ChiMera: An easy to use pipeline for Bacterial Genome Based Metabolic Network Reconstruction, Evaluation and Visualization

Abstract

Several genome scale metabolic reconstruction tools have been developed in the last decades. They have helped to construct many metabolic models, which have contributed to a variety of fields, e.g., genetic engineering, drug discovery, prediction of phenotypes, and other model-driven discoveries. However, the use of these programs requires a higher level of bioinformatic skills, and most of them are not scalable for multiple genomes. Moreover, the functionalities required to build models are generally scattered through multiple tools, requiring knowledge of their utilization.Here, we present ChiMera, which combines the most efficient tools in model reconstruction, prediction, and visualization. ChiMera uses CarveMe top-down approach based on genomic evidence to prune a global model with a high level of curation, generating a draft genome able to produce growth predictions using flux balance analysis for gram-positive and gram-negative bacteria. ChiMera also contains two modules of visualization implemented, predefined and universal. The first generates maps for the most important pathways, e.g., core-metabolism, fatty acid oxidation and biosynthesis, nucleotides and amino acids biosynthesis, glycolysis, and others. The second module produces a genome-wide metabolic map, which can be used to harvest KEGG pathway information for each compound in the model. A module of gene essentiality and knockout is also present. Overall, ChiMera combines model creation, gap-filling, FBA and metabolic visualization to create a simulation ready genome-scale model, helping genetic engineering projects, prediction of phenotypes, and other model-driven discoveries in a friendly manner.

Cost-effective hybrid long-short read assembly delineates alternative GC-richStreptomyceschassis for natural product discovery

“…The procedure has been detailed in Methods section. Briefly, we first performed protein annotations on the genome assemblies of all strains using Bakta (v1.4.0) (43), the annotations were then used to build draft metabolic networks using Chimera pipeline (44). Finally, KEGG pathway information was extracted from files containing the genome-wide metabolic mapping information (Figure 5).…”

“…Metabolic pathway enrichment analysis The genome sequences were first subjected to annotation using Bakta pipeline (v1.4.0) [41]. Strain-specific genome-scale metabolic models were generated from the Bakta protein annotations using CarveMe algorithm (v1.4.1) [54] part of Chimera pipeline [42]. KEGG [46] pathway information for each strain was retrieved from Chimera outputs and merged to generate pathway enrichment heatmaps.…”

Transcriptome guided metabolic network analysis reveals rearrangements of carbon flux distribution inNeisseria gonorrhoeaeduring neutrophil co-culture

“…Genome-scale metabolic network reconstructions (GENREs) are a mathematical framework encompassing much of the known metabolic information on an organism (13). A draft GENRE can be generated with an annotated genome and several automated network reconstruction tools (14)(15)(16)(17), then extensively manually curated using published literature and experimental data. GENREs can simulate all possible growth capabilities of an organism, which are then constrained by biological and physical parameters such as metabolite availability and optimized for a desired outcome, such as biomass production.…”

ChiMera: an easy to use pipeline for bacterial genome based metabolic network reconstruction, evaluation and visualization

“…This tool combines model creation, gap-filling, flux balance analysis (FBA), and metabolic network visualization to create a simulation-ready genomescale model, based on CarveMe (17), COBRApy (22), Escher (23), and PSAMM (24) algorithms. All the steps are done automatically for the user, reducing the need for bioinformatics skills (25).…”

“…The prediction capabilities of ChiMera models were also compared with manually curated models. Both sets of models were simulated using 46, 50, and 70 different carbon sources for B. subtilis (27,28), E. coli (23,24), and P. putida models (25), respectively (Supplementary Table 4, 5 and 6). This analysis suggested that ChiMera models were able to perform comparably to manually-curated models.…”