Usage

Usage as standalone application

Warning

main.py will be deprecated from version 2.0.0 onwards.*
For the main pipeline in main.py see the CarveMe-ModelPolisher-based (CMPB) workflow in SPECIMEN.
Beware that SPECIMEN only runs with refineGEMs 2.0.0 (starting with developmental versions).

The script main.py can be used directly in the command line after entering the virtual environment with pipenv shell or conda activate <EnvName>.

The config.yaml file contains defaults for all variables that need to be set by the user.

Warning

Using lab_strain=True has the following two requirements:

The model already contains GeneProduct identifiers containing valid NCBI Protein/RefSeq identifiers.
If there is no available data for the modeled organism in any database these identifiers can be added with the pipeline described in Pipeline: From genome sequence to draft model before draft model creation.

Input of a FASTA file containing header lines similar to:
>lcl|CP035291.1_prot_QCY37216.1_1 [gene=dnaA] [locus_tag=EQ029_00005] [protein=chromosomal replication initiator protein DnaA] [protein_id=QCY37216.1] [location=1..1356] [gbkey=CDS] Of the description part in the header line only locus_tag, protein and protein_id are important for polish.

Description: >
  This file can be adapted to choose what refineGEMs should do.
  Note: For windows use \ instead of / for the paths


General Setting: >
  Path to GEM to be investigated

model: 'data/e_coli_core.xml'
# Set the out path for all analysis files
out_path: ''

Settings for scripts that investigate the model: >
  These are only necessary if none of the scripts to manipulate the model are used.

# Set to TRUE if you want pngs that aid in model investigation, will be saved to a folder called 'visualization'
visualize: TRUE

# Set the basis medium to simulate growth from
growth_basis: 'minimal_uptake' # 'default_uptake' or 'minimal_uptake'

# Set to TRUE if you want to simulate anaerobic growth
anaerobic_growth: FALSE

# Settings if you want to compare multiple models
multiple: FALSE
multiple_paths: # enter as many paths as you need below
  - 'data/e_coli_core.xml'
  - ''
  - ''
single: TRUE # set to False if you only want to work with the multiple models

# media to simulate growth from, just comment the media you do not want with a #
media:
  - 'LB'
  - 'RPMI'
  - 'M9'
  - 'SNM3'
  - 'CGXII'
  - 'CasA'
  - 'Blood'
  - 'dGMM'
  - 'MP-AU'

# Determine whether the biomass function should be checked & normalised
biomass: TRUE

# determine whether the memote score should be calculated, default: FALSE
memote: FALSE

# compare metabolites to the ModelSEED database
modelseed: FALSE # set to False if not needed


Settings for scripts that manipulate the model: >
  They are all split into the ON / OFF switch (TRUE / FALSE) and additional settings like a path to where the new model should be saved.

model_out: '' # path and filename to where to save the modified model
entrez_email: '' # necessary to access NCBI API

### Addition of KEGG Pathways as Groups ###
keggpathways: FALSE

### SBO-Term Annotation ###
sboterms: FALSE

### Model polishing ### The database of the model identifiers needs to be specified with 'id_db'
polish: FALSE
id_db: 'BIGG' # Required!
# Possible identifiers, currently: BiGG & VMH
# For other IDs the `polish` function in `polish.py` might need adjustment
lab_strain: FALSE # Needs to be set to ensure that protein IDs get the 'bqbiol:isHomologTo' qualifier
                  # & to set the locus_tag to the ones obtained by the annotation
protein_fasta: '' # Path to used CarveMe input file, if exists; Needs to be set for lab_strain: True

### Charge correction ###
charge_corr: FALSE

### Manual Curation ###
man_cur: FALSE
man_cur_type: 'gapfill' # either 'gapfill' or 'metabs'
man_cur_table: 'data/manual_curation.xlsx'

### Automatic gap filling ###
# All parameters are required for all db_to_compare choices except:
# - organismid which is only required for db_to_compare: 'KEGG'/'KEGG+BioCyc'
# - and biocyc_files which is not required for 'KEGG'
gap_analysis: FALSE
gap_analysis_params:
  db_to_compare: 'KEGG'  # One of the choices KEGG|BioCyc|KEGG+BioCyc
  organismid: 'T05059'  # Needs to be specified for KEGG
  gff_file: 'data/cstr.gff'  # Path to RefSeq GFF file
  biocyc_files:
    - 'Path0'  # Path to TXT file containing a SmartTable from BioCyc with the columns 'Accession-2' 'Reaction of gene' (-)
    - 'Path1'  # Path to TXT file containing a SmartTable with all reaction relevant information (*)
    - 'Path2'  # Path to TXT file containing a SmartTable with all metabolite relevant information (+)
    - 'Path3'  # Path to protein FASTA file used as input for CarveMe (Needed to get the protein IDs from the locus tags)
# (-) If the organism is not in BioCyc retrieve a table mapping all reactions in BioCyc to the corresponding sequence
# (*) 'Reaction' 'Reactants of reaction' 'Products of reaction' 'EC-Number' 'Reaction-Direction' 'Spontaneous?'
# (+) 'Compound' 'Object ID' 'Chemical Formula' 'InChI-Key' 'ChEBI'
gapfill_model: FALSE
gap_analysis_file: 'Path to Excel file with which gaps in model should be filled'
# Either obtained by running gapfill_analysis/Created by hand with the same structure as the result file from gapfill_analysis
# Example Excel file to fill in by hand: data/modelName_gapfill_analysis_date_example.xlsx

The repository structure has the following intention:

refinegems/ contains all the functions needed in main.py
data/ contains all example tables that can be used as input for the curation scripts as well as the media_db.csv and a toy model e_coli_core.xml
Instead of using the files given in data/, you can use your own files and just change the paths in config.yaml. Please be aware that some functions rely on input in a certain format so make sure to check the files given in the data/ folder and use the same formatting.
refinegems/databases/ contains the SQL Schema file for the media and sboann-related tables as well as the ready-to-use database file necessary for the SBOAnn script by Elisabeth Fritze as well as the modules gapfill, growth and modelseed.
The setup.py and pyproject.toml enable creating a PyPI package called refineGEMs.

Usage as python module

Warning

Function calls will be deprecated from version 2.0.0 onwards.
Due to massive restructuring and extension, all functions have been moved into new modules.
If you used any functions from refineGEMs until now and want to use version 2.0.0 in the future, make sure to check your code after the version change.

See examples to learn how to use refineGEMs. Note that at this time most of the modules only make sense when you use the respective main functions:

Warning

Using lab_strain=True has the following two requirements:

The model already contains GeneProduct identifiers containing valid NCBI Protein/RefSeq identifiers.
If there is no available data for the modeled organism in any database these identifiers can be added with the pipeline described in Pipeline: From genome sequence to draft model before draft model creation.
Input of a FASTA file containing header lines similar to:
>lcl|CP035291.1_prot_QCY37216.1_1 [gene=dnaA] [locus_tag=EQ029_00005] [protein=chromosomal replication initiator protein DnaA] [protein_id=QCY37216.1] [location=1..1356] [gbkey=CDS] Of the description part in the header line only locus_tag, protein and protein_id are important for polish.

The modules io, cvterms and investigate provide functions that can be used by themselves.

`io`

Provides a couple of helper functions to load models, databases and parse gfffiles

`investigate`

Provides a couple of functions to get parameters of your model

`cvterms`

Provides functions to work with cvterms

Usage

Usage as standalone application

Usage as python module

io

investigate

cvterms

`io`

`investigate`

`cvterms`