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

padmet.utils.connection.biggAPI_to_padmet.add_kegg_pwy(pwy_file, padmetRef, verbose=False)[source]

#TODO

padmet.utils.connection.biggAPI_to_padmet.biggAPI_to_padmet(output, pwy_file=None, verbose=False)[source]

Extract BIGG database using the api. Create a padmet file. Escape reactions of biomass. 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

Parameters:
  • output (str) – path to output, the padmet file.
  • pwy_file (str) – path to pathway file, add kegg pathways, line:’pwy_id, pwy_name, x, rxn_id’.
  • verbose (bool) – if True print information

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()

padmet.utils.connection.check_orthology_input.check_ids(model_metabolic, model_faa, cutoff, verbose=False)[source]

check if genes ids of model_metabolic = model_faa for a given cutoff faa genes ids are in the first line of each sequence: >GENE_ID …. metabolic netowkrs genes ids are in note section, GENE_ASSOCIATION: gene_id-1 or gene_id-2

Parameters:
  • model_metabolic (str) – path to sbml file
  • model_faa (str) – path to fasta faa file
  • cutoff (int) – cutoff genes ids from model found in faa
  • verbose (bool) – verbose
Returns:

True if same ids, if verbose, print % of genes under cutoff

Return type:

bool

padmet.utils.connection.check_orthology_input.check_orthology_input(model_metabolic, model_faa, dict_ids_file, output, verbose, cutoff)[source]

#TODO

padmet.utils.connection.check_orthology_input.get_valid_faa(model_faa, dict_ids_file, output)[source]

create a new faa from the model_faa by converting the gene id with the dict_ids dict_ids: line = origin_id new_gene_id, sep =

Parameters:
  • model_faa (str) – path to faa file
  • dict_ids_file (str) – path to file containing link old to new ids
  • output (str) – path to new faa file

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)

padmet.utils.connection.enhanced_meneco_output.enhanced_meneco_output(meneco_output_file, padmetRef, output, verbose=False)[source]

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 for manual_curation 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)

Parameters:
  • meneco_output_file (str) – pathname of a meneco run’ result
  • padmetRef (padmet.padmetRef) – path to padmet file corresponding to the database of reference (the repair network)
  • output (str) – path to tsv output file
  • verbose (bool) – if True print information

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.

padmet.utils.connection.extract_orthofinder.dict_data_to_sbml(dict_data, dict_orthogroups=None, dict_orthologues=None, strict_match=True)[source]

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 1./ Read dict_orthogroups and check if model associated to dict_data and study org share orthologue 2./ Read sbml of model, parse all reactions and get genes associated to reaction. 3./ 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

4./ Create the new sbml file.

Parameters:
  • dict_data (dict) – {‘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 }
  • dict_orthogroup (dict) – k=orthogroup_id, v = {k = name, v = set of genes}
  • verbose (bool) – if True print information
padmet.utils.connection.extract_orthofinder.get_sbml_files(sbml, workflow=None, verbose=False)[source]

#TODO

padmet.utils.connection.extract_orthofinder.orthogroups_to_sbml(orthogroups_file, all_model_sbml, output_folder, study_id, verbose=False)[source]

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
  1. 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 This function will create a sbml from each model and conserve only reactions associated to ortholog genes For more information read the doc of func dict_data_to_sbml

Parameters:
  • orthogroups_file (str) – path of Orthofinder output file ‘Orthogroups.csv’
  • orthology_based_folder (str) – path of folder with model’s sbml
  • output (str) – pathname of the output folder of all sbml extracted
  • study_id (str) – name of the studied organism
  • verbose (bool) – if True print information
padmet.utils.connection.extract_orthofinder.orthologue_to_sbml(orthologue_folder, all_model_sbml, output_folder, study_id, verbose=False)[source]

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
  1. 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 This function will create a sbml from each model and conserve only reactions associated to ortholog genes For more information read the doc of func dict_data_to_sbml

Parameters:
  • orthogroups_file (str) – path of Orthofinder output file ‘Orthogroups.csv’
  • orthology_based_folder (str) – path of folder with model’s sbml
  • output (str) – pathname of the output folder of all sbml extracted
  • study_id (str) – name of the studied organism
  • verbose (bool) – if True print information

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’: ….

padmet.utils.connection.extract_rxn_with_gene_assoc.extract_rxn_with_gene_assoc(sbml, output, verbose=False)[source]

From a given sbml document, create a sbml with only the reactions associated to a gene. Need for a reaction, in section ‘note’, ‘GENE_ASSOCIATION’: ….

