Scripts: Connection

Description:

#TODO

biggAPI_to_padmet

Description:

Require internet access !

Allows to extract the bigg database from the API to create a padmet.

1./ Get all reactions universal id from http://bigg.ucsd.edu/api/v2/universal/reactions, escape reactions of biomass.

2./ Using async_list, extract all the informations for each reactions (compounds, stochio, name …)

3./ Need to use sleep time to avoid to lose the server access.

4./ Because the direction fo the reaction is not set by default in bigg. We get all the models where the reaction is and the final direction will the one found in more than 75%

5./ Also extract xrefs

usage:
    biggAPI_to_padmet.py --output=FILE [--pwy_file=FILE] [-v]

options:
    -h --help     Show help.
    --output=FILE    path to output, the padmet file.
    --pwy_file=FILE   add kegg pathways from pathways file, line:'pwy_id, pwy_name, x, rxn_id'.
    -v   print info.
padmet_utils.connection.biggAPI_to_padmet.main()[source]

check_orthology_input

Description:

Before running orthology based reconstruction it is necessary to check if the metabolic network and the proteom of the model organism use the same ids for genes (or at least more than a given cutoff). To only check this. Use the 2nd usage.

If the genes ids are not the same, it is necessary to use a dictionnary of genes ids associating the genes ids from the proteom to the genes ids from the metabolic network.

To create the correct proteom from the dictionnnary, use the 3nd usage Finnaly by using the 1st usage, it is possible to:

1/ Check model_faa and model_metabolic for a given cutoff

2/ if under the cutoff, convert model_faa to the correct one with dict_ids_file

3/ if still under, SystemExit()

usage:
    check_orthology_input.py    --model_metabolic=FILE    --model_faa=FILE    [--cutoff=FLOAT] [--dict_ids_file=FILE] --output=FILE    [-v]
    check_orthology_input.py    --model_metabolic=FILE    --model_faa=FILE    [--cutoff=FLOAT] [-v]
    check_orthology_input.py    --model_faa=FILE    --dict_ids_file=FILE    --output=FILE [-v]

option:
    -h --help    Show help.
    --model_metabolic=FILE    pathname to the metabolic network of the model (sbml).
    --model_faa=FILE    pathname to the proteom of the model (faa)
    --cutoff=FLOAT    cutoff [0:1] for comparing model_metabolic and model_faa. [default: 0.70].
    --dict_ids_file=FILE    pathname to the dict associating genes ids from the model_metabolic to the model_faa. line = gene_id_in_metabolic_network       gene_id_in_faa
    --output=FILE    output of get_valid_faa (a faa) or get_dict_ids (a dictionnary of gene ids in tsv)
    -v   print info
padmet_utils.connection.check_orthology_input.main()[source]

enhanced_meneco_output

Description:

The standard output of meneco return ids of reactions corresponding to the solution for gapfilling.

The ids are those from the sbml and so they are encoded.

This script extract the solution corresponding to the union of reactions “Computing union of reactions from all completion” Based on padmetRef return a file with more information for each reaction.

ex: RXN__45__5

RXN-5, common_name, ec-number, Formula (with id),Formula (with cname),Action,Comment Also, the output can be used as input of the script update_padmetSpec.py In the column Action: ‘add’ => To add the reaction, ‘’ => to do nothing

Comment: the reason of adding the reaction (ex: added for gap-filling by meneco)

usage:
    enhanced_meneco_output.py --meneco_output=FILE --padmetRef=FILE --output=FILE [-v]

options:
    -h --help     Show help.
    --meneco_output=FILE    pathname of a meneco run' result
    --padmetRef=FILE    path to padmet file corresponding to the database of reference (the repair network)
    --output=FILE    path to tsv output file
padmet_utils.connection.enhanced_meneco_output.main()[source]

extract_orthofinder

Description:

After running orthofinder on n fasta file, read the output file ‘Orthogroups.csv’

Require a folder ‘orthology_based_folder’ with this archi:

|– model_a
– model_a.sbml
|– model_b
–model_b.sbml

And the name of the studied organism ‘study_id’

  1. Read the orthogroups file, extract orthogroups in dict ‘all_orthogroups’, and all org names

  2. In orthology folder search for sbml files ‘extension = .sbml’

  3. For each models regroup all information in a dict dict_data:

    {‘study_id’: study_id, ‘model_id’ : model_id, ‘sbml_template’: path to sbml of model’, ‘output’: path to the output sbml, ‘verbose’: bool, if true print information }

