def test_consensusEcGraph_difference(self):
FEV_KEGG.startProcessPool()
enterobacteriales_organisms_abbreviations = [
'eco', 'ses', 'sfl', 'ent', 'esa', 'kpn', 'cko', 'ype', 'spe',
'buc'
]
enterobacteriales_organisms = Organism.Group(
organismAbbreviations=enterobacteriales_organisms_abbreviations)
enterobacteriales_organisms_abbreviations = [
'eco', 'ses', 'sfl', 'ent', 'esa', 'kpn', 'cko', 'ype', 'spe',
'buc'
]
gammaproteobacteria_organisms_abbreviations = [
'hin', 'mht', 'xcc', 'vch', 'pae', 'acb', 'son', 'pha', 'amc',
'lpn', 'ftu', 'aha'
]
gammaproteobacteria_organisms_abbreviations.extend(
enterobacteriales_organisms_abbreviations
) # extend with the sub-set, because they are also part of the set
gammaproteobacteria_organisms = Organism.Group(
organismAbbreviations=gammaproteobacteria_organisms_abbreviations)
enterobacteriales_EC_graph = enterobacteriales_organisms.consensusEcGraph(
noMultifunctional=True)
gammaproteobacteria_EC_graph = gammaproteobacteria_organisms.consensusEcGraph(
noMultifunctional=True)
enterobacteriales_EC_set = enterobacteriales_EC_graph.getECs()
gammaproteobacteria_EC_set = gammaproteobacteria_EC_graph.getECs()
only_enterobacteriales_EC_set = enterobacteriales_EC_set.difference(
gammaproteobacteria_EC_set)
output = []
for ec in only_enterobacteriales_EC_set:
output.append(ec.__str__())
result = len(output)
print(str(result) + ' results')
self.assertEqual(result, 87)
enterobacteriales_enzyme_graph = enterobacteriales_organisms.collectiveEnzymeGraphByEcConsensus(
noMultifunctional=True)
gammaproteobacteria_enzyme_graph = gammaproteobacteria_organisms.collectiveEnzymeGraphByEcConsensus(
noMultifunctional=True)
enterobacteriales_enzyme_graph.removeMultifunctionalEnzymes()
gammaproteobacteria_enzyme_graph.removeMultifunctionalEnzymes()
enterobacteriales_enzymes = enterobacteriales_enzyme_graph.getEnzymes()
gammaproteobacteria_enzymes = gammaproteobacteria_enzyme_graph.getEnzymes(
)
enterobacteriales_EC_set_2 = set()
for enzyme in enterobacteriales_enzymes:
ecNumbers = enzyme.ecNumbers
enterobacteriales_EC_set_2.update(ecNumbers)
gammaproteobacteria_EC_set_2 = set()
for enzyme in gammaproteobacteria_enzymes:
ecNumbers = enzyme.ecNumbers
gammaproteobacteria_EC_set_2.update(ecNumbers)
only_enterobacteriales_EC_set_2 = enterobacteriales_EC_set_2.difference(
gammaproteobacteria_EC_set_2)
output = []
for ec in only_enterobacteriales_EC_set_2:
output.append(ec.__str__())
result2 = len(output)
print(str(result2) + ' results')
self.assertEqual(result2, result)
output = []
for ec in only_enterobacteriales_EC_set_2.symmetric_difference(
only_enterobacteriales_EC_set):
output.append(ec.__str__())
result3 = len(output)
print(str(result3) + ' results')
self.assertEqual(result3, 0)
for ecString in output:
print(ecString)
'pha', 'pin', 'plu', 'ppr', 'rma', 'saz', 'sde', 'sdn', 'shm',
'tcx', 'vfi', 'vvu', 'xca'
]
organisms = representativeOrganisms
output.append('Representative:')
elif i == 2:
#- 2. get group of organisms 'Gammaproteobacteria', excluding unclassified
organisms = taxonomy.getOrganismAbbreviationsByPath(
'Gammaproteobacteria',
exceptPaths='unclassified',
oneOrganismPerSpecies=False)
output.append('\nGammaproteobacteria without unclassified:')
group = Organism.Group(organisms)
#- REPEAT for varying majority-percentages:
for percentage in [100, 90, 80, 70, 60, 50, 40, 30, 20, 10, 1]:
#- calculate EC numbers occuring in group's core metabolism
ourECnumbers = group.majorityEcGraph(
majorityPercentage=percentage,
noMultifunctional=False).getECs()
#- reduce set of EC numbers to first three levels
ourECnumbers = EcNumber.insertWildcards(ourECnumbers,
keepLevels=3,
allowHigherWildcards=False)
#- overlap Poot-Hernandez' set with ours and print amount of EC numbers inside the intersection and falling off either side
