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.
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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:
check_orthology_input¶
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
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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:
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:
And the name of the studied organism ‘study_id’
Read the orthogroups file, extract orthogroups in dict ‘all_orthogroups’, and all org names
In orthology folder search for sbml files ‘extension = .sbml’
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
Store all previous dict_data in a list all_dict_data
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
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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 ids4./ 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]¶
-
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- 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:
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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- 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:
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.extract_rxn_with_gene_assoc(sbml_document, 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¶
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
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_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.asp_synt(pred, list_args)[source]¶ create a predicat for asp
example: asp_synt(“direction”,[“R1”,”REVERSIBLE”]) => “direction(‘R1’,’reversible’).”
Parameters: Returns: the predicat ‘pred(‘’list_args[0]’‘,’‘list_args[1]’‘,…,’‘list_args[n]’‘).’
Return type:
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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_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 = " ".
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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.
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_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.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’
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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
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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
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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
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padmet_utils.connection.padmet_to_tsv.pwy_rate(padmetRef, padmetSpec, metabolic_network, output)[source]¶ pwy rate in padmetSpec is calculated based on padmetRef
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.
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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:
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padmet_utils.connection.pgdb_to_padmet.compounds_parser(filePath, padmet, verbose=False)[source]¶ Parameters:
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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: Returns: padmet instance with pgdb within pgdb + metabolic-reactions.xml data
Return type: padmet.padmetRef
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padmet_utils.connection.pgdb_to_padmet.enzrxns_parser(filePath, padmet, dict_protein_gene_id, source, verbose=False)[source]¶ Parameters:
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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
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padmet_utils.connection.pgdb_to_padmet.genes_parser(filePath, padmet, verbose=False)[source]¶ Parameters:
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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.
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padmet_utils.connection.pgdb_to_padmet.pathways_parser(filePath, padmet, verbose=False)[source]¶ Parameters:
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padmet_utils.connection.pgdb_to_padmet.proteins_parser(filePath, padmet, verbose=False)[source]¶ Parameters:
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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:
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.
--obj_fct=STR id of the reaction objective.
-v print info.
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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
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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.
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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 @param compounds_file: the pathname to the file containing the compounds ids and the compart, line = cpd-id compart. @param output: the pathname to the sbml file to create @param padmetRef_file: the pathname to the file padmet of reference @param padmetRef_file: the pathname to the file padmet of a species @param compart_name: the default compart to concatenate @param sbml_version: the sbml version @param verbose: print informations @type compounds_file, output, padmetRef_file, padmetSpec_file, verbose: str @type sbml_lvl, sbml_version: int @return: check return of writeSBMLToFile @rtype: int
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padmet_utils.connection.sbmlGenerator.create_annotation(inchi, ref_id)[source]¶ dict_data, k = url, v = id
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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 @param padmet_file: the pathname to the padmet file to convert @param output: the pathname to the sbml file to create @param obj_fct: the identifier of the objection function, the reaction to test in FBA @param sbml_lvl: the sbml level @param sbml_version: the sbml version @param verbose: print informations @type padmet_file, output, verbose: str @type sbml_lvl, sbml_version: int @return: check return of writeSBMLToFile @rtype: int
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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 @param reactions: list of reactions ids @param padmetRef: padmet of reference @param output: the pathname to the sbml file to create @param sbml_lvl: the sbml level @param sbml_version: the sbml version @param verbose: print informations @type reactions: set @type output, verbose: str @type padmetRef: <Padmet> @type sbml_lvl, sbml_version: int @return: check return of writeSBMLToFile @rtype: int
sbml_to_curation_form¶
- Description:
- extract all reactions from a sbml file to the form used in aureme for curation.
usage:
sbml_to_curation_form.py --sbml=FILE --output=FILE --comment=STR [--rxn_id=ID]
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 or n reactions sep by ';', if None try to extract the reaction with objective coefficient == 1.
--comment=STR comment associated to the reactions in the form. Used to track sources of curation in aureme.
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=FILE --padmetRef=FILE [--output=FILE] [--db=STR] [--version=STR] [-v]
sbml_to_padmet.py --sbml=FILE --padmetSpec=FILE [--output=FILE] [--db=STR] [--version=STR] [-v]
sbml_to_padmet.py --sbml=FILE --padmetSpec=FILE [--padmetRef=FILE] [--output=FILE] [--mapping=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
--db=STR database name
--version=STR database version
-v print info
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.
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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
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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
#TODO
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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
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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:
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padmet_utils.connection.wikiGenerator.reduce_padmet_data(total_padmet_data, verbose=False)[source]¶ #TODO
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padmet_utils.connection.wikiGenerator.update_basic_attrib(node, current_node_dict, padmet_source)[source]¶ #TODO
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padmet_utils.connection.wikiGenerator.venn4(labels, names=['A', 'B', 'C', 'D'], **options)[source]¶ plots a 4-set Venn diagram
Parameters: Returns: (Figure, AxesSubplot), pyplot Figure and AxesSubplot object
Return type: