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KEGGdecoder_Heme.py
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KEGGdecoder_Heme.py
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#!/usr/bin/python
'''
Usage: python KEGG-decoder.py <KOALA INPUT> <FUNCTION LIST FORMAT>
Designed to parse through a blastKoala or ghostKoala output to determine
the completeness of various KEGG pathways
Dependencies:
Pandas - http://pandas.pydata.org/pandas-docs/stable/install.html
Seaborn - http://seaborn.pydata.org/installing.html
matplotlib - http://matplotlib.org/users/installing.html
For extended information about KEGG assignments, genes and pathways,
please see accompanying document "KOALA_definitions.txt"
'''
def C5_PPH(ko_match):
#Check for presence of 9 genes
total = 0
#glutamyl-tRNA reductase, glutamate-1-semialdehyde 2,1-aminomutase
#porphobilinogen synthase, hydroxymethylbilane synthase
#uroporphyrinogen decarboxylase, ferrochelatase
single_ko = ['K02492', 'K01845', 'K01698', 'K01749', 'K01599', 'K01772']
for i in single_ko:
if i in ko_match:
total += 1
#uroporphyrinogen-III synthase
if ('K01719' in ko_match or 'K13542' in ko_match or 'K13543' in ko_match):
total += 1
#coproporphyrinogen III oxidase
if ('K00228' in ko_match or 'K02495' in ko_match):
total += 1
#protoporphyrinogen oxidase
if ('K00230' in ko_match or 'K00231' in ko_match or 'K08973' in ko_match):
total += 1
value = float(total)/float(9)
return {'C5_PPH': float("%.2f" % (value))}
def C5_CPH(ko_match):
#Check for presence of 9 genes
total = 0
#glutamyl-tRNA reductase, glutamate-1-semialdehyde 2,1-aminomutase
#porphobilinogen synthase, hydroxymethylbilane synthase
#uroporphyrinogen decarboxylase, coproporphyrinogen III oxidase, ferrochelatase
single_ko = ['K02492', 'K01845', 'K01698', 'K01749', 'K01599', 'K00231', 'K01772']
for i in single_ko:
if i in ko_match:
total += 1
#uroporphyrinogen-III synthase
if ('K01719' in ko_match or 'K13542' in ko_match or 'K13543' in ko_match):
total += 1
#heme synthase
if ('K00435' in ko_match or 'K22227' in ko_match):
total += 1
value = float(total)/float(9)
return {'C5_CPH': float("%.2f" % (value))}
def C5_SIRO(ko_match):
#Check for presence of 11 genes
total = 0
#glutamyl-tRNA reductase, glutamate-1-semialdehyde 2,1-aminomutase
#porphobilinogen synthase, hydroxymethylbilane synthase
#siroheme decarboxylase, Fe-coproporphyrin III synthase
single_ko = ['K02492', 'K01845', 'K01698', 'K01749', 'K22225', 'K22226']
for i in single_ko:
if i in ko_match:
total += 1
#uroporphyrinogen-III synthase
if ('K01719' in ko_match or 'K13542' in ko_match or 'K13543' in ko_match):
total += 1
#uroporphyrin-III C-methyltransferase
if ('K00589' in ko_match or 'K02302' in ko_match or 'K02303' in ko_match or 'K02496' in ko_match or 'K13542' in ko_match or 'K13543' in ko_match):
total += 1
#precorrin-2 dehydrogenase
if ('K02302' in ko_match or 'K02304' in ko_match):
total += 1
#sirohydrochlorin ferrochelatase
if ('K02302' in ko_match or 'K02304' in ko_match or 'K03794' in ko_match):
total += 1
#heme synthase
if ('K00435' in ko_match or 'K22227' in ko_match):
total += 1
value = float(total)/float(11)
return {'C5_SIRO': float("%.2f" % (value))}
def C4_PPH(ko_match):
#Check for presence of 8 genes
total = 0
#5-aminolevulinate synthase
#porphobilinogen synthase, hydroxymethylbilane synthase
#uroporphyrinogen decarboxylase, ferrochelatase
single_ko = ['K00643', 'K01698', 'K01749', 'K01599', 'K01772']
for i in single_ko:
if i in ko_match:
total += 1
#uroporphyrinogen-III synthase
if ('K01719' in ko_match or 'K13542' in ko_match or 'K13543' in ko_match):
total += 1
#coproporphyrinogen III oxidase
if ('K00228' in ko_match or 'K02495' in ko_match):
total += 1
#protoporphyrinogen oxidase
if ('K00230' in ko_match or 'K00231' in ko_match or 'K08973' in ko_match):
total += 1
value = float(total)/float(8)
return {'C4_PPH': float("%.2f" % (value))}
def C4_CPH(ko_match):
#Check for presence of 8 genes
total = 0
#5-aminolevulinate synthase
#porphobilinogen synthase, hydroxymethylbilane synthase
#uroporphyrinogen decarboxylase, coproporphyrinogen III oxidase, ferrochelatase
single_ko = ['K00643', 'K01698', 'K01749', 'K01599', 'K00231', 'K01772']
for i in single_ko:
if i in ko_match:
total += 1
#uroporphyrinogen-III synthase
if ('K01719' in ko_match or 'K13542' in ko_match or 'K13543' in ko_match):
total += 1
#heme synthase
if ('K00435' in ko_match or 'K22227' in ko_match):
total += 1
value = float(total)/float(8)
return {'C4_CPH': float("%.2f" % (value))}
def C4_SIRO(ko_match):
#Check for presence of 10 genes
