"""
Context
-------
Extending :mod:`54` by a list of all the alternative paths each neofunctionalisation (or rather one of their EC numbers) occurs on.
Question
--------
Which neofunctionalisations exist in the core metabolism of Archaea, using a certain E-value?
Grouped by function change, sorted lexicographically, annotated with links to KEGG, annotated with human-readable names (if possible), and exported into an HTML file.
Which neofunctionalised enzymes cause the core metabolism of Archaea to have increased redundancy?
Grouped by contributed functions, sorted lexicographically, annotated with links to KEGG, annotated with human-readable names (if possible), and exported into an HTML file.
Also listing each alternative path a neofunctionalisation occurs on, contributing to redundancy of the current group's redundant EC number.
Method
------
- get clade
- get core metabolism
- calculate "neofunctionalised" ECs
- report neofunctionalisations
- print them into nice HTML
- calculate redundancy
- REPEAT for each "neofunctionalised" EC contributing to redundancy
- report enzyme pairs of neofunctionalisations, which caused the EC to be considered "neofunctionalised", and are in return contributing to redundancy
- report the alternative paths constituting the redundancy
- print them into nice HTML
Result
------
::
core metabolism majority: 80%
neofunctionalisation majority: 0% (this means that gene duplication within a single organism is enough)
Archaea:
core metabolism ECs: 114
All neofunctionalisations: 1140
[see Archaea_Neofunctionalisations-For-FunctionChange.html]
"neofunctionalised" ECs: 16 (14%)
Neofunctionalisations contributing to robustness: 93 (8%)
[see Archaea_Neofunctionalisations-For-Contributed-EC.html]
Conclusion
----------
Nice and shiny paths.
"""
from FEV_KEGG.KEGG.File import cache
from FEV_KEGG.Evolution.Clade import Clade
from FEV_KEGG.Statistics import Percent
from FEV_KEGG.Robustness.Topology.Redundancy import RedundancyType, Redundancy, RedundancyContribution
from FEV_KEGG import settings
from FEV_KEGG.Util.Util import dictToHtmlFile
[docs]@cache(folder_path='experiments', file_name='archaea_clade')
def getClade():
clade = Clade('Archaea')
# pre-fetch collective metabolism into memory
clade.collectiveMetabolism(excludeMultifunctionalEnzymes=settings.defaultNoMultifunctional)
# pre-fetch collective enzyme metabolism into memory
clade.collectiveMetabolismEnzymes(excludeMultifunctionalEnzymes=settings.defaultNoMultifunctional)
return clade
if __name__ == '__main__':
output = ['']
#- get clade
clade = getClade()
majorityPercentageCoreMetabolism = 80
majorityPercentageNeofunctionalisation = 0
eValue = 1e-15
redundancyType = RedundancyType.ROBUSTNESS_BOTH
output.append( 'core metabolism majority: ' + str(majorityPercentageCoreMetabolism) + '%' )
output.append( 'neofunctionalisation majority: ' + str(majorityPercentageNeofunctionalisation) + '% (this means that gene duplication within a single organism is enough)' )
output.append('')
output.append(', '.join(clade.ncbiNames) + ':')
output.append('')
#- get core metabolism
cladeEcGraph = clade.coreMetabolism(majorityPercentageCoreMetabolism)
cladeEcCount = len(cladeEcGraph.getECs())
output.append( 'core metabolism ECs: ' + str(cladeEcCount) )
output.append('')
#- report neofunctionalisations
neofunctionalisationsForFunctionChange = clade.neofunctionalisationsForFunctionChange(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, eValue=eValue)
allNeofunctionalisations = set() # set of all neofunctionalisations, no matter which function change they belong to
for valueSet in neofunctionalisationsForFunctionChange.values():
allNeofunctionalisations.update( valueSet )
output.append('')
output.append( 'All neofunctionalisations: ' + str(len(allNeofunctionalisations)) )
#- print them into nice HTML
ecNumbers = set()
for functionChange in neofunctionalisationsForFunctionChange.keys():
ecNumbers.update( functionChange.ecPair )
dictToHtmlFile(neofunctionalisationsForFunctionChange, clade.ncbiNames[0] + '_Neofunctionalisations-For-FunctionChange.html', byValueFirst=False, inCacheFolder=True, addEcDescriptions=ecNumbers)
output.append( '\t[see ' + clade.ncbiNames[0] + '_Neofunctionalisations-For-FunctionChange.html]' )
output.append('')
#- calculate "neofunctionalised" ECs
cladeNeofunctionalisedMetabolismSet = clade.neofunctionalisedECs(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, eValue=eValue).getECs()
cladeNeofunctionalisationsForFunctionChange = clade.neofunctionalisationsForFunctionChange(majorityPercentageCoreMetabolism, majorityPercentageNeofunctionalisation, eValue=eValue)
#- 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:
currentSetOfContributedECs = set()
robustnessContributingNeofunctionalisations[neofunctionalisation] = currentSetOfContributedECs
for ec in functionChange.ecPair:
contributedECs = cladeRobustnessContributedECsForContributingNeofunctionalisedEC.get(ec, None)
if contributedECs is not None:
currentSetOfContributedECs.update(contributedECs)
output.append('')
output.append( 'Neofunctionalisations contributing to robustness: ' + str(len(robustnessContributingNeofunctionalisations)) + ' (' + str(Percent.getPercentStringShort(len(robustnessContributingNeofunctionalisations), len(allNeofunctionalisations), 0)) + '%)' )
output.append( '\t[see ' + clade.ncbiNames[0] + '_Neofunctionalisations-For-Contributed-EC.html]' )
output.append('')
output.append('')
#- report the alternative paths constituting the redundancy
markedPathsForContributedECs = cladeRedundancyContribution.getContributedPathsForKey(redundancyType)
#- print them into nice HTML
neofunctionalisationsForContributedEC = dict()
for neofunctionalisation, contributedECs in robustnessContributingNeofunctionalisations.items():
for contributedEC in contributedECs:
currentSetOfNeofunctionalisations = neofunctionalisationsForContributedEC.get(contributedEC, None)
if currentSetOfNeofunctionalisations is None:
currentSetOfNeofunctionalisations = set()
neofunctionalisationsForContributedEC[contributedEC] = currentSetOfNeofunctionalisations
currentSetOfNeofunctionalisations.add(neofunctionalisation)
ecNumbers = set()
for contributedEC in neofunctionalisationsForContributedEC.keys():
ecNumbers.add( contributedEC )
dictToHtmlFile(neofunctionalisationsForContributedEC, clade.ncbiNames[0] + '_Neofunctionalisations-For-Contributed-EC.html', byValueFirst=False, inCacheFolder=True, addEcDescriptions=ecNumbers, headingDescriptionForHeading=markedPathsForContributedECs)
for line in output:
print( line )