Parameters:
  • sbml_file (libsbml.document) – sbml document
  • output (str) – pathname of the output sbml

gbk_to_faa

Description:
convert GBK to FAA with Bio package
padmet.utils.connection.gbk_to_faa.gbk_to_faa(gbk_file, output, qualifier='locus_tag', verbose=True)[source]

convert GBK to FAA with Bio package

Parameters:
  • gbk_file (str) – path to the gbk file
  • output (str) – path to the output, a FAA file
  • qualifier (str) – he qualifier of the gene id
  • verbose (bool) – if True print information

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

padmet.utils.connection.gene_to_targets.gene_to_targets(padmet, genes_file, output, verbose=False)[source]

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

Parameters:
  • padmet (padmet.classes.PadmetSpec) – padmet to explore
  • genes_file (str) – path of genes file, 1 gene id by line
  • output (str) – pathname of the output file
  • verbose (bool) – if True print information

modelSeed_to_padmet

Description:
#TODO
padmet.utils.connection.modelSeed_to_padmet.add_kegg_pwy(pwy_file, padmetRef, verbose=False)[source]

#TODO

padmet.utils.connection.modelSeed_to_padmet.modelSeed_to_padmet(rxn_file, pwy_file, output, verbose=False)[source]

#TODO

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).
padmet.utils.connection.padmet_to_asp.asp_synt(pred, list_args)[source]

create a predicat for asp

example: asp_synt(“direction”,[“R1”,”REVERSIBLE”]) => “direction(‘R1’,’reversible’).”

Parameters:
  • pred (str) – the predicat
  • list_args (list) – list of atoms to put in the predicat
Returns:

the predicat ‘pred(‘’list_args[0]’‘,’‘list_args[1]’‘,…,’‘list_args[n]’‘).’

Return type:

str

padmet.utils.connection.padmet_to_asp.padmet_to_asp(padmet_file, output, verbose=False)[source]

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).

Parameters:
  • padmet_file (str) – the path to padmet file to convert
  • output (str) – the path to the output to create
  • verbose (bool) – print informations

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’.

padmet.utils.connection.padmet_to_matrix.padmet_to_matrix(padmet, output)[source]

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’.

Parameters:
  • padmet (padmet.PadmetSpec) – padmet instance
  • output – path to the output file, col: rxn, row: metabo, sep = ” “

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.

padmet.utils.connection.padmet_to_padmet.padmet_to_padmet(to_add, output, padmetRef=None, verbose=False)[source]

#TODO

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
padmet.utils.connection.padmet_to_tsv.entity_xref_file(data, output)[source]

#TODO

padmet.utils.connection.padmet_to_tsv.extract_entity_xref(entity_xref_rlt, padmet)[source]

#TODO

padmet.utils.connection.padmet_to_tsv.extract_nodes(padmet, nodes, entity_id, output, opt_col={})[source]

for n nodes in nodes. for each node.misc = {A:[‘x’],B:[‘y’,’z’]} create a file with line = [node.id,A[0],B[0]],[node.id,”“,B[1]] the order is defined in fieldnames. merge common name and synonyms in ‘name’

#TODO

padmet.utils.connection.padmet_to_tsv.extract_pwy(padmet)[source]

from padmet return a dict, k = pwy_id, v = set of rxn_id in pwy

#TODO

padmet.utils.connection.padmet_to_tsv.extract_rxn_cpd(rxn_cpd_rlt)[source]

for rlt in rxn_cpd_rlt, append in data: [rxn_id,cpd_id(consumed),’‘,stoich,compartment] and/or [rxn_id,’‘,cpd_id(produced),stoich,compartment]. The value in index 0 is a merge of all data to create a unique relation id

#TODO

padmet.utils.connection.padmet_to_tsv.extract_rxn_gene(rxn_gene_rlt)[source]

#TODO

padmet.utils.connection.padmet_to_tsv.extract_rxn_pwy(rxn_pwy_rlt)[source]

for rlt in rxn_pwy_rlt, append in data: [rxn_id,pwy_id]. The value in index 0 is a merge of all data to create a unique relation id

#TODO

padmet.utils.connection.padmet_to_tsv.extract_rxn_rec(rxn_rec_rlt)[source]

#TODO

padmet.utils.connection.padmet_to_tsv.padmet_to_tsv(padmetSpec_file, padmetRef_file, output_dir, verbose=False)[source]

