#- calculate redundancy
cladeRedundancy = Redundancy(cladeEcGraph)
cladeRedundancyContribution = RedundancyContribution(
cladeRedundancy, cladeNeofunctionalisedMetabolismSet)
cladeRobustnessContributedECsForContributingNeofunctionalisedEC = cladeRedundancyContribution.getContributedKeysForSpecial(
redundancyType)
cladeRobustnessContributingNeofunctionalisedECs = set(
cladeRobustnessContributedECsForContributingNeofunctionalisedEC.keys())
#- REPEAT for each function change consisting of "neofunctionalised" ECs, which also contribute to redundancy
output.append(
'"neofunctionalised" ECs: ' +
str(len(cladeNeofunctionalisedMetabolismSet)) + ' (' + str(
Percent.getPercentStringShort(
len(cladeNeofunctionalisedMetabolismSet), cladeEcCount, 0)) +
'%)')
robustnessContributingNeofunctionalisations = dict()
for functionChange, neofunctionalisations in cladeNeofunctionalisationsForFunctionChange.items(
):
#- report enzyme pairs of neofunctionalisations, which caused the EC to be considered "neofunctionalised", and are in return contributing to redundancy
if functionChange.ecA in cladeRobustnessContributingNeofunctionalisedECs or functionChange.ecB in cladeRobustnessContributingNeofunctionalisedECs: # function change contributes to robustness
for neofunctionalisation in neofunctionalisations:
currentSetOfContributedECs = robustnessContributingNeofunctionalisations.get(
neofunctionalisation, None)
if currentSetOfContributedECs is None:
cladeASortedList = list(cladeANeofunctionalisedMetabolismSet)
cladeASortedList.sort()
#- calculate redundancy
cladeARedundancy = Redundancy(cladeAEcGraph)
cladeARedundancyContribution = RedundancyContribution(
cladeARedundancy, cladeANeofunctionalisedMetabolismSet)
cladeARobustECsForNeofunctionalisedEC = cladeARedundancyContribution.getContributedKeysForSpecial(
RedundancyType.ROBUSTNESS)
cladeAFlexibleECsForNeofunctionalisedEC = cladeARedundancyContribution.getContributedKeysForSpecial(
RedundancyType.TARGET_FLEXIBILITY)
#- REPEAT for each "neofunctionalised" EC
output.append('"neofunctionalised" ECs: ' + str(len(cladeASortedList)) +
' (' + str(
Percent.getPercentStringShort(len(cladeASortedList),
cladeAEcCount, 0)) + '%)')
for ecNumber in cladeASortedList:
#- print number of ECs the "neofunctionalised" EC provides redundancy for (robustness and flexibility)
output.append(
str(ecNumber) + ' \t(' +
str(len(cladeAFlexibleECsForNeofunctionalisedEC.get(
ecNumber, []))) + ' target-flexibility) (' +
str(len(cladeARobustECsForNeofunctionalisedEC.get(ecNumber, []))) +
' robustness)')
output.append('')
output.append('Target-flexibility: ' + Percent.floatToPercentString(
cladeARedundancy.getRedundancyRatio(RedundancyType.TARGET_FLEXIBILITY))
)
output.append('Target-flexibility contribution: ' +
Percent.floatToPercentString(
output.append('')
output.append(', '.join(clade.ncbiNames) + ':')
output.append('')
#- get ECs
cladeEcGraph = clade.coreMetabolism(majorityPercentageCoreMetabolism)
cladeEcCount = len(cladeEcGraph.getECs())
output.append( 'core metabolism ECs: ' + str(cladeEcCount) )
output.append('')
#- calculate redundancy
cladeRedundancy = Redundancy(cladeEcGraph)
#- print number of redundant ECs, including percentage of all ECs, for all types of redundancy
output.append('')
output.append( 'Robustness fully: ' + Percent.floatToPercentString(cladeRedundancy.getRedundancyRatio(RedundancyType.ROBUSTNESS) ) )
output.append( 'Robustness partial: ' + Percent.floatToPercentString(cladeRedundancy.getRedundancyRatio(RedundancyType.ROBUSTNESS_PARTIAL) ) )
