Graphery: Interactive Tutorials for Biological Network Algorithms

TL;DR

Graphery is an interactive web-based platform that provides tutorials with executable code to help biological researchers understand and visualize fundamental graph algorithms and concepts using real-world biological networks.

Contribution

It introduces a user-friendly, community-driven tutorial platform that combines visualization, code modification, and real biological data to enhance understanding of graph algorithms in biology.

Findings

Supports a wide range of biological networks from molecular to ecological scales

Enables users to modify and execute tutorials without an account

Facilitates community contributions for expanding tutorials and datasets

Abstract

Networks provide a meaningful way to represent and analyze complex biological information, but the methodological details of network-based tools are often described for a technical audience. Graphery is a hands-on tutorial webserver designed to help biological researchers understand the fundamental concepts behind commonly-used graph algorithms. Each tutorial describes a graph concept along with executable Python code that visualizes the concept in a code view and a graph view. Graphery tutorials help researchers understand graph statistics (such as degree distribution and network modularity) and classic graph algorithms (such as shortest paths and random walks). Users navigate each tutorial using their choice of real-world biological networks, ranging in scale from molecular interaction graphs to ecological networks. Graphery also allows users to modify the code within each tutorial or write new programs, which all can be executed without requiring an account. Discipline-focused tutorials will be essential to help researchers interpret their biological data. Graphery accepts ideas for new tutorials and datasets that will be shaped by both computational and biological researchers, growing into a community-contributed learning platform. Availability: Graphery is available at https://graphery.reedcompbio.org/.