#TODO

padmet.utils.connection.padmet_to_tsv.pwy_rate(padmetRef, padmetSpec, metabolic_network, output)[source]

pwy rate in padmetSpec is calculated based on padmetRef

#TODO

padmet.utils.connection.padmet_to_tsv.rxn_cpd_file(data, output)[source]

from data obtained with extract_rxn_cpd(), create file rxn_cpd

#TODO

padmet.utils.connection.padmet_to_tsv.rxn_gene_file(data, output)[source]

#TODO

padmet.utils.connection.padmet_to_tsv.rxn_pwy_file(data, output)[source]

#TODO

padmet.utils.connection.padmet_to_tsv.rxn_rec_file(data, output)[source]

#TODO

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)

padmet.utils.connection.pgdb_to_padmet.classes_parser(filePath, padmet, verbose=False)[source]

from class.dat: get for each class, the UNIQUE-ID, COMMON-NAME, TYPES, SYNONYMS, DBLINKS Create a class node with node.id = UNIQUE-ID, node.misc = {COMMON-NAME:[COMMON-NAMES]} - For each types: A type is in fact a class. this information is stocked in padmet as: is_a_class relation btw a node and a class_node check if the type is already in the padmet if not create a new class_node (var: subClass) with subClass_node.id = type Create a relation current node is_a_class type - For each Synonyms: this information is stocked in padmet as: has_name relation btw a node and a name_node create a new name_node with name_node.id = class_id+”_names” and name_node.misc = {LABEL:[synonyms]} Create a relation current node has_name name_node.id - For each DBLINKS: DBLINKS is parsed with regex_xref to get the db and the id this information is stocked in padmet as: has_xref relation btw a node and a xref_node create a new xref_node with xref_node.id = class_id+”_xrefs” and xref_node.misc = {db:[id]} Create a relation current node has_xref xref_node.id

Parameters:
  • filePath (str) – path to classes.dat
  • padmet (padmet.PadmetRef) – padmet instance
  • verbose (bool) – if True print information
padmet.utils.connection.pgdb_to_padmet.compounds_parser(filePath, padmet, verbose=False)[source]
Parameters:
  • filePath (str) – path to compounds.dat
  • padmet (padmet.PadmetRef) – padmet instance
  • verbose (bool) – if True print information
padmet.utils.connection.pgdb_to_padmet.enhance_db(metabolic_reactions, padmet, with_genes, verbose=False)[source]

Parse sbml metabolic_reactions and add reactions in padmet if with_genes: add also genes information

Parameters:
  • metabolic_reactions (str) – path to sbml metabolic-reactions.xml
  • padmet (padmet.PadmetRef) – padmet instance
  • with_genes (bool) – if true alos add genes information.
Returns:

padmet instance with pgdb within pgdb + metabolic-reactions.xml data

Return type:

padmet.padmetRef

padmet.utils.connection.pgdb_to_padmet.enzrxns_parser(filePath, padmet, dict_protein_gene_id, source, verbose=False)[source]
Parameters:
  • filePath (str) – path to enzrxns.dat
  • padmet (padmet.PadmetRef) – padmet instance
  • verbose (bool) – if True print information
padmet.utils.connection.pgdb_to_padmet.from_pgdb_to_padmet(pgdb_folder, db='NA', version='NA', source='GENOME', extract_gene=False, no_orphan=False, enhanced_db=False, padmetRef_file=None, verbose=False)[source]
Parameters:
  • pgdb_folder (str) – path to pgdb
  • db (str) – pgdb name, default is ‘NA’
  • version (str) – pgdb version, default is ‘NA’
  • source (str) – tag reactions for traceability, default is ‘GENOME’
  • extract_gene (bool) – if true extract genes information
  • no_orphan (bool) – if true, remove reactions without genes associated
  • enhanced_db (bool) – if true, read metabolix-reactions.xml sbml file and add information in final padmet
  • padmetRef_file (str) – path to padmetRef corresponding to metacyc in padmet format
  • verbose (bool) – if True print information
Returns:

padmet instance with pgdb within pgdb data

Return type:

padmet.padmetRef

padmet.utils.connection.pgdb_to_padmet.genes_parser(filePath, padmet, verbose=False)[source]
Parameters:
  • filePath (str) – path to genes.dat
  • padmet (padmet.PadmetRef) – padmet instance
  • verbose (bool) – if True print information
padmet.utils.connection.pgdb_to_padmet.map_gene_id(dict_protein_gene_id, map_gene_ids)[source]

