"""
Question
--------
When comparing the core metabolism of Archaea and Bacteria, what differences and similarities occur?
Method
------
- build Bacteria clade
- build Archaea clade
- REPEAT for varying majority-percentages:
- overlap core metabolisms and print amount of EC numbers inside the intersection and falling off either side
- remove wildcard EC numbers
- END
- build clade pair
- export unified metabolism, coloured by only-Archaea/both/only-Bacteria
Result
------
::
Maj. % Bacteria both Archaea
100%: 0 1 7
90%: 50 40 36
80%: 83 67 47
70%: 103 103 59
60%: 125 129 58
50%: 153 163 72
40%: 191 192 75
30%: 235 229 90
20%: 304 279 83
10%: 400 386 87
1%: 631 653 91
See bacteria_vs_archaea.jpeg
Conclusion
----------
Bacteria and Archaea always share a significant amount of EC numbers, but never all of them. The much bigger group of Bacteria also has many more EC numbers which never occur in Archaea.
This might be because there are more known Bacteria organisms than known Archaea organisms, i.e. a statistical skew. Or it might be because Bacteria are, as a group, more versatile in habitat than Archaea.
The exported graph comparing Bacteria and Archaea directly (at 80% majority) shows several regions (more or less complete pathways) which only occur in either of the clades' core metabolisms.
Which does not mean they do not occur in any individual organism of the other clade!
For example:
Only in Bacteria: 00061 Fatty acid biosynthesis and 00550 Peptidoglycan biosynthesis
Only in Archaea: 00790 Folate biosynthesis and 00900 Terpenoid backbone biosynthesis
Apart from these regions standing out, both clades seem to have evolved different ways of providing redundancy to their common metabolism.
"""
from FEV_KEGG.Drawing import Export
from FEV_KEGG.KEGG.File import cache
from FEV_KEGG.Evolution.Clade import CladePair, Clade
from FEV_KEGG.Graph.Elements import EcNumber
[docs]@cache(folder_path='experiments/41', file_name='bacteria_clade')
def getBacteriaClade():
bacteriaClade = Clade('/Bacteria')
# pre-fetch collective metabolism into memory
bacteriaClade.collectiveMetabolism(excludeMultifunctionalEnzymes=True)
return bacteriaClade
[docs]@cache(folder_path='experiments/41', file_name='archaea_clade')
def getArchaeaClade():
archaeaClade = Clade('/Archaea')
# pre-fetch collective metabolism into memory
archaeaClade.collectiveMetabolism(excludeMultifunctionalEnzymes=True)
return archaeaClade
if __name__ == '__main__':
output = ['Maj. %\tBacteria\tboth\tArchaea']
#- build Bacteria clade
bacteriaClade = getBacteriaClade()
#- build Archaea clade
archaeaClade = getArchaeaClade()
#- REPEAT for varying majority-percentages:
for percentage in [100, 90, 80, 70, 60, 50, 40, 30, 20, 10 , 1]:
#- overlap core metabolisms and print amount of EC numbers inside the intersection and falling off either side
bacteriaECs = bacteriaClade.coreMetabolism(percentage).getECs()
archaeaECs = archaeaClade.coreMetabolism(percentage).getECs()
bothECs = bacteriaECs.intersection(archaeaECs)
onlyBacteriaECs = bacteriaECs.difference(archaeaECs)
onlyArchaeaECs = archaeaECs.difference(bacteriaECs)
#- remove wildcard EC numbers
onlyBacteriaECs = EcNumber.removeWildcards(onlyBacteriaECs)
bothECs = EcNumber.removeWildcards(bothECs)
onlyArchaeaECs = EcNumber.removeWildcards(onlyArchaeaECs)
output.append( str(percentage) + '%:\t' + str(len(onlyBacteriaECs)) + '\t' + str(len(bothECs)) + '\t' + str(len(onlyArchaeaECs)) )
for line in output:
print(line)
#- build clade pair
cladePair = CladePair(bacteriaClade, archaeaClade)
#- export unified metabolism, coloured by only Archaea/both/only Bacteria
unifiedEcGraph = cladePair.unifiedMetabolism(colour = True)
Export.forCytoscape(unifiedEcGraph, file = 'experiments/41/bacteria_vs_archaea', inCacheFolder=True)