Year: 2026
Type: preprint
Abstract: Motivation Genome-scale metabolic reconstructions containing thousands of reactions are now curated and published for many organisms, but their visualization still relies heavily on manually constructed pathway maps. As these models grow in size and number, an automatic method is needed to organize their reactions into readable maps for visualization and downstream analysis. Results We developed M eta C arto , an automatic layout framework for genome-scale metabolic maps. M eta C arto identifies a biologically meaningful primary connection for each reaction and then organizes these connections into readable pathway-scale layouts, separating the biological representation of a reaction from its geometric placement. The resulting maps show strong agreement with curated KEGG pathway representations while maintaining geometric quality comparable to established graph-layout approaches. We applied M eta C arto to all 108 BiGG models and generated 2621 pathway maps without manual rearrangement. As a large-scale example, the human Recon3D reconstruction, containing 10 600 reactions, was automatically decomposed into 93 maps and laid out in 107 s. The generated maps are exported as Escher-compatible JSON and SVG files and can be directly browsed, edited, and used for metabolic data visualization. Availability and implementation Source code is available under CC BY 4.0 at github.com/forxhunter/MetaCarto. The complete collection of generated maps is available at GitHub map collection and can be browsed interactively at forxhunter.github.io/escher. Contact [email protected] Supplementary information Supplementary data are available at github.com/forxhunter/MetaCarto.
Author: Tianyu Wu
Institution: University of Illinois Urbana-Champaign
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7214373646