Map gene ID created by Pathway Tools with gene ID from the data. Automatically Pathway Tools uppercased all the letter in gene ID. So we need to do this mapping to retrieve the unuppercased gene ID.

padmet.utils.connection.pgdb_to_padmet.pathways_parser(filePath, padmet, verbose=False)[source]
Parameters:
  • filePath (str) – path to pathways.dat
  • padmet (padmet.PadmetRef) – padmet instance
  • verbose (bool) – if True print information
padmet.utils.connection.pgdb_to_padmet.proteins_parser(filePath, padmet, verbose=False)[source]
Parameters:
  • filePath (str) – path to proteins.dat
  • padmet (padmet.PadmetRef) – padmet instance
  • verbose (bool) – if True print information
padmet.utils.connection.pgdb_to_padmet.reactions_parser(filePath, padmet, extract_gene, source, verbose=False)[source]

from reaction.dat: get for each reaction, the UNIQUE-ID, COMMON-NAME, TYPES, SYNONYMS, DBLINKS Create a reaction node with node.id = UNIQUE-ID, node.misc = {COMMON-NAME:[COMMON-NAMES]} - For each types: A type is in fact a class. this information is stocked in padmet as: is_a_class relation btw a node and a class_node check if the type is already in the padmet if not create a new class_node (var: subClass) with subClass_node.id = type Create a relation current node is_a_class type - For each Synonyms: this information is stocked in padmet as: has_name relation btw a node and a name_node create a new name_node with name_node.id = reaction_id+”_names” name_node.misc = {LABEL:[synonyms]} Create a relation current node has_name name_node.id - For each DBLINKS: DBLINKS is parsed with regex_xref to get the db and the id this information is stocked in padmet as: has_xref relation btw a node and a xref_node create a new xref_node with xref_node.id = reaction_id+”_xrefs” and xref_node.misc = {db:[id]} Create a relation current node has_xref xref_node.id

Parameters:
  • filePath (str) – path to reactions.dat
  • padmet (padmet.PadmetRef) – padmet instance
  • verbose (bool) – if True print information

sbmlGenerator

Description:
The module sbmlGenerator contains functions to generate sbml files from padmet and txt usign the libsbml package
padmet.utils.connection.sbmlGenerator.add_ga(rId_encoded, all_ga_subsets)[source]

if list_ga len == 1: only 1 list of gene: if len of this list is 1: just add gene, else create OR structure else: create OR structure, then for each list of gene for each ga in list_ga: if len == 1: if the only ga len == 1: just add gene, else create OR structure elif len > 1: create AND structure, then for each GA if len GA == 1: just add gene, else create OR structure if no suppdata, if linked_genes: if len linked_genes == 1: just add gene, else create OR structure #TODO

padmet.utils.connection.sbmlGenerator.check(value, message)[source]

If ‘value’ is None, prints an error message constructed using ‘message’ and then exits with status code 1. If ‘value’ is an integer, it assumes it is a libSBML return status code. If the code value is LIBSBML_OPERATION_SUCCESS, returns without further action; if it is not, prints an error message constructed using ‘message’ along with text from libSBML explaining the meaning of the code, and exits with status code 1.

padmet.utils.connection.sbmlGenerator.compound_to_sbml(species_compart, output, verbose=False)[source]

convert a list of compounds to sbml format if compart_name is not None, then the compounds id will by: M_originalID_compart_name if verbose and specified padmetRef and/or padmetSpec: will check if compounds are in one of the padmet files Ids are encoded for sbml using functions sbmlPlugin.convert_to_coded_id

Parameters:
  • species_file (str) – pathname to the file containing the compounds ids and the compart, line = cpd-id compart.
  • output (str) – pathname to the sbml file to create
  • verbose (bool) – print informations
padmet.utils.connection.sbmlGenerator.create_annotation(inchi, ref_id)[source]

dict_data, k = url, v = id #TODO

padmet.utils.connection.sbmlGenerator.create_note(dict_data)[source]

#TODO

padmet.utils.connection.sbmlGenerator.from_init_source(padmet_file, init_source, output, verbose=False)[source]

#TODO

padmet.utils.connection.sbmlGenerator.padmet_to_sbml(padmet, output, model_id=None, obj_fct=None, sbml_lvl=3, mnx_chem_prop=None, mnx_chem_xref=None, verbose=False)[source]

