The RAVEN (Reconstruction, Analysis and Visualization of Metabolic Networks) Toolbox 2 is a software suite for Matlab that allows for semi-automated reconstruction of genome-scale models (GEMs). It makes use of published models and/or KEGG, MetaCyc databases, coupled with extensive gap-filling and quality control features. The software suite also contains methods for visualizing simulation results and omics data, as well as a range of methods for performing simulations and analyzing the results. The software is a useful tool for system-wide data analysis in a metabolic context and for streamlined reconstruction of metabolic networks based on protein homology.
The information about downloading, installing and developing RAVEN is included in the Wiki. The source code documentation is also available online.
If you use RAVEN 2 in your scientific 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.
Starting with RAVEN v2.3.1, all the releases are also archived in Zenodo, for you to cite the specific version of RAVEN that you used in your study
If you use ftINIT in your scientific work, please 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
For crediting supporting work, please cite doi:10.1002/msb.145122 (tInit); doi:10.1371/journal.pcbi.1000859 (randomsampling). For crediting RAVEN 1, cite doi:10.1371/journal.pcbi.1002980. For more details, see wiki#cite-us.
For support, technical issues, bug reports etc., please
For more systems biology related software and recently published genome-scale models from the Systems and Synthetic Biology group at Chalmers University of Technology, please visit the GitHub page. For more information and publications by the Systems and Synthetic Biology please visit SysBio.