Niche-specific metabolic phenotypes can be used to identify antimicrobial targets in pathogens

This repostiory contains all software created for data analysis for the project titled "Niche-specific metabolic phenotypes can be used to identify antimicrobial targets in pathobionts"

gapfill_rxn_extractor.py: Used to extract the number of gapfilled reactions and metabolites from reconstructor output file. Used in S11.

binary_essential_genes.py: creates a matrix of 0s and 1s indicating whether a gene is essential or non-essential across all network models.

MakeFigure.py: Renders the final figure presented in Figure 4.

GetEssentialGenes.py: Determines essential genes for each metabolic network model.

Genesvsrxns.py: Renders Figure 1g

Gene_to_KO.py: Converts KEGG gene names to KO numbers

16s_extractor.py: Extracts 16s sequences from a .fasta file

genesrxnsmetabolfig.py: Renders figures 1def

ReactionAnnotationFig.py: Renders figure 2.

Memotescript.py: Renders figure S2. 

makereacitondf.py: Generates a dataframe that is the union of all reactions across models.

isolationsourceEG.py: Identifies genes uniquely essential to a given physiological location.

genome_features_rRNA.py: Extracts rRNA features 

Get_Essential_Genes_367.py: Determines essential genes for a subset of 367 models; those with annotated 16S sequences. Used in S11.

gapfilled_rxns_metabolites.py: Renders S11 Figure 2

GrowthCurvesForFinal.py: Renders S10

Tsne.py: Renders Fig 3 and S9

Tsne100recon100samp.py: Renders S8