    The output is by default:

    output_orthofinder_from_’model_id’.sbml

  4. Store all previous dict_data in a list all_dict_data

  5. iter on dict from all_dict_data and use function dict_data_to_sbml

Use a dict of data dict_data and dict of orthogroups dict_orthogroup to create sbml files.

dict_data and dict_orthogroup are obtained with fun orthofinder_to_sbml

6./ Read dict_orthogroups and check if model associated to dict_data and study org share orthologue

7./ Read sbml of model, parse all reactions and get genes associated to reaction.

8./ For each reactions:

Parse genes associated to sub part (ex: (gene-a and gene-b) or gene-c) = [(gene-a,gene-b), gene-c]

Check if study org have orthologue with at least one sub part (gene-a, gene-b) or gene-c

if yes: add the reaction to the new sbml and change genes ids by study org genes ids

Create the new sbml file.

usage:
    extract_orthofinder --sbml=FILE/DIR --orthologues=DIR --study_id=STR --output=DIR [--workflow=STR] [-v]
    extract_orthofinder --sbml=DIR --orthogroups=FILE --study_id=STR --output=DIR [--workflow=STR] [-v]

option:
    -h --help    Show help.
    --sbml=DIR   Folder with sub folder named as models name within sbml file name as model_name.sbml
    --orthogroups=FILE   Output file of Orthofinder run Orthogroups.tsv
    --orthologues=DIR   Output directory of Orthofinder run Orthologues
    --study_id=ID   name of the studied organism
    --workflow=ID   worklow id in ['aureme','aucome']. specific run architecture where to search sbml files
   --output=DIR   folder where to create all sbml output files
    -v   print info
padmet_utils.connection.extract_orthofinder.main()[source]

extract_rxn_with_gene_assoc

Description:

From a given sbml file, create a sbml with only the reactions associated to a gene.

Need for a reaction, in section ‘note’, ‘GENE_ASSOCIATION’: ….

usage:
    extract_rxn_with_gene_assoc.py --sbml=FILE --output=FILE [-v]

options:
    -h --help     Show help.
    --sbml=FILE    path to the sbml file
    --output=FILE    path to the sbml output (with only rxn with genes assoc)
    -v   print info
padmet_utils.connection.extract_rxn_with_gene_assoc.main()[source]

gbk_to_faa

Description:
convert GBK to FAA with Bio package
usage:
    gbk_to_faa.py    --gbk=FILE --output=FILE [--qualifier=STR] [-v]

option:
    -h --help    Show help.
    --gbk=FILE    path to the gbk file.
    --output=FILE    path to the output, a FAA file.
    --qualifier=STR    the qualifier of the gene id [default: locus_tag].
    -v   print info
padmet_utils.connection.gbk_to_faa.main()[source]

gene_to_targets

Description:

From a list of genes, get from the linked reactions the list of products.

R1 is linked to G1, R1 produces M1 and M2. output: M1,M2. Takes into account reversibility

usage:
    gene_to_targets.py --padmetSpec=FILE --genes=FILE --output=FILE [-v]

option:
    -h --help     Show help
    --padmetSpec=FILE    path to the padmet file
    --genes=FILE   path to the file containing gene ids, one id by line
    --output=FILE    path to the output file containing all tagerts which can by produced by all reactions associated to the given genes
    -v   print info
padmet_utils.connection.gene_to_targets.main()[source]

modelSeed_to_padmet

Description:
#TODO
usage:
    modelSeed_to_padmet.py --output=FILE --rxn_file=FILE --pwy_file=FILE [-v]

options:
    -h --help     Show help.
    --output=FILE    path of the padmet file to create
    --rxn_file=FILE   path to json file of modelSeed reactions
    --pwy_file=FILE   path to pathway reactions association from modelSeed
    -v   print info.
padmet_utils.connection.modelSeed_to_padmet.main()[source]