total = 0
#5-aminolevulinate synthase
#porphobilinogen synthase, hydroxymethylbilane synthase
#siroheme decarboxylase, Fe-coproporphyrin III synthase
single_ko = ['K00643', 'K01698', 'K01749', 'K22225', 'K22226']
for i in single_ko:
if i in ko_match:
total += 1
#uroporphyrinogen-III synthase
if ('K01719' in ko_match or 'K13542' in ko_match or 'K13543' in ko_match):
total += 1
#uroporphyrin-III C-methyltransferase
if ('K00589' in ko_match or 'K02302' in ko_match or 'K02303' in ko_match or 'K02496' in ko_match or 'K13542' in ko_match or 'K13543' in ko_match):
total += 1
#precorrin-2 dehydrogenase
if ('K02302' in ko_match or 'K02304' in ko_match):
total += 1
#sirohydrochlorin ferrochelatase
if ('K02302' in ko_match or 'K02304' in ko_match or 'K03794' in ko_match):
total += 1
#heme synthase
if ('K00435' in ko_match or 'K22227' in ko_match):
total += 1
value = float(total)/float(10)
return {'C4_SIRO': float("%.2f" % (value))}
def upper_C5(ko_match):
#Check for presence of 2 genes
total = 0
#glutamyl-tRNA reductase, glutamate-1-semialdehyde 2,1-aminomutase
single_ko = ['K02492', 'K01845']
for i in single_ko:
if i in ko_match:
total += 1
value = float(total)/float(2)
return {'upper_C5': float("%.2f" % (value))}
def upper_C4(ko_match):
#Check for presence of 1 genes
total = 0
#5-aminolevulinate synthase
single_ko = ['K00643']
for i in single_ko:
if i in ko_match:
total += 1
value = float(total)/float(1)
return {'upper_C4': float("%.2f" % (value))}
def Common(ko_match):
#Check for presence of 3 genes
total = 0
#porphobilinogen synthase, hydroxymethylbilane synthase
single_ko = ['K01698', 'K01749']
for i in single_ko:
if i in ko_match:
total += 1
#uroporphyrinogen-III synthase
if ('K01719' in ko_match or 'K13542' in ko_match or 'K13543' in ko_match):
total += 1
value = float(total)/float(3)
return {'Common': float("%.2f" % (value))}
def lower_PPH(ko_match):
#Check for presence of 4 genes
total = 0
#uroporphyrinogen decarboxylase, ferrochelatase
single_ko = ['K01599', 'K01772']
for i in single_ko:
if i in ko_match:
total += 1
#coproporphyrinogen III oxidase
if ('K00228' in ko_match or 'K02495' in ko_match):
total += 1
#protoporphyrinogen oxidase
if ('K00230' in ko_match or 'K00231' in ko_match or 'K08973' in ko_match):
total += 1
value = float(total)/float(4)
return {'lower_PPH': float("%.2f" % (value))}
def lower_CPH(ko_match):
#Check for presence of 4 genes
total = 0
#uroporphyrinogen decarboxylase, coproporphyrinogen III oxidase, ferrochelatase
single_ko = ['K01599', 'K00231', 'K01772']
for i in single_ko:
if i in ko_match:
total += 1
#heme synthase
if ('K00435' in ko_match or 'K22227' in ko_match):
total += 1
value = float(total)/float(4)
return {'lower_CPH': float("%.2f" % (value))}
def lower_SIRO(ko_match):
#Check for presence of 6 genes
total = 0
#siroheme decarboxylase, Fe-coproporphyrin III synthase
single_ko = ['K22225', 'K22226']
for i in single_ko:
if i in ko_match:
total += 1
#uroporphyrin-III C-methyltransferase
if ('K00589' in ko_match or 'K02302' in ko_match or 'K02303' in ko_match or 'K02496' in ko_match or 'K13542' in ko_match or 'K13543' in ko_match):
total += 1
#precorrin-2 dehydrogenase
if ('K02302' in ko_match or 'K02304' in ko_match):
total += 1
#sirohydrochlorin ferrochelatase
if ('K02302' in ko_match or 'K02304' in ko_match or 'K03794' in ko_match):
total += 1
#heme synthase
if ('K00435' in ko_match or 'K22227' in ko_match):
total += 1
value = float(total)/float(6)
return {'lower_SIRO': float("%.2f" % (value))}
def default_viz(genome_df, outfile_name):
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(font_scale=1.2)
sns.set_style({"savefig.dpi": 200})
ax = sns.heatmap(genome_df, cmap=plt.cm.YlOrRd, linewidths=2,
linecolor='k', square=True, xticklabels=True,
yticklabels=True, cbar_kws={"shrink": 0.1})
ax.xaxis.tick_top()
#ax.set_yticklabels(ax.get_yticklabels(), rotation=90)
plt.xticks(rotation=90)
plt.yticks(rotation=0)
# get figure (usually obtained via "fig,ax=plt.subplots()" with matplotlib)
fig = ax.get_figure()
# specify dimensions and save
#xLen = len(genome_df.columns.values.tolist())*20
#yLen = len(genome_df.index.tolist())*20
fig.set_size_inches(100, 100)
fig.savefig(outfile_name, bbox_inches='tight', pad_inches=0.1)
def main():
import os
import matplotlib
matplotlib.use('Agg')
import argparse
import pandas as pd
from scipy.cluster import hierarchy
from scipy.spatial import distance
parser = argparse.ArgumentParser(description="Accepts KEGG KOALA\
text file as input. Produces function\
list and heat map figure.")
