Documentation · Installation · Guide · API reference · Migrate from RAVEN 2 · Citations
The RAVEN (Reconstruction, Analysis and Visualization of Metabolic Networks) Toolbox is a software suite for MATLAB for the semi-automated reconstruction of genome-scale metabolic models (GEMs). It reconstructs from published models or from the KEGG database, with gap-filling and quality control, and it simulates and analyses models and visualises simulation results and omics data. For context-specific model extraction, ftINIT is the method to use; getINITModel and runINIT are the original tINIT implementation, kept to reproduce models built with it.
RAVEN has a Python port, raven-toolbox, built on cobrapy. The documentation covers both toolboxes, and RAVEN 3 and raven-toolbox lists the functions that exist in one of them and the functions that return a different result in each.
This repository is where RAVEN 3 is developed. RAVEN 3 is a breaking release: the code is reorganised into functional folders, optional function arguments are passed by position, by name or both, external binaries and KEGG reference data are fetched on demand, several MATLAB toolbox dependencies are dropped, and low-value and duplicate functions are removed. The code can change until 3.0.0 is tagged. The last RAVEN 2 release is 2.11.4, maintained in SysBioChalmers/RAVEN. Migrating from RAVEN 2 lists every change for users who upgrade.
- Install RAVEN as described in the installation instructions. The Add-Ons manager of MATLAB installs the latest stable release, which is RAVEN 2; a RAVEN 3 pre-release is installed from this repository, and the instructions describe how.
checkRavenverifies the installation and the solver. - Follow Getting started in the guide, which has MATLAB and Python examples side by side, or open a script in the
tutorialsfolder. The tutorials come from the original RAVEN paper and are described in Legacy tutorials. - The GEM reconstruction protocol reconstructs a model for Hansenula polymorpha from start to finish.
The installation instructions, the guide, the protocol, the tuned parameter defaults and the API reference for MATLAB and Python are at raven-docs. The function help texts in this repository generate the MATLAB part of the API reference. Use GitHub Discussions for questions and comments, and issues for bug reports.
If you use RAVEN in your work, please cite:
Wang H, Marcišauskas S, Sánchez BJ, Domenzain I, Hermansson D, Agren R, Nielsen J, Kerkhoven EJ. (2018) RAVEN 2.0: A versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor. PLoS Comput Biol 14(10): e1006541. doi:10.1371/journal.pcbi.1006541.
All releases are archived on Zenodo, which allows citing the version used. If you use ftINIT, please also cite:
Gustafsson J, Anton M, Roshanzamir F, Jörnsten R, Kerkhoven EJ, Robinson JL, Nielsen J. (2023) Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data. Proc Natl Acad Sci 120(6): e2217868120. doi:10.1073/pnas.2217868120
Citations lists the papers behind individual methods, among them the first RAVEN paper (2013), random sampling and the INIT method.
Contributions are welcome. Read the contributing guidelines first. RAVEN 3 is developed on the develop branch, and RAVEN 2 is maintained in SysBioChalmers/RAVEN.