Generated April 3, 2023

Formation and interaction network of a constructed microbial community in a nutrient-rich environment

This narrative was used to generate metabolic model of strain Sphingobium sp. AP49 gapfilled by R2A medium environment. The metabolic model of strain AP49 (AP49_ReSeq_MetabolicModel_R2A004Gapfilled) was further used for dynamic flux balance analysis (dFBA) simulation by Computation of Microbial Ecosystems in Time and Space (COMETS) tool.

Annotate or re-annotate genome/assembly using RASTtk (Rapid Annotations using Subsystems Technology toolkit).
This app completed without errors in 5m 34s.
Objects
Created Object Name Type Description
AP49_ReSeq_Annotation_01 Genome RAST re-annotated genome
Summary
The RAST algorithm was applied to annotating a genome sequence comprised of 1 contigs containing 4506188 nucleotides. No initial gene calls were provided. Standard features were called using: glimmer3; prodigal. A scan was conducted for the following additional feature types: rRNA; tRNA; selenoproteins; pyrrolysoproteins; repeat regions; crispr. The genome features were functionally annotated using the following algorithm(s): Kmers V2; Kmers V1; protein similarity. In addition to the remaining original 0 coding features and 0 non-coding features, 4465 new features were called, of which 187 are non-coding. Output genome has the following feature types: Coding gene 4278 Non-coding crispr_array 1 Non-coding crispr_repeat 40 Non-coding crispr_spacer 39 Non-coding repeat 40 Non-coding rna 67 The number of distinct functions can exceed the number of genes because some genes have multiple functions.
Links
Construct a draft metabolic model based on an annotated genome.
This app completed without errors in 3m 2s.
Objects
Created Object Name Type Description
AP49_ReSeq_MetabolicModel_R2A004Gapfilled FBAModel FBAModel-14 AP49_ReSeq_MetabolicModel_R2A004Gapfilled
AP49_ReSeq_MetabolicModel_R2A004Gapfilled.gf.1 FBA FBA-13 AP49_ReSeq_MetabolicModel_R2A004Gapfilled.gf.1
Report
Summary
RefGlucoseMinimal media.
Output from Build Metabolic Model
The viewer for the output created by this App is available at the original Narrative here: https://narrative.kbase.us/narrative/117923
Predict metabolite fluxes in a metabolic model of an organism grown on a given media using flux balance analysis (FBA).
This app completed without errors in 5m 38s.
Objects
Created Object Name Type Description
AP49_ReSeq_FBA_R2A004Gapfilled_Carbon60 FBA FBA-13 AP49_ReSeq_FBA_R2A004Gapfilled_Carbon60
Report
Summary
A flux balance analysis (FBA) was performed on the metabolic model 117923/6/1 growing in 117923/3/1 media.
Output from Run Flux Balance Analysis
The viewer for the output created by this App is available at the original Narrative here: https://narrative.kbase.us/narrative/117923

Apps

  1. Annotate Genome/Assembly with RASTtk - v1.073
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  2. Build Metabolic Model
    • [1] Henry CS, DeJongh M, Best AA, Frybarger PM, Linsay B, Stevens RL. High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat Biotechnol. 2010;28: 977 982. doi:10.1038/nbt.1672
    • [2] Overbeek R, Olson R, Pusch GD, Olsen GJ, Davis JJ, Disz T, et al. The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST). Nucleic Acids Res. 2014;42: D206 D214. doi:10.1093/nar/gkt1226
    • [3] Latendresse M. Efficiently gap-filling reaction networks. BMC Bioinformatics. 2014;15: 225. doi:10.1186/1471-2105-15-225
    • [4] Dreyfuss JM, Zucker JD, Hood HM, Ocasio LR, Sachs MS, Galagan JE. Reconstruction and Validation of a Genome-Scale Metabolic Model for the Filamentous Fungus Neurospora crassa Using FARM. PLOS Computational Biology. 2013;9: e1003126. doi:10.1371/journal.pcbi.1003126
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  3. Run Flux Balance Analysis
    • Henry CS, DeJongh M, Best AA, Frybarger PM, Linsay B, Stevens RL. High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat Biotechnol. 2010;28: 977 982. doi:10.1038/nbt.1672
    • Orth JD, Thiele I, Palsson B . What is flux balance analysis? Nature Biotechnology. 2010;28: 245 248. doi:10.1038/nbt.1614