padmet_to_asp

Description:
Convert PADMet to ASP following these predicats: common_name({reaction_id or enzyme_id or pathway_id or compound_id} , common_name) direction(reaction_id, reaction_direction). reaction_direction in[LEFT-TO-RIGHT,REVERSIBLE] ec_number(reaction_id, ec(x,x,x)). catalysed_by(reaction_id, enzyme_id). uniprotID(enzyme_id, uniprot_id). #if has has_xref and db = “UNIPROT” in_pathway(reaction_id, pathway_id). reactant(reaction_id, compound_id, stoechio_value). product(reaction_id, compound_id, stoechio_value). is_a(compound_id, class_id). is_a(pathway_id, pathway_id).
usage:
    padmet_to_asp.py --padmet=FILE --output=FILE [-v]

option:
    -h --help     Show help.
    --padmet=FILE    path to padmet file to convert.
    --output=FILE    path to output file in lp format.
    -v    print info.
padmet_utils.connection.padmet_to_asp.main()[source]

padmet_to_matrix

Description:

Create a stoichiometry matrix from a padmet file.

The columns represent the reactions and rows represent metabolites.

S[i,j] contains the quantity of metabolite ‘i’ produced (negative for consumed) by reaction ‘j’.

usage:
    padmet_to_matrix.py --padmet=FILE --output=FILE

option:
    -h --help    Show help.
    --padmet=FILE    path to the padmet file to convert.
    --output=FILE    path to the output file, col: rxn, row: metabo, sep = "        ".
padmet_utils.connection.padmet_to_matrix.main()[source]

padmet_to_padmet

Description:

Allows to merge 1-n padmet. 1./ Update the ‘init_padmet’ with the ‘to_add’ padmet(s). to_add can be a file or a folder with only padmet files to add.

padmetRef can be use to ensure data uniformization.

usage:
    padmet_to_padmet.py --to_add=FILE/DIR --output=FILE [--padmetRef=FILE]  [-v]

options:
    -h --help     Show help.
    --to_add=FILE/DIR    path to the padmet file to add (sep: ;) or path to folder of padmet files.
    --output=FILE   path to the new padmet file
    --padmetRef=FILE    path to the padmet file representing to the database of reference (ex: metacyc_18.5.padmet)
    -v   print info
padmet_utils.connection.padmet_to_padmet.main()[source]

padmet_to_tsv

Description:

convert a padmet representing a database (padmetRef) and/or a padmet representing a model (padmetSpec) to tsv files for askomics.

1./ Folder creation given the output directory. Create this directory if required and create a folder padmetRef filename and/or padmetSpec filename

2./

2.1/ For padmetRef:

2.1.a/ Nodes

get all reactions nodes => extract data from misc with extract_nodes(rxn_nodes, “reaction”, “../rxn.tsv”)

get all compounds nodes => extract data from misc with extract_nodes(cpd_nodes, “compounds”, “../cpd.tsv”)

get all pathways nodes => extract data from misc with extract_nodes(pwy_nodes, “pathway”, “../pwy.tsv”)

get all xrefs nodes => extract data from misc with extract_nodes(xref_nodes, “xref”, “../xref.tsv”)

2.1.b/ Relations

for each rxn in rxn nodes:

get all rlt consumes/produces => create list of data with extract_rxn_cpd(rxn_cpd_rlt)
fieldnames = “rxn_cpd”,”concerns@reaction”,”consumes@compound”,”produces@compound”,”stoichiometry”,”compartment”
get all rlt is_in_pathway => create list of data with extract_rxn_pwy(rxn_pwy_rlt)
fieldnames = “rxn_pwy”,”concerns@reaction”,”in_pwy@pathway”

get all rlt has_xref => create list of data with extract_entity_xref(rxn_xref_rlt)

for each cpd in cpd nodes:

get all rlt has_xref => update previous list of data with extract_entity_xref(cpd_xref_rlt)
fieldnames = “entity_xref”,”concerns@reaction”,”concerns@compound”,”has_xref@xref”
usage:
    padmet_to_tsv.py --padmetSpec=FILE [--padmetRef=FILE] --output_dir=DIR [-v]
    padmet_to_tsv.py --padmetRef=FILE [--padmetSpec=FILE] --output_dir=DIR [-v]

options:
    -h --help     Show help.
    --padmetSpec=FILE    path of the padmet representing the network to convert
    --padmetRef=FILE    path of the padmet representing the database
    --output_dir=DIR
    -v
padmet_utils.connection.padmet_to_tsv.main()[source]

pgdb_to_padmet

Description:

Read a PGDB folder (from BIOCYC/PATHWAYTOOLS) and create a padmet. 1./ To create a padmet without any genes information extracted use the first usage with:

pgdb: path to pgdb folder output: path to the padmet to create version: to specify the version of the pgdb (20.0, 22.0) db: to sepcify the name of the database (METACYC, ECOCYC, …) enhance: to also read the file metabolic-reaction.xml and add the to the padmet
2./ To create a padmet and add only reactions from pgdb if they are in padmetRef specifie.