output.append( 'Robustness both: ' + Percent.floatToPercentString(cladeRedundancy.getRedundancyRatio(RedundancyType.ROBUSTNESS_BOTH) ) )
output.append('')
output.append( 'Flexibility fully: ' + Percent.floatToPercentString(cladeRedundancy.getRedundancyRatio(RedundancyType.FLEXIBILITY) ) )
output.append( 'Flexibility partial: ' + Percent.floatToPercentString(cladeRedundancy.getRedundancyRatio(RedundancyType.FLEXIBILITY_PARTIAL) ) )
output.append( 'Flexibility both: ' + Percent.floatToPercentString(cladeRedundancy.getRedundancyRatio(RedundancyType.FLEXIBILITY_BOTH) ) )
output.append('')
output.append( 'Target-flexibility fully: ' + Percent.floatToPercentString(cladeRedundancy.getRedundancyRatio(RedundancyType.TARGET_FLEXIBILITY) ) )
output.append( 'Target-flexibility partial: ' + Percent.floatToPercentString(cladeRedundancy.getRedundancyRatio(RedundancyType.TARGET_FLEXIBILITY_PARTIAL) ) )
output.append( 'Target-flexibility both: ' + Percent.floatToPercentString(cladeRedundancy.getRedundancyRatio(RedundancyType.TARGET_FLEXIBILITY_BOTH) ) )
output.append('')
output.append( 'Source-flexibility fully: ' + Percent.floatToPercentString(cladeRedundancy.getRedundancyRatio(RedundancyType.SOURCE_FLEXIBILITY) ) )
output.append( 'Source-flexibility partial: ' + Percent.floatToPercentString(cladeRedundancy.getRedundancyRatio(RedundancyType.SOURCE_FLEXIBILITY_PARTIAL) ) )
output.append( 'Source-flexibility both: ' + Percent.floatToPercentString(cladeRedundancy.getRedundancyRatio(RedundancyType.SOURCE_FLEXIBILITY_BOTH) ) )
output.append('')
Export.addColourAttribute(cladeEcGraph,
Export.Colour.GREEN,
nodes=False,
edges=edgesOfContributingNeofunctionalisedECs)
Export.forCytoscape(cladeEcGraph,
clade.ncbiNames[0],
inCacheFolder=True,
addDescriptions=True,
totalNumberOfOrganisms=clade.organismsCount)
#- REPEAT for each function change consisting of "neofunctionalised" ECs, which also contribute to redundancy
output.append(
'"neofunctionalised" ECs: ' +
str(len(cladeNeofunctionalisedMetabolismSet)) + ' (' + str(
Percent.getPercentStringShort(
len(cladeNeofunctionalisedMetabolismSet), cladeEcCount, 0)) +
'%)')
robustKeys = cladeRedundancy.getRedundantKeys(redundancyType)
robustKeysCount = len(robustKeys)
output.append(
'robust ECs: ' + str(robustKeysCount) + ' (' +
str(Percent.getPercentStringShort(robustKeysCount, cladeEcCount, 0)) +
'%)')
output.append(' of which are robust due to "neofunctionalised" ECs: ' +
str(
Percent.getPercentStringShort(
cladeRedundancyContribution.getKeyContributionRatio(
redundancyType), 1, 0)) + '%')
#- calculate "neofunctionalised" ECs
cladeNeofunctionalisedMetabolismSet = clade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation).getECs()
cladeNeofunctionalisationsForFunctionChange = clade.neofunctionalisationsForFunctionChange(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation)
#- calculate redundancy
cladeRedundancy = Redundancy(cladeEcGraph)
cladeRedundancyContribution = RedundancyContribution(cladeRedundancy, cladeNeofunctionalisedMetabolismSet)
for redundancyType in redundancyTypes:
cladeRobustnessContributedECsForContributingNeofunctionalisedEC = cladeRedundancyContribution.getContributedKeysForSpecial(redundancyType)
cladeRobustnessContributingNeofunctionalisedECs = set(cladeRobustnessContributedECsForContributingNeofunctionalisedEC.keys())
#- REPEAT for each function change consisting of "neofunctionalised" ECs, which also contribute to redundancy
