Generation of prior knowledge network for COSMOS

This repository contains a set of functions to generate organism-specific prior knowledge networks (PKNs) for the cosmosR R package. It is structured as follows:

• data: it contains the starting data needed to generate PKNs for every organism. These data come from this repository, a platform of genome-scale metabolic models (GEM) for different organisms. For more details about how these GEMs were generated, see Wang et al., 2021. In this repository, only GEMs for human (Homo sapiens), mouse (Mus musculus) and rat (Ratus norvegicus) are available. In addition, information from KEGG to determine cofactors is also already downloaded and stored as an RDS file. Importantly, COSMOS also uses information of chemical-protein interactions obtained from the STITCH database (Kuhn et al., 2008). However, these files are not available in this repository, and must be downloaded from here.
• notebooks: Rmd with a brief analysis of COSMOS results comparing the old (available at the cosmosR R package) and new PKN for human data.
• output: final PKNs as RDS files and COSMOS results.
• reports: HTML and plots (png) generated after rendering Rmds in notebooks.
• src:
  • final_functions_PKN_COSMOS.R: set of functions in charge of generating the PKNs.
  • generation_networks_COSMOS.R: script which loads final_functions_PKN_COSMOS.R functions and write the final PKNs as RDS files in output.
  • run_COSMOS_*: scripts used to run COSMOS with data from https://github.com/saezlab/Sciacovelli_Dugourd_2021_paper and the old and new PKNs. The suffix determines the PKN and maximum_network_depth parameter used.

These functions will be available on the OmnipathR R package.

References

• Kuhn M, von Mering C, Campillos M, Jensen LJ, Bork P. STITCH: interaction networks of chemicals and proteins. Nucleic Acids Research. 2008 Jan 1;36(suppl_1):D684–8.
• Wang H, Robinson JL, Kocabas P, Gustafsson J, Anton M, Cholley PE, et al. Genome-scale metabolic network reconstruction of model animals as a platform for translational research. Proceedings of the National Academy of Sciences. 2021 Jul 27;118(30):e2102344118.