data). However, creating such a network is a complex and tedious process as these packages require the input data to be in a specific format. In this study,•We outline a flexible method that can be used to easily create graphical network representations in NetworkX or OSMnx using road network topology data stored in ESRI Shapefiles.•A detailed step-by-step process is outlined to successfully transform the ESRI Shapefile data into the compatible format for graph analysis libraries like OSMnx and NetworkX.•A data cleaning strategy is suggested to reduce resource consumption without distorting the actual structure of the graph.This method will allow researchers to efficiently generate graphical networks and validate their theories by evaluating their efficiencies using real-world network data of different sizes and topologies. This method could benefit, but is not limited to, research areas such as Advanced Transportation Systems (ATS), Graph Neural Networks (GNN), Multi-Objective Genetic Algorithms, to mention a few.

Protein-Protein Interaction Network Analysis Using NetworkX.

In recent years, extracting information from biological data has become a particularly valuable way of gaining knowledge. Molecular interaction networks provide a framework for visualizing cellular processes, but their complexity frequently makes their interpretation difficult. Proteins are one of the primary determinants of biological function. Indeed, most biological activities in the living cells are functionally regulated by protein-protein interactions (PPIs). Thus, studying protein interactions is critical for understanding their roles within the cell. Exploring the PPI networks can open new avenues for future experimental studies and offer interspecies predictions for effective interaction mapping. In this chapter we will demonstrate how to construct, visualize, and analyze a protein-protein interaction network using NetworkX.

Visualizing chemical space networks with RDKit and NetworkX.

This article demonstrates how to create Chemical Space Networks (CSNs) using a Python RDKit and NetworkX workflow. CSNs are a type of network visualization that depict compounds as nodes connected by edges, defined as a pairwise relationship such as a 2D fingerprint similarity value. A step by step approach is presented for creating two different CSNs in this manuscript, one based on RDKit 2D fingerprint Tanimoto similarity values, and another based on maximum common substructure similarity values. Several different CSN visualization features are included in the tutorial including methods to represent nodes with color based on bioactivity attribute value, edges with different line styles based on similarity value, as well as replacing the circle nodes with 2D structure depictions. Finally, some common network property and analysis calculations are presented including the clustering coefficient, degree assortativity, and modularity. All code is provided in the form of Jupyter Notebooks and is available on GitHub with a permissive BSD-3 open-source license: https://github.com/vfscalfani/CSN_tutorial.