Copy information of the reaction not from the pgdb but from the padmetRef. This allow to uniform reaction to the same version of metacyc represented in the padmetRef For example, in some case 2 pgdb from different version can contain different information for a same reaction,pathway… In this case use:

padmetRef: path to the padmet of reference
3./ To create a padmet wth genes information extracted use:
extract-gene
3.1/ To remove from the final padmet all reactions without genes associated use:
no-orphan
4./ To read the metabolic-reaction.xml file, a sbml with some missing reactions in PGDB use:
enhance

For more information of the parsing process read information below.

classes.dat: For each class: create new node / class = class UNIQUE-ID (1) => node.id = UNIQUE-ID COMMON-NAME (0-n) => node.Misc[‘COMMON-NAME’] = COMMON-NAME TYPES (0-n) => for each, check or create new node class, create rlt (node is_a_class types) SYNONYMS (0-n) => for each, create new node name, create rlt (node has_name synonyms)

compounds.dat: for each compound: create new node / class = compound UNIQUE-ID (1) => node.id = UNIQUE-ID COMMON-NAME (0-n) => node.Misc[‘COMMON-NAME’] = COMMON-NAME INCHI-KEY (0-1) {InChIKey=XXX} => node.misc[‘INCHI_KEY’: XXX] MOLECULAR-WEIGHT (0-1) => node.misc()[‘MOLECULAR_WEIGHT’] = MOLECULAR-WEIGHT SMILES (0-1) => node.misc()[‘SMILES’] = SMILES TYPES (0-n) => for each, check or create new node class, create rlt (node is_a_class types) SYNONYMS (0-n) => for each, create new node name, create rlt (node has_name name) DBLINKS (0-n) {(db “id” …)} => for each, create new node xref, create rlt (node has_xref xref)

proteins.dat: for each protein: create new node / class = protein UNIQUE-ID (1) => node.id = UNIQUE-ID COMMON-NAME (0-n) => node.Misc[‘COMMON-NAME’] = COMMON-NAME INCHI-KEY (0-1) {InChIKey=XXX} => node.misc[‘INCHI_KEY’: XXX] MOLECULAR-WEIGHT (0-1) => node.misc()[‘MOLECULAR_WEIGHT’] = MOLECULAR-WEIGHT SMILES (0-1) => node.misc()[‘SMILES’] = SMILES TYPES (0-n) => for each, check or create new node class, create rlt (node is_a_class types) SYNONYMS (0-n) => for each, create new node name, create rlt (node has_name name) DBLINKS (0-n) {(db “id” …)} => for each, create new node xref, create rlt (node has_xref xref) SPECIES (0-1) => for each, check or create new node class, create rlt (node is_in_species class)

reactions.dat: for each reaction: create new node / class = reaction + node.misc()[“DIRECTION”] = “UNKNOWN” by default UNIQUE-ID (1) => node.id = UNIQUE-ID COMMON-NAME (0-n) => node.Misc[‘COMMON-NAME’] = COMMON-NAME EC-NUMBER (0-n) => node.Misc[‘EC-NUMBER’] = EC-NUMBER REACTION-DIRECTION (0-1) => node.Misc[‘DIRECTION’] = reaction-direction, if REVERSIBLE, else: LEFT-TO-RIGHT RXN-LOCATIONS (0,n) => node.misc[‘COMPARTMENT’] = rxn-location TYPES (0-n) => check or create new node class, create rlt (node.id is_a_class types’s_node.id) DBLINKS (0-n) {(db “id” …)} => create new node xref, create rlt (node has_xref xref’s_node.id) SYNONYMS (0-n) => create new node name, create rlt (node has_name name’s_node.id) – for LEFT and RIGHT, also check 2 next lines if info about ‘coefficient’ or ‘compartment’ defaut value: coefficient/stoichiometry = 1, compartment = unknown also check if the direction is ‘RIGHT-TO-LEFT’, if yes, inverse consumes and produces relations then change direction to ‘LEFT-TO-RIGHT’ LEFT (1-n) => create rlt (node.id consumes left’s_node.id) RIGHT (1-n) => create rlt (node.id produces right’s_node.id)