output.append( '"neofunctionalised" ECs: ' + str(len(cladeNeofunctionalisedMetabolismSet)) + ' (' + str(Percent.getPercentStringShort(len(cladeNeofunctionalisedMetabolismSet), cladeEcCount, 0)) + '%)' )
robustnessContributingNeofunctionalisations = dict()
for functionChange, neofunctionalisations in cladeNeofunctionalisationsForFunctionChange.items():
#- report enzyme pairs of neofunctionalisations, which caused the EC to be considered "neofunctionalised", and are in return contributing to redundancy
if functionChange.ecA in cladeRobustnessContributingNeofunctionalisedECs or functionChange.ecB in cladeRobustnessContributingNeofunctionalisedECs: # function change contributes to robustness
for neofunctionalisation in neofunctionalisations:
currentSetOfContributedECs = robustnessContributingNeofunctionalisations.get(neofunctionalisation, None)
if currentSetOfContributedECs is None:
currentSetOfContributedECs = set()
robustnessContributingNeofunctionalisations[neofunctionalisation] = currentSetOfContributedECs
#- calculate redundancy
cladeRedundancy = Redundancy(cladeEcGraph)
cladeRedundancyContribution = RedundancyContribution(
cladeRedundancy, cladeNeofunctionalisedMetabolismSet)
cladeRobustnessContributedECsForContributingNeofunctionalisedEC = cladeRedundancyContribution.getContributedKeysForSpecial(
redundancyType)
cladeRobustnessContributingNeofunctionalisedECs = set(
cladeRobustnessContributedECsForContributingNeofunctionalisedEC.keys())
#- REPEAT for each function change consisting of "neofunctionalised" ECs, which also contribute to redundancy
output.append(
'"neofunctionalised" ECs: ' +
str(len(cladeNeofunctionalisedMetabolismSet)) + ' (' + str(
Percent.getPercentStringShort(
len(cladeNeofunctionalisedMetabolismSet), cladeEcCount, 0)) +
'%)')
robustnessContributingNeofunctionalisations = dict()
for functionChange, neofunctionalisations in cladeNeofunctionalisationsForFunctionChange.items(
):
#- report enzyme pairs of neofunctionalisations, which caused the EC to be considered "neofunctionalised", and are in return contributing to redundancy
if functionChange.ecA in cladeRobustnessContributingNeofunctionalisedECs or functionChange.ecB in cladeRobustnessContributingNeofunctionalisedECs: # function change contributes to robustness
for neofunctionalisation in neofunctionalisations:
currentSetOfContributedECs = robustnessContributingNeofunctionalisations.get(
neofunctionalisation, None)
if currentSetOfContributedECs is None:
#- REPEAT for each clade in Archaea, Bacteria, Archaea+Bacteria
for clade in [CoreLUCA.CladeType.archaea, CoreLUCA.CladeType.bacteria, CoreLUCA.CladeType.archaeaBacteria]:
#- get collective metabolism of clade
luca = CoreLUCA(clade)
output.append(luca.nameAbbreviation)
collectiveMetabolismLength = len( luca.collectiveMetabolism().getECs() )
#- REPEAT for varying majority percentages
for percentage in range(100, 0, -10):
#- calculate core metabolism
ecNumbers = luca.substanceEcGraph( percentage ).getECs()
#- print number of ECs by majority percentage
if percentage == 100:
#- IF majority percentage is 100%, i.e. consensus, also print the ECs
output.append( str(percentage) + "% ECs: " + str(len( ecNumbers )) + " (" + ", ".join([str(x) for x in ecNumbers]) + ")" + ' -> ' + Percent.getPercentStringShort(len( ecNumbers ), collectiveMetabolismLength, 0) + '% of collective')
else:
output.append( str(percentage) + "% ECs: " + str(len( ecNumbers )) + ' -> ' + Percent.getPercentStringShort(len( ecNumbers ), collectiveMetabolismLength, 0) + '% of collective')
output.append("\n")
luca.clade.group.freeHeap()
luca = None
for line in output:
print(line)