Package: kegg2bipartitegraph
kegg2bipartitegraphkegg2bipartitegraph is a Python package to create KEGG graphs. The main idea of this package is to create metabolic graphs from KEGG database according to the ones used in the articleWeber Zendrera et al. (2021). In this article, the authors creates the metabolic networks from the organism of KEGG (accessible in thisgithub repository). Using annotation (from EsMeCaTa, eggnog-mapper, KofamKOALA or GenBank files) or a KEGG organism ID,kegg2bipartitegraphmaps the EC (and Gene Ontology Terms) to KEGG reactions and reconstruct metabolic networks associated with the organism following the proposal of the articleWeber Zendrera et al. (2021).InstallationThis package can be installed using pip:pip install kegg2bipartitegraphUsageKegg2bipartitegraph can be called by using the commandk2bg. It is divided in different parts:k2bg referencean optional ones that creates the reference data (especially the universal reference metabolic graphs). By default, these data are precomputed and available within the package located inkegg2bipartitegraph/data/kegg_model. It is not meant to be used by the user as it takes a long time to process, only used the precomputed reference files or archived files.subcommands to reconstruct metabolic graphs from different inputs:k2bg reconstruct_from_esmecatatakes as input the annotation output folder fromEsMeCaTaand reconstruct the metabolic networks associated with each taxon.k2bg reconstruct_from_eggnogtakes as input the annotation output file fromeggnog-mapperto map the EC to KEGG reactions.k2bg reconstruct_from_kofamkoalatakes as input the result fromKofamKOALA.k2bg reconstruct_from_picrusttakes as input the result folder frompicrust2.k2bg reconstruct_from_organismtakes as input an organism ID from KEGG (such ashsafor human orecoforEscherichia coli). You can find the list of the accessile organisms inKEGG website.k2bg reconstruct_from_genbanktakes as input a folder containing GenBank files.Online / Offline requirementsMultiple subcommands can be used to reconstruct draft networks. Some of them required an internet connection to work, you can see which ones in the following table:SubcommandsOnlineOfflinereconstruct_from_esmecata(Mapping of KOs)X (without mapping KOs)reconstruct_from_eggnogXreconstruct_from_kofamkoalaXreconstruct_from_picrustXreconstruct_from_genbankXreconstruct_from_organismXreferenceXReference modelThek2bg referenceis to be used only if you want to update the KEGG reference data. First, delete the data contain inkegg2bipartitegraph/data/kegg_model, then use this command to download all the required data. This step is long, it is advised to not use it.It will create several files:kegg_model.sbml: a universal graph containing most of the reactions contained in KEGG database. Such as in the graph made byWeber Zendrera et al. (2021), 14 cofactors have been removed (H2O, ATP, ADP, NAD+, NADH, NADP+, NADPH, CO2, ammonia, sulfate, thioredoxin, phosphate, pyrophosphate (PPi), and H+). Also the stoechiometry is simplified as these metabolic networks are created in order to be used in topological analysis.So they are not supposed to be used with other methods (such as Constraint-Based Modelling).kegg_model.graphml: the metabolic bipartite graph associated with thekegg_model.sbmlfile.several mapping files to go from annotation (especially EC number) to KEGG reactions:kegg_compound_name.tsv,kegg_mapping.tsvandkegg_pathways.tsv. Also a file to use KEGG hierarchy for pathway/module/metabolite:kegg_hierarchy.json. A mapping fileec_to_gos.tsvto convert Gene Ontology terms to EC number using thego2ec fileprovided by the Gene Ontology Consortium.kegg_metadata.json: a metadata file showing the metadata for the creation of the reference files for kegg2bipartitegraph.Output files of other commandThe other subcommands will reconstruct draft metabolic networks by mapping the annotation with the metabolic graphs contained in kegg2bipartitegraph.Then it will create multiple files:a sbml file containing the metabolic network that can be used with topological analysis methods (such asMeneTools,MiSCoToorMetage2Metabo).a graphml file containing the metabolic network as a bipartite graph. At this moment, it is not used, but I am currently adaptating the scope method ofWeber Zendrera et al. (2021)to automatise its use with this package.tsv files indicating the pathways/modules contained in the metabolic networks, their completness ratio and the associated reactions.a tsv file showing KO information if the option has been used.amodule_class.tsvshowing the absence/presence of generic module classes in the organism.several statistics/metadata/log files.CitationAt this moment, there are no articles for kegg2bipartitegraph, if you use it and want to cite it, you can cite this GitHub.Also, please cite the following article:the article made by Adèle Weber Zendrera et al. (2021) that proposed this method:Weber Zendrera, A., Sokolovska, N. & Soula, H.A. Functional prediction of environmental variables using metabolic networks. Scientific Reports  11, 12192 (2021).https://doi.org/10.1038/s41598-021-91486-8theKEGG database:Kanehisa, M.,  Furumichi, M., Sato, Y., Kawashima, M., Ishiguro-Watanabe, M. KEGG for taxonomy-based analysis of pathways and genomes, Nucleic Acids Research, Volume 51, Issue D1, Pages D587–D592 (2023).https://doi.org/10.1093/nar/gkac963Kanehisa, M., Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes, Nucleic Acids Research, Volume 28, Issue 1, Pages 27–30 (2000).https://doi.org/10.1093/nar/28.1.27Kanehisa, M. Toward understanding the origin and evolution of cellular organisms. Protein Science. 28: 1947–1951 (2019).https://doi.org/10.1002/pro.3715bioservicesfor the query on KEGG:Cokelaer, T., Pultz, D., Harder, L., M., Serra-Musach, J., Saez-Rodriguez, J., BioServices: a common Python package to access biological Web Services programmatically, Bioinformatics, Volume 29, Issue 24, Pages 3241–3242 (2013).https://doi.org/10.1093/bioinformatics/btt547libsbmlfor the handling of the SBML:Bornstein B. J., Keating S. M., Jouraku, A., Hucka, M., LibSBML: an API Library for SBML, Bioinformatics, Volume 24, Issue 6, Pages 880–881 (2008).https://doi.org/10.1093/bioinformatics/btn051networkxfor the creation of the graphml:Hagberg A. A., Schult D. A., Swart P. J. Exploring Network Structure, Dynamics, and Function using NetworkX, in: Varoquaux, G., Vaught, T., Millman, J. (Eds.), . Presented at the Proceedings of the Python in Science Conference (SciPy) 2008. 11–15.http://conference.scipy.org/proceedings/SciPy2008/paper_2/If you have used the subcommandreconstruct_from_genbank, please also cite:Biopythonfor GenBank parsing:Cock, P.J.A., Antao, T., Chang, J.T., Chapman, B.A., Cox, C.J., Dalke, A., Friedberg, I., Hamelryck, T., Kauff, F., Wilczynski, B., de Hoon, M.J.L. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 2009, 25, 1422–1423https://doi.org/10.1093/bioinformatics/btp163.