"""
from FEV_KEGG.Evolution.Events import SimpleGeneDuplication, ChevronGeneDuplication, NeofunctionalisedECs,\
NeofunctionalisedEnzymes
from FEV_KEGG.Evolution.Taxonomy import NCBI
import FEV_KEGG.KEGG.Organism as Organism
if __name__ == '__main__':
output = []
#- get NCBI taxonomy tree
taxonomy = NCBI.getTaxonomy()
#- get group of organisms 'Archaea/Thaumarchaeota'
group = Organism.Group(
taxonomy.getOrganismAbbreviationsByPath('Archaea/Thaumarchaeota',
oneOrganismPerSpecies=False))
#- get supergroup of organisms 'Archaea'
supergroup = Organism.Group(
taxonomy.getOrganismAbbreviationsByPath('Archaea',
oneOrganismPerSpecies=False))
#- calculate new EC numbers occuring in group's core metabolism compared to supergroup's core metabolism
newECs = group.consensusEcGraph(
noMultifunctional=True).getECs().difference(
supergroup.consensusEcGraph(noMultifunctional=True).getECs())
output.append('new EC numbers: ' + str(len(newECs)))
#- calculate neofunctionalised EC numbers in group's core metabolism
descendantEnzymeGraph = group.collectiveEnzymeGraphByEcConsensus(
'2.5.1.15', '2.5.1.19', '2.5.1.7', '2.5.1.9', '2.6.1.16', '2.7.1.107',
'2.7.1.130', '2.7.1.23', '2.7.1.24', '2.7.1.26', '2.7.1.33', '2.7.2.3',
'2.7.4.6', '2.7.4.8', '2.7.4.9', '2.7.6.3', '2.7.7.18', '2.7.7.2',
'2.7.7.23', '2.7.7.27', '2.7.7.3', '2.7.7.38', '2.7.7.41', '2.7.8.5',
'2.7.8.8', '3.1.3.45', '3.5.4.16', '3.5.4.25', '3.5.4.26', '3.6.1.1',
'3.6.1.34', '3.6.1.45', '4.1.1.36', '4.1.1.65', '4.1.2.13', '4.1.2.16',
'4.1.2.25', '4.2.1.10', '4.2.1.11', '4.6.1.3', '4.6.1.4', '5.1.1.3',
'5.3.1.1', '5.3.1.13', '6.3.2.12', '6.3.2.13', '6.3.2.15', '6.3.2.4',
'6.3.2.5', '6.3.2.8', '6.3.2.9'
]
theirECnumbers = set()
for string in theirECnumberStrings:
theirECnumbers.add(EcNumber(string))
#- get group of organisms 'Escherichia coli'
eco = Organism.Organism('eco')
#- calculate EC numbers occuring in eco's core metabolism
ourECnumbersWithWildcard = eco.substanceEcGraph(
noMultifunctional=True).getECs()
ourECnumbers = EcNumber.removeWildcards(ourECnumbersWithWildcard)
#- overlap Almaas' set with ours and print amount of EC numbers inside the intersection and falling off either side
onlyInTheirs = theirECnumbers.difference(ourECnumbers)
inBoth = theirECnumbers.intersection(ourECnumbers)
onlyInOurs = ourECnumbers.difference(theirECnumbers)
output.append(
str(len(onlyInTheirs)) + '\t' + str(len(inBoth)) + '\t' +
str(len(onlyInOurs)))
'2.7.4.6', '2.7.4.8', '2.7.4.9', '2.7.6.3', '2.7.7.18', '2.7.7.2',
'2.7.7.23', '2.7.7.27', '2.7.7.3', '2.7.7.38', '2.7.7.41', '2.7.8.5',
'2.7.8.8', '3.1.3.45', '3.5.4.16', '3.5.4.25', '3.5.4.26', '3.6.1.1',
'3.6.1.34', '3.6.1.45', '4.1.1.36', '4.1.1.65', '4.1.2.13', '4.1.2.16',
'4.1.2.25', '4.2.1.10', '4.2.1.11', '4.6.1.3', '4.6.1.4', '5.1.1.3',
'5.3.1.1', '5.3.1.13', '6.3.2.12', '6.3.2.13', '6.3.2.15', '6.3.2.4',
'6.3.2.5', '6.3.2.8', '6.3.2.9'
]
theirECnumbers = set()
for string in theirECnumberStrings:
theirECnumbers.add(EcNumber(string))
#- get group of organisms 'Escherichia coli'
taxonomy = NCBI.getTaxonomy()
group = Organism.Group(
taxonomy.getOrganismAbbreviationsByPath('Escherichia coli',
oneOrganismPerSpecies=False))
#- REPEAT for varying majority-percentages:
for percentage in [100, 90, 80, 70, 60, 50, 40, 30, 20, 10, 1]:
#- calculate EC numbers occuring in group's core metabolism
ourECnumbersWithWildcard = group.majorityEcGraph(
majorityPercentage=percentage, noMultifunctional=True).getECs()
ourECnumbers = EcNumber.removeWildcards(ourECnumbersWithWildcard)
#- overlap Almaas' set with ours and print amount of EC numbers inside the intersection and falling off either side
onlyInTheirs = theirECnumbers.difference(ourECnumbers)
inBoth = theirECnumbers.intersection(ourECnumbers)
onlyInOurs = ourECnumbers.difference(theirECnumbers)