Convert padmet file to sbml file. Specificity: - ids are encoded for sbml using functions sbmlPlugin.convert_to_coded_id

Parameters:
  • padmet (str or padmet.classes.PadmetSpec/PadmetRef) – the pathname to the padmet file to convert, or PadmetSpec/PadmetRef object
  • output (str) – the pathname to the sbml file to create
  • model_id (str or None) – model id to set in sbml file
  • obj_fct (str) – the identifier of the objection function, the reaction to test in FBA
  • sbml_lvl (int) – the sbml level
  • sbml_version (int) – the sbml version
  • verbose (bool) – print informations
padmet.utils.connection.sbmlGenerator.parse_mnx_chem_prop(mnx_chem_prop)[source]

#TODO

padmet.utils.connection.sbmlGenerator.parse_mnx_chem_xref(mnx_chem_xref)[source]

#TODO

padmet.utils.connection.sbmlGenerator.reaction_to_sbml(reactions, output, padmetRef, verbose=False)[source]

convert a list of reactions to sbml format based on a given padmet of reference. - ids are encoded for sbml using functions sbmlPlugin.convert_to_coded_id

Parameters:
  • reactions (list) – list of reactions ids
  • padmetRef (padmet.classes.PadmetRef) – padmet of reference
  • output (str) – the pathname to the sbml file to create

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.
padmet.utils.connection.sbml_to_curation_form.sbml_to_curation(sbml_file, rxn_list, output, extract_gene=False, comment='N.A', verbose=False)[source]

Read a sbml file, check if each reaction ids are in the sbml, if no, raise ValueError Then create the form. this form can then be used with manual_curation.py

Parameters:
  • sbml_file (str) – path to sbml file
  • rxn_list (list) – list of reaction id, ids must be identic as in the sbml, carrefull to encoded ids.
  • output (str) – path to the form to create
  • extract_gene (bool) – if true extract genes association
  • comment (str) – Comment why the reaction will be added in the network for traceability.
  • verbose (bool) – if True print information

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

padmet.utils.connection.sbml_to_padmet.create_padmet_instance(padmet_file, padmet_type, db, version, padmetRef=None)[source]

#TODO

padmet.utils.connection.sbml_to_padmet.sbml_to_padmetRef(sbml, padmetRef_file, output=None, db='NA', version='NA', verbose=False)[source]
if padmetRef, not padmetSpec:
if padmetRef exist, instance PadmetRef else init PadmetRef update padmetRef
if padmetSpec:
if padmetRef, check if exist else raise Error if padmetSpec exist, instance PadmetSpec else init PadmetSpec update padmetSpec using padmetRef if padmetRef

#TODO

padmet.utils.connection.sbml_to_padmet.sbml_to_padmetSpec(sbml, padmetSpec_file, padmetRef_file=None, output=None, mapping=None, mapping_tag='_dict.csv', source_tool=None, source_category=None, db='NA', version='NA', verbose=False)[source]

Convert 1 - n sbml to padmet file. sbml var is file or dir padmetSpec_file: path to new padmet file to create or old padmet to update padmetRef_file: path to database of reference to use for data standardization output: path to new padmet file, if none, overwritte padmetSpec_file source_tool: tool used to create this sbml(s) ex Orthofinder source_category: Category of the tool ex: orthology if new padmet without padmetRef:

db: database used ex: metacyc, bigg version: version of the database, 23, 18…
if padmetRef, not padmetSpec:
if padmetRef exist, instance PadmetRef else init PadmetRef update padmetRef
if padmetSpec:
if padmetRef, check if exist else raise Error if padmetSpec exist, instance PadmetSpec else init PadmetSpec update padmetSpec using padmetRef if padmetRef

#TODO

sbml_to_sbml

Description:
Create sbml from sbml. Use it to change sbml level.
padmet.utils.connection.sbml_to_sbml.from_sbml_to_sbml(input_sbml, output_sbml, new_sbml_level, cpu=1)[source]

Turn sbml to sbml.

Parameters:
  • input_sbml (str) – pathname to species sbml file/folder
  • output_sbml (str) – pathname to output sbml file/folder
  • new_sbml_level (int) – new sbml level
  • cpu (int) – number of cpu
Returns:

pathname to output sbml file/folder

Return type:

str

padmet.utils.connection.sbml_to_sbml.run_sbml_to_sbml(multiprocess_data)[source]

Turn sbml to sbml.

