Files in the data folder

The data folder of a GECKO 4 project holds the input data of the reconstruction, the kcat source files and the files of the kcat predictors. GECKO (MATLAB) and geckopy (Python) read and write the same files, so a data folder made with one toolbox is used by the other. Getting started describes the layout of a project and Project format 4 the folders outside data.

Databases and gene identifiers

File	Content	Functions	Page
uniprot.tsv	UniProt data for the organism: protein identifier, gene identifier, EC number, MW and sequence. loadDatabases downloads it when it is missing	loadDatabases (Python: geckopy uniprot-download) writes; makeEcModel, getStandardKcat and calculateFfactor read	Getting started, Building an empty ecModel
kegg.tsv	KEGG data for the organism: UniProt protein ID, gene, KEGG gene ID, EC number, MW, pathway and sequence. loadDatabases downloads it when it is missing	loadDatabases (Python: geckopy kegg-download) writes; makeEcModel (make_ec_model) reads	Getting started, Gathering kcats
uniprotConversion.tsv	Conversion table for models whose gene identifiers match no UniProt field. A header row, then the gene identifier and the UniProt identifier. A gene that is not listed keeps its identifier in geckopy and gets an empty UniProt identifier in MATLAB. Written by the user	ModelAdapter.getUniprotIDsFromTable (Python: get_uniprot_ids_from_table)	Getting started
pseudoRxns.tsv	Optional. Reaction identifiers in the first column and reaction names, for reference, in the second. These reactions lose their gene association in makeEcModel (make_ec_model) and receive no standard kcat in getStandardKcat (assign_standard_kcat). Written by the user	makeEcModel, getStandardKcat	Building an empty ecModel, Applying kcats
ComplexPortal.json	Enzyme complex information from the Complex Portal	getComplexData (Python: get_complex_data) writes; applyComplexData (apply_complex_data) reads	Building an empty ecModel
smilesDB.tsv	SMILES for the metabolites of the ecModel, a cache of the PubChem queries	findMetSmiles (Python: find_met_smiles)	Gathering kcats

Kcat source files and BRENDA data

Path	Content	Functions	Page
kcats/<source>.tsv	One kcat source file per source: custom, brenda, ec_tree, homologs, dlkcat and okp_<method>. The format is described in Kcat source files	kcatsFromBrenda, kcatsFromECTree, kcatsFromHomologs, kcatsFromDLKcat and kcatsFromOKP write; loadKcatSource and collectKcats read. custom.tsv is written by hand or by convertCustomKcats (convert_custom_kcats)	Gathering kcats, Applying kcats
brenda/	Optional. The BRENDA files of the project: kcat.tsv, sa.tsv, mw.tsv, measurements.tsv and the other files of the BRENDA release. Without the folder, the files of the toolbox-assets release are downloaded once	ModelAdapter.getBrendaDBFolder (Python: get_brenda_db_folder); geckopy brenda-refresh writes kcat.tsv, sa.tsv and mw.tsv to the folder, by default data/brenda of the current directory; it does not produce measurements.tsv, sequences.fasta or substrates.tsv, and kcats_from_ec_tree then falls back to the asset	Gathering kcats
customKcats.tsv	Curated kcats in the format of GECKO 3, with the columns proteins, genes, gene_name, kcat, rxns, notes and stoicho. collectKcats applies the file when a stage lists custom and kcats/custom.tsv does not exist	applyCustomKcats (Python: apply_custom_kcats) reads; convertCustomKcats (convert_custom_kcats) converts	Applying kcats
PhylDist.mat	KEGG phylogenetic distances, which geckopy uses to match kcats of other organisms. Downloaded when it is missing. GECKO reads the corresponding file of RAVEN	kcats_from_brenda	Gathering kcats

Predictor files

File	Content	Functions	Page
DLKcat.tsv	Input of DLKcat and, after a run, its output	writeDLKcatInput (Python: write_dlkcat_input) and runDLKcat write; kcatsFromDLKcat and readDLKcatOutput read	Gathering kcats
DLKcat_run.txt	The number of reactions that the last DLKcat run covered, when it covered a subset of the reactions	kcatsFromDLKcat (Python: kcats_from_dlkcat)	Gathering kcats
DLKcatIgnoreMets.tsv, DLKcatCurrencyMets.tsv	Optional project copies of the lists of metabolites that are left out of the DLKcat and OKP input: names with SMILES, and pairs of currency metabolites. Without them, the files of the toolbox are used	writeDLKcatInput, writeOpenKineticsPredictorInput	Gathering kcats
OKP.csv	Input of an OpenKineticsPredictor job	writeOpenKineticsPredictorInput, submitOpenKineticsPredictor	Gathering kcats
OKP_job_<method>.txt, OKP_output_<method>.csv	The job and the result of one OKP method	kcatsFromOKP (Python: kcats_from_okp)	Gathering kcats
OKP_job.txt, OKP_output.csv	The job and the result of a job that submitOpenKineticsPredictor and fetchOpenKineticsPredictor handle. kcatsFromOKP adopts them for its method when no job of that method exists	submitOpenKineticsPredictor, fetchOpenKineticsPredictor	Gathering kcats
okpApiKey.txt	The API key of OpenKineticsPredictor, an alternative to the argument apiKey and to the environment variable OKP_API_KEY	submitOpenKineticsPredictor, fetchOpenKineticsPredictor	Gathering kcats

Proteomics, flux data and tuning data

File	Content	Functions	Page
paxDB.tsv	Protein abundance data from PaxDB for the f factor (the mass fraction of proteins accounted for in the ecModel). Header lines start with #, followed by three tab-separated columns: an internal identifier, the gene and the abundance. Without the file, calculateFfactor returns 0.5	calculateFfactor (Python: load_pax_db, calculate_f_factor)	Applying kcats
proteomics.tsv	Measured protein levels (mg protein/gDCW) from one or more experiments: UniProt identifiers in the first column, protein levels from individual replicates in each subsequent column	loadProtData (Python: load_prot_data)	Proteomics integration
fluxData.tsv	Experimental growth rate, protein content and carbon-source uptake, that constrain the ecModel during proteomics integration and in flux-variability comparisons. Exchange fluxes in mmol/gDCWh; uptake negative, excretion positive. Not required for the initial reconstruction	loadFluxData (Python: load_flux_data), constrainFluxData	Proteomics integration, Simulation and analysis
evotuneFluxData.tsv, evotuneMaxGrowth.tsv, evotuneZeroExch.tsv	Optional data of the kcat tuning with CMA-ES: flux data, maximum growth rates and exchange reactions with zero flux	loadEvotuneData	Tuning against experimental data

Spontaneous reactions

The file that [spontaneous] file in model_adapter.toml names is a TSV with a header row. It lists reaction identifiers in the first column and, optionally, a second column in which 1 or true marks the spontaneous reactions. The path is relative to the project folder and the file name is free. See Model adapter parameters.

See also

• Getting started, where a project's data folder is created.

• Model adapter parameters, the parameters of model_adapter.toml.

• Project format 4, the folder layout of a GECKO 4 project.