parser.add_argument('-i', '--input', help="Input KOALA file. See documentation\
for correct format")
parser.add_argument('-t', '--tangleopt', help="Number of tree iterations for minimizing tangles in tanglegram", default=1000)
parser.add_argument('-o', '--output', help="List version of the final heat\
map figure")
parser.add_argument('-v', '--vizoption', help="Options: static, interactive, tanglegram")
parser.add_argument('--newick', help="Required input for tanglegram visualization")
parser.add_argument("-m", "--myorder", help ="Orders output as specified by user.", default="None")
args = parser.parse_args()
arg_dict = vars(args)
genome_data = {}
for line in open(str(arg_dict['input']), "r"):
line = line.rstrip()
info = line.split()
if len(info) > 1:
if info[0].rsplit("_",1)[0] in genome_data.keys():
genome_data[info[0].rsplit("_",1)[0]].append(info[1])
else:
genome_data[info[0].rsplit("_",1)[0]] = [info[1]]
function_order = ['C5_PPH', 'C5_CPH', 'C5_SIRO', 'C4_PPH', 'C4_CPH', 'C4_SIRO', 'upper_C5', 'upper_C4', 'Common', 'lower_PPH', 'lower_CPH', 'lower_SIRO']
filehandle = str(arg_dict['output'])
out_file = open(filehandle, "w")
out_file.write('Function'+"\t"+str("\t".join(function_order))+"\n")
for k in genome_data:
pathway_data = {}
pathway_data.update(C5_PPH(genome_data[k]))
pathway_data.update(C5_CPH(genome_data[k]))
pathway_data.update(C5_SIRO(genome_data[k]))
pathway_data.update(C4_PPH(genome_data[k]))
pathway_data.update(C4_CPH(genome_data[k]))
pathway_data.update(C4_SIRO(genome_data[k]))
pathway_data.update(upper_C5(genome_data[k]))
pathway_data.update(upper_C4(genome_data[k]))
pathway_data.update(Common(genome_data[k]))
pathway_data.update(lower_PPH(genome_data[k]))
pathway_data.update(lower_CPH(genome_data[k]))
pathway_data.update(lower_SIRO(genome_data[k]))
# print k, pathway_data
out_string = str(k)+"\t"
out_list = [k]
for i in function_order:
out_list.append(pathway_data[i])
out_string = str(out_list).strip('[]')
tab_string = ""
for l in out_string:
if l == "\'":
continue
if l == ",":
tab_string = tab_string + "\t"
else:
tab_string = tab_string + l
out_file.write(tab_string+"\n")
out_file.close()
file_in = open(filehandle, "r")
genome = pd.read_csv(file_in, index_col=0, sep='\t')
rearrange = False
if arg_dict["myorder"] != 'None' and os.path.exists(arg_dict["myorder"]):
rearrange = True
leaf_order = []
for line in open(str(arg_dict["myorder"]), "r"):
line = line.rstrip("\r\n")
leaf_order.append(line)
genome = genome.reindex(leaf_order)
if arg_dict['vizoption'] == 'static':
from .KEGG_clustering import hClust_euclidean
if len(genome.index) >= 2 and not rearrange:
genome = hClust_euclidean(genome)
default_viz(genome, os.path.splitext(filehandle)[0] + ".svg")
if arg_dict['vizoption'] == 'interactive':
from .Plotly_viz import plotly_viz
plotly_viz(genome, os.path.splitext(filehandle)[0] + ".html")
if arg_dict['vizoption'] == 'tanglegram':
from .MakeTanglegram import make_tanglegram
if len(genome.index) >= 3:
make_tanglegram(genome, str(arg_dict['newick']), os.path.splitext(filehandle)[0] + ".tanglegram.svg", int(arg_dict["tangleopt"]))
else:
raise ValueError("Tanglegram mode requires three or more genomes")
if __name__ == "__main__":
main()