enzrxns.dat: for each association enzyme/reaction: create new rlt / type = catalyses ENZYME (1) => stock enzyme as ‘enzyme catalyses’ REACTION (1-n) => for each reaction after, create relation ‘enzyme catalyses reaction’

pathways.dat: for each pathway: create new node / class = pathway UNIQUE-ID (1) => node._id = UNIQUE-ID TYPES (0-n) => check or create new node class, create rlt (node is_a_class types) COMMON-NAME (0-n) => node.Misc[‘COMMON-NAME’] = COMMON-NAME DBLINKS (0-n) {(db “id” …)} => create new node xref, create rlt (node has_xref xref) SYNONYMS (0-n) => create new node name, create rlt (node has_name name) IN-PATHWAY (0-n) => check or create new node pathway, create rlt (node is_in_pathway name) REACTION-LIST (0-n) => check or create new node pathway, create rlt (node is_in_pathway name)

usage:
    pgdb_to_padmet.py --pgdb=DIR --output=FILE [--version=V] [--db=ID] [--padmetRef=FILE] [--source=STR] [-v] [--enhance]
    pgdb_to_padmet.py --pgdb=DIR --output=FILE --extract-gene [--no-orphan]  [--version=V] [--db=ID] [--padmetRef=FILE] [--source=STR] [-v] [--enhance]

options:
    -h --help     Show help.
    --version=V    Xcyc version [default: N.A].
    --db=ID    Biocyc database corresponding to the pgdb (metacyc, ecocyc, ...) [default: N.A].
    --output=FILE    padmet file corresponding to the DB.
    --pgdb=DIR    directory containg all the .dat files of metacyc (data).
    --padmetRef=FILE    padmet of reference.
    --source=STR    Tag associated to the source of the reactions, used to ensure traceability [default: GENOME].
    --enhance    use the metabolic-reactions.xml file to enhance the database.
    --extract-gene    use the genes_file (use if its a specie's pgdb, if metacyc, do not use).
    --no-orhpan    use the genes_file (use if its a specie's pgdb, if metacyc, do not use).
    -v   print info.
padmet_utils.connection.pgdb_to_padmet.main()[source]

sbmlGenerator

Description:
The module sbmlGenerator contains functions to generate sbml files from padmet and txt usign the libsbml package
usage:
    sbmlGenerator.py --padmet=FILE --output=FILE --sbml_lvl=STR [--model_id=STR] [--obj_fct=STR] [--mnx_chem_prop=FILE] [--mnx_chem_xref=FILE] [-v]
    sbmlGenerator.py --padmet=FILE --output=FILE [--init_source=STR] [-v]
    sbmlGenerator.py --compound=FILE --output=FILE [--padmetRef=FILE] [-v]
    sbmlGenerator.py --reaction=FILE --output=FILE --padmetRef=FILE [-v]

option:
    -h --help    Show help.
    --padmet=FILE    path of the padmet file to convert into sbml
    --output=FILE    path of the sbml file to generate.
    --mnx_chem_prop=FILE    path of the MNX chemical compounds properties.
    --mnx_chem_xref=FILE    path of the mnx dict of chemical compounds id mapping.
    --reaction=FILE    path of file of reactions ids, one by line to convert to sbml.
    --compound=FILE    path of file of compounds ids, one by line to convert to sbml.
    --init_source=STR    Select the reactions of padmet to convert on sbml based on the source of the reactions, check relations rxn has_reconstructionData.
    --sbml_lvl=STR    sbml level of output. [default 3]
    --obj_fct=STR    id of the reaction objective.
    -v   print info.
padmet_utils.connection.sbmlGenerator.main()[source]

sbml_to_curation_form

Description:
extract 1 reaction (if rxn_id) or a list of reactions (if rxn_file) from a sbml file to the form used in aureme for curation. For example use this script to extract specific missing reaction of a model to a just created metabolic network.
usage:
    sbml_to_curation_form.py --sbml=FILE --output=FILE --rxn_id=ID [--comment=STR] [--extract-gene] [-v]
    sbml_to_curation_form.py --sbml=FILE --output=FILE --rxn_file=FILE [--comment=STR] [--extract-gene] [-v]

options:
    -h --help     Show help.
    --sbml=FILE    path of the sbml.
    --output=FILE    form containing the reaction extracted, form used for manual curation in aureme.
    --rxn_id=FILE    id of one reaction to extract
    --rxn_file=FILE    file of reactions ids to extract, 1 id by line.
    --extract-gene    If true, extract also genes associated to reactions.
    --comment=STR    comment associated to the reactions in the form. Used to track sources of curation in aureme [default: "N.A"].
    -v   print info
padmet_utils.connection.sbml_to_curation_form.main()[source]

sbml_to_padmet

Description:

There are 3 cases of convertion sbml to padmet:

1./ Creation of a reference database in padmet format from sbml(s) (or updating one with new(s) sbml(s)) First usage, padmetRef is the padmetRef to create or to update. If it’s an update case, the output can be used to create a new padmet, if output None, will overwritte the input padmetRef.

2./ Creation of a padmet representing an organism in padmet format from sbml(s) (or updating one with new(s) sbml(s)) 2.A/ Without a database of reference: Second usage, padmetSpec is the padmetSpec to create or update. If it’s an update case, the output can be used to create a new padmet, if output None, will overwritte the input padmetSpec.

2.B/ With a database of refence: Third usage, padmetSpec is the padmetSpec to create or update. If it’s an update case, the output can be used to create a new padmet, if output None, will overwritte the input padmetSpec. padmetRef is the padmet representing the database of reference.

It is possible to define a specific policy and info for the padmet. To learn more about policy and info check doc of lib.padmetRef/Spec. if the ids of reactions/compounds are not the same between padmetRef and the sbml, it is possible to use a dictionnary of association (sbml_id padmetRef_id) with one line = ‘id_sbml id_padmetRef’ Finally if a reaction from sbml is not in padmetRef, it is possible to force the copy and creating a new reaction in padmetSpec with the arg -f

usage:
    sbml_to_padmet.py --sbml=DIR/FILE --padmetRef=FILE [--output=FILE] [--db=STR] [--version=STR] [-v]
    sbml_to_padmet.py --sbml=DIR/FILE --padmetSpec=FILE [--output=FILE] [--source_tool=STR] [--source_category=STR] [--source_id=STR] [-v]
    sbml_to_padmet.py --sbml=DIR/FILE --padmetSpec=FILE  --padmetRef=FILE  [--mapping=DIR/FILE] [--mapping_tag=STR] [--output=FILE] [--source_tool=STR] [--source_category=STR] [--source_id=STR] [-v] [-f]

options:
    -h --help     Show help.
    --padmetSpec=FILE    path to the padmet file to update with the sbml. If there's no padmetSpec, just specify the output
    --padmetRef=FILE    path to the padmet file representing to the database of reference (ex: metacyc_18.5.padmet)
    --sbml=FILE    1 sbml file to convert into padmetSpec (ex: my_network.xml/sbml) OR a directory with n SBML
    --output=FILE   pathanme to the new padmet file
    --mapping=FILE    dictionnary of association id_origin id_ref
    --mapping_tag=STR    if sbml is a folder, use a tag to define mapping files ex: org1.sbml and org1_dict.csv, '_dict.csv' will be the mapping tag. [default: _dict.csv]
    --db=STR    database name
    --version=STR    database version
    -v   print info
padmet_utils.connection.sbml_to_padmet.main()[source]

wikiGenerator

Description:
Contains all necessary functions to generate wikiPages from a padmet file and update a wiki online. Require WikiManager module (with wikiMate,Vendor)
usage:
    wikiGenerator.py --padmet=FILE/DIR --output=DIR --wiki_id=STR [--database=STR] [--padmetRef=FILE] [--log_file=FILE] [-v]
    wikiGenerator.py --aureme_run=DIR --padmetSpec=ID -v

options:
    -h --help     Show help.
    --padmet=FILE    path to padmet file.
    --output=DIR    path to folder to create with all wikipages in subdir.
    --wiki_id=STR    id of the wiki.
    --padmetRef=FILE    path to padmet of reference, ex: metacyc_xx.padmet, if given, able to calcul pathway rate completion.
    --log_file=FILE    log file from an aureme run, use this file to create a wikipage with all the command used during the aureme run.
    --aureme_run=DIR    can use an aureme run as input, will use from config file information for model_id and log_file and padmetRef.
    -v    print info.
padmet_utils.connection.wikiGenerator.main()[source]