Parameters:multiprocess_data (dict) – pathname to species sbml file, pathname to output sbml file, new sbml level
Returns:True if sbml file exists
Return type:bool
padmet.utils.connection.sbml_to_sbml.sbml_to_padmet(sbml, db, version, source_tool, source_category, source_id, mapping, verbose)[source]

#TODO

wikiGenerator

Description:
Contains all necessary functions to generate wikiPages from a padmet file and update a wiki online. Require WikiManager module (with wikiMate,Vendor)
padmet.utils.connection.wikiGenerator.add_collapsible(text_array, title=None)[source]

#TODO

padmet.utils.connection.wikiGenerator.add_property(properties, prop_id, prop_values)[source]

#TODO

padmet.utils.connection.wikiGenerator.copy_io_files()[source]
padmet.utils.connection.wikiGenerator.createDirectory(output, verbose=False)[source]

create the folders genes, reactions, metabolites, pathways in the folder dirPath/ if already exist, it will replace old folders (and delete old files)

Parameters:output (str) – path to output folder
padmet.utils.connection.wikiGenerator.create_biological_page(category, page_id, page_dict_data, total_padmet_data, ext_link, output_file, padmetRef=None, verbose=False)[source]

#TODO

padmet.utils.connection.wikiGenerator.create_log_page(log_file, output_folder)[source]

#TODO

padmet.utils.connection.wikiGenerator.create_main(total_padmet_data, wiki_id, output_file, verbose=False)[source]

#TODO

padmet.utils.connection.wikiGenerator.create_navigation_page(total_padmet_data, navigation_folder, verbose=False)[source]

#TODO

padmet.utils.connection.wikiGenerator.create_venn(total_padmet_data, output_file, verbose=False)[source]

#TODO

padmet.utils.connection.wikiGenerator.draw_ellipse(fig, ax, x, y, w, h, a, fillcolor)[source]
padmet.utils.connection.wikiGenerator.draw_text(fig, ax, x, y, text, color=[0, 0, 0, 1])[source]
padmet.utils.connection.wikiGenerator.extract_padmet_data(padmetFile, total_padmet_data, global_pwy_rxn_dict=None, padmetRef=None, verbose=False)[source]

total_padmet_data: k in [‘reaction’, ‘gene’, ‘organism’, ‘pathway’, …] if k = ‘reaction’, v = {‘misc’:{},’gene_assoc’:}

For reaction in padmetFile:
if reaction_id not in total_padmet_data[“reaction”].keys():
add total_padmet_data[“reaction”][reaction_id][padmet_source] = dict()

else, add data only if differents from first

#TODO

padmet.utils.connection.wikiGenerator.get_cmd_label(cmd)[source]

#TODO

padmet.utils.connection.wikiGenerator.get_labels(data, fill=['number'])[source]

get a dict of labels for groups in data example: In [12]: get_labels([range(10), range(5,15), range(3,8)], fill=[“number”]) Out[12]: {‘001’: ‘0’, ‘010’: ‘5’, ‘011’: ‘0’, ‘100’: ‘3’, ‘101’: ‘2’, ‘110’: ‘2’, ‘111’: ‘3’}

Parameters:
  • data (list) – data to get label for
  • fill – [“number”|”logic”|”percent”]
Returns:

a dict of labels for different sets

Return type:

dict

padmet.utils.connection.wikiGenerator.reduce_padmet_data(total_padmet_data, verbose=False)[source]

#TODO

padmet.utils.connection.wikiGenerator.update_basic_attrib(node, current_node_dict, padmet_source)[source]

#TODO

padmet.utils.connection.wikiGenerator.venn4(labels, names=['A', 'B', 'C', 'D'], **options)[source]

plots a 4-set Venn diagram

Parameters:
  • labels (dict) – a label dict where keys are identified via binary codes (‘0001’, ‘0010’, ‘0100’, …), hence a valid set could look like: {‘0001’: ‘text 1’, ‘0010’: ‘text 2’, ‘0100’: ‘text 3’, …}. unmentioned codes are considered as ‘’.
  • names (list) – group names
Returns:

(Figure, AxesSubplot), pyplot Figure and AxesSubplot object

Return type:

set

padmet.utils.connection.wikiGenerator.wikiGenerator(padmet, output, wiki_id, padmetRef=None, database=None, log_file=None, verbose=False)[source]

#TODO

#TODO