AuReMe/Ontosunburst

Sunburst representation of ontologies

Ontosunburst

Sunburst visualisation of an ontology representing classes of sets of metabolic objects

Requirements

Mandatory

Python 3.10 recommended

Requirements from requirements.txt

• numpy>=1.22.0
• padmet>=5.0.1
• plotly>=5.17.0
• scipy>=1.8.1
• SPARQLWrapper>=2.0.0

Optional

Need Apache Jena Fuseki SPARQL server for ChEBI and GO requests and their OWL files.

• Download Apache Jena Fuseki : https://jena.apache.org/download/index.cgi
• Download ChEBI ontology : https://ftp.ebi.ac.uk/pub/databases/chebi/ontology/ (chebi.owl or chebi_lite.owl)
• Download GO ontology : https://geneontology.org/docs/download-ontology/ (go.owl)

Installation

pip install -r requirements.txt
pip install -e .

Set up Jena SPARQL server (optional : for ChEBI and GO)

Execute followed bash script to launch server.

ChEBI

#!/bin/bash

FUSEKI_PATH=/path/to/apache-jena-fuseki-x.x.x
CHEBI_PATH=/path/to//chebi_lite.owl

${FUSEKI_PATH}/fuseki-server --file=${CHEBI_PATH} /chebi

GO

#!/bin/bash

FUSEKI_PATH=/path/to/apache-jena-fuseki-x.x.x
GO_PATH=/path/to/go.owl

${FUSEKI_PATH}/fuseki-server --file=${GO_PATH} /go

Utilisation

Availabilities

5 Ontologies :

With local files :

• MetaCyc (compounds, reactions, pathways)
• EC (EC-numbers)
• KEGG Ontology (modules, pathways, ko, ko_transporter, metabolite, metabolite_lipid)

With SPARQL server :

• ChEBI (chebi roles)
• Gene Ontology (works well only with small set)

2 Analysis :

• Topology (1 set + 1 optional reference set) : displays proportion (number of occurrences) representation of all classes
• Enrichment (1 set + 1 reference set) : displays enrichment analysis significance of a set according to a reference set of metabolic objects

Inputs

Objects sets

A python collection (list, set, ...) of metabolic objects IDs

Metabolic objects from some tools outputs can be extracted easily with functions from obj_extraction

Ontology

1. Files (MetaCyc, EC, KEGG)

Intern tool files (in Inputs folder) by default

Specified user local files

• MetaCyc (Classes ontology json file)
• EC (Classes ontology json file + Names json file)
• KEGG (Classes ontology json file)

2. SPARQL server url (ChEBI, GO)

See Set up Jena SPARQL server (optional : for ChEBI and GO) for install guide.

Main workflow function : ontosunburst.ontosunburst

Parameters

---------- REQUIRED ----------

ontology : str

• Ontology to use, must be in : [metacyc, ec, kegg, chebi, go]

metabolic_objects : Collection[str]

• Set of metabolic objects to classify

---------- OPTIONAL ----------

reference_set : Collection[str] (optional, default=None)

• Set of reference metabolic objects

analysis : str (optional, default=topology)

• Analysis mode, must be in : `[topology, enrichment]

output : str (optional, default=None)

• Path to output to save figure

class_file : str (optional, default=None)

• Path to class ontology file

names_file : str (optional, default=None)

• Path to EC_ID - EC_NAME association json file

endpoint_url : str (optional, default=None)

• URL of ChEBI ontology for SPARQL requests

test : str (optional, default=binomial)

• Type of test if analysis=enrichment, must be in : [binomial, hypergeometric]

full : bool (optional, default=True)

• True to duplicate labels if +1 parents (False to take exactly 1 random parent)

total : bool (optional, default=True)

• True to have branch values proportional of the total parent (may not work in some cases)

root_cut : str (optional, default=ROOT_CUT)

• mode for root cutting (uncut, cut, total)

ref_base : bool (optional, default=False)

• True to have the base classes representation of the reference set in the figure.

show_leaves : bool (optional, default=False)

• True to show input metabolic objets at sunburst leaves

MetaCyc

Example

from ontosunburst.ontosunburst import ontosunburst

MET_SET = {'CPD-24674', 'CPD-24687', 'CPD-24688'}
REF_MET = {'CPD-24674', 'CPD-24687', 'CPD-24688',
           'CPD-12782', 'CPD-12784', 'CPD-12787',
           'CPD-12788', 'CPD-12789', 'CPD-12796',
           'CPD-12797', 'CPD-12798', 'CPD-12805',
           'CPD-12806', 'CPD-12812', 'CPD-12816',
           'CPD-1282', 'CPD-12824', 'CPD-1283'}

# TOPOLOGY
fig = ontosunburst(ontology='metacyc', 
                   metabolic_objects=MET_SET, 
                   reference_set=REF_MET,
                   output='test_mc_cpd_prop', 
                   ref_base=True)

# ENRICHMENT
fig = ontosunburst(ontology='metacyc', 
                   metabolic_objects=MET_SET, 
                   reference_set=REF_MET,
                   analysis='enrichment', 
                   output='test_mc_cpd_comp', 
                   ref_base=True)

EC

Example

from  ontosunburst.ontosunburst import ontosunburst

EC_SET = {'2.6.1.45', '1.1.1.25', '1.1.1.140'}
REF_EC = {'2.6.1.45', '1.1.1.25', '1.1.1.140',
          '1.14.14.52', '2.7.1.137', '7.1.1.8',
          '1.17.4.5', '2.3.1.165', '3.2.1.53',
          '3.2.1.91', '6.3.4.2', '5.4.99.8'}

# TOPOLOGY
fig = ontosunburst(ontology='ec', 
                   metabolic_objects=EC_SET, 
                   reference_set=REF_EC,
                   output='test_ec_prop', 
                   ref_base=True, 
                   show_leaves=True)

# ENRICHMENT
fig = ontosunburst(ontology='ec', 
                   metabolic_objects=EC_SET, 
                   reference_set=REF_EC,
                   output='test_ec_comp', 
                   analysis='enrichment', 
                   ref_base=True)

ChEBI

Example

from  ontosunburst.ontosunburst import ontosunburst

URL = 'http://localhost:3030/chebi/'
CH_SET = {'38028', '28604', '85146'}
REF_CH = {'38028', '28604', '85146',
          '23066', '27803', '37565',
          '58215', '79983', '42639'}

# TOPOLOGY
fig = ontosunburst(ontology='chebi', 
                   metabolic_objects=CH_SET, 
                   reference_set=REF_CH,
                   endpoint_url=URL, 
                   output='test_chebi_prop', 
                   ref_base=True)

# ENRICHMENT
fig = ontosunburst(ontology='chebi', 
                   metabolic_objects=CH_SET, 
                   reference_set=REF_CH,
                   endpoint_url=URL, 
                   output='test_chebi_comp', 
                   analysis='enrichment', 
                   ref_base=True)