如何将 KEGG 模块 (md:) 平面文件解析为 Python 中的 Pandas DataFrame？ （即，逐节解析文本，逐行阅读一次）
How to parse KEGG module (md:) flat files into Pandas DataFrame in Python? (i.e., parse text by section reading line-by-line only once)
我正在尝试为这种类型的文件编写解析器，但事实证明它比我想象的要复杂一些。如果这是几年前，我会多次阅读文件，但现在我考虑效率，这不是什么好事，因为必须有更好的方法。
如何在逐行读取文件的同时解析每个部分？
我的尝试是设置一个根据部分更改的变量。这开始起作用，但正如您所看到的，它在第二个分组（例如反应）中变得混乱。我觉得 while 循环可能是答案，但我不确定如何在这种情况下实现它。
from io import StringIO
f = StringIO(
"""
ENTRY M00001 Pathway Module
NAME Glycolysis (Embden-Meyerhof pathway), glucose => pyruvate
DEFINITION (K00844,K12407,K00845,K00886,K08074,K00918) (K01810,K06859,K13810,K15916) (K00850,K16370,K21071,K00918) (K01623,K01624,K11645,K16305,K16306) K01803 ((K00134,K00150) K00927,K11389) (K01834,K15633,K15634,K15635) K01689 (K00873,K12406)
ORTHOLOGY K00844,K12407,K00845 hexokinase/glucokinase [EC:2.7.1.1 2.7.1.2] [RN:R01786]
K00886 polyphosphate glucokinase [EC:2.7.1.63] [RN:R02189]
K08074,K00918 ADP-dependent glucokinase [EC:2.7.1.147] [RN:R09085]
K01810,K06859,K13810,K15916 glucose-6-phosphate isomerase [EC:5.3.1.9] [RN:R02740]
K00850,K16370,K21071 6-phosphofructokinase [EC:2.7.1.11] [RN:R04779]
K00918 ADP-dependent phosphofructokinase [EC:2.7.1.146] [RN:R09084]
K01623,K01624,K11645,K16305,K16306 fructose-bisphosphate aldolase [EC:4.1.2.13] [RN:R01070]
K01803 triosephosphate isomerase [EC:5.3.1.1] [RN:R01015]
K00134,K00150 glyceraldehyde 3-phosphate dehydrogenase [EC:1.2.1.12 1.2.1.59] [RN:R01061 R01063]
K00927 phosphoglycerate kinase [EC:2.7.2.3] [RN:R01512]
K11389 glyceraldehyde-3-phosphate dehydrogenase (ferredoxin) [EC:1.2.7.6] [RN:R07159]
K01834,K15633,K15634,K15635 phosphoglycerate mutase [EC:5.4.2.11 5.4.2.12] [RN:R01518]
K01689 enolase [EC:4.2.1.11] [RN:R00658]
K00873,K12406 pyruvate kinase [EC:2.7.1.40] [RN:R00200]
CLASS Pathway modules; Carbohydrate metabolism; Central carbohydrate metabolism
PATHWAY map00010 Glycolysis / Gluconeogenesis
map01200 Carbon metabolism
map01100 Metabolic pathways
REACTION R01786,R02189,R09085 C00267 -> C00668
R02740 C00668 -> C05345
R04779,R09084 C05345 -> C05378
R01070 C05378 -> C00111 + C00118
R01015 C00111 -> C00118
R01061,R01063 C00118 -> C00236
R01512 C00236 -> C00197
R07159 C00118 -> C00197
R01518 C00197 -> C00631
R00658 C00631 -> C00074
R00200 C00074 -> C00022
COMPOUND C00267 alpha-D-Glucose
C00668 alpha-D-Glucose 6-phosphate
C05345 beta-D-Fructose 6-phosphate
C05378 beta-D-Fructose 1,6-bisphosphate
C00111 Glycerone phosphate
C00118 D-Glyceraldehyde 3-phosphate
C00236 3-Phospho-D-glyceroyl phosphate
C00197 3-Phospho-D-glycerate
C00631 2-Phospho-D-glycerate
C00074 Phosphoenolpyruvate
C00022 Pyruvate
///
"""
)
kegg_definition = None
kegg_orthology = list()
kegg_ortholog_set = set()
kegg_class = None
kegg_pathways = list()
kegg_pathway_set = set()
kegg_reactions = list()
kegg_reaction_set = set()
kegg_compounds = list()
kegg_compound_set = set()
# Read KEGG module text
parsing = None
for line_level_1 in f:
line_level_1 = line_level_1.strip()
if not line_level_1.startswith("/"):
# Get orthologs
if line_level_1.startswith("DEFINITION"):
kegg_definition = line_level_1.replace("DEFINITION","").strip()
kegg_ortholog_set = str(kegg_definition)
for character in list("(+ -)"):
kegg_ortholog_set.replace(character, ",")
kegg_ortholog_set = set(filter(bool, kegg_ortholog_set.split(",")))
parsing = "ORTHOLOGY"
if parsing == "ORTHOLOGY":
ko_annot = line_level_1.replace("DEFINITION","").strip()
kegg_orthology.append(ko_annot)
# Get class
if line_level_1.startswith("CLASS"):
# parsing = None
kegg_class = line_level_1.replace("CLASS","").strip().split("; ")
# Get pathways
if line_level_1.startswith("PATHWAY"):
parsing = "PATHWAY"
if parsing == "PATHWAY":
kegg_pathway = line_level_1.replace("PATHWAY","").strip().split("; ")
kegg_pathways.append(kegg_pathway)
# Get reactions
if line_level_1.startswith("REACTION"):
parsing = "REACTION"
if parsing == "REACTION":
kegg_reaction = line_level_1.replace("REACTION","").strip().split("; ")
kegg_reactions.append(kegg_reaction)
# Get compounds
if line_level_1.startswith("COMPOUND"):
parsing = "COMPOUND"
if parsing == "COMPOUND":
kegg_compound = line_level_1.replace("COMPOUND","").strip().split("; ")
kegg_compounds.append(kegg_compound)
kegg_pathways
# [['map00010 Glycolysis / Gluconeogenesis'],
# ['map01200 Carbon metabolism'],
# ['map01100 Metabolic pathways'],
# ['REACTION R01786,R02189,R09085 C00267 -> C00668']]
这是因为直到您已经将线路收集到 PATHWAY 之后才检查 REACTION。做你所有的“下一节”吗？在开始使用“解析”值之前进行检查：
# Read KEGG module text
parsing = None
for line_level_1 in f:
line_level_1 = line_level_1.strip()
if not line_level_1.startswith("/"):
if line_level_1.startswith("DEFINITION"):
kegg_definition = line_level_1.replace("DEFINITION","").strip()
kegg_ortholog_set = str(kegg_definition)
for character in list("(+ -)"):
kegg_ortholog_set.replace(character, ",")
kegg_ortholog_set = set(filter(bool, kegg_ortholog_set.split(",")))
elif line_level_1.startswith("CLASS"):
kegg_class = line_level_1.replace("CLASS","").strip().split("; ")
elif line_level_1.startswith("ORTHOLOGY"):
parsing = "ORTHOLOGY"
elif line_level_1.startswith("PATHWAY"):
parsing = "PATHWAY"
elif line_level_1.startswith("REACTION"):
parsing = "REACTION"
elif line_level_1.startswith("COMPOUND"):
parsing = "COMPOUND"
if parsing == "ORTHOLOGY":
ko_annot = line_level_1.replace("DEFINITION","").strip()
kegg_orthology.append(ko_annot)
elif parsing == "PATHWAY":
# Get pathways
kegg_pathway = line_level_1.replace("PATHWAY","").strip().split("; ")
kegg_pathways.append(kegg_pathway)
# Get reactions
elif parsing == "REACTION":
kegg_reaction = line_level_1.replace("REACTION","").strip().split("; ")
kegg_reactions.append(kegg_reaction)
# Get compounds
elif parsing == "COMPOUND":
kegg_compound = line_level_1.replace("COMPOUND","").strip().split("; ")
kegg_compounds.append(kegg_compound)
print(kegg_pathways)
以防万一这对将来的任何人都有帮助。这是我制作的解析器。但是，如果您希望从一组直系同源物（例如宏基因组组装基因组或 bin）中计算模块完成率，那么您需要考虑 logicals in the KEGG definition and the fact that some modules are bifurcated...in which you should probably use some of the code in MicrobeAnnotator
import datetime
from Bio.KEGG.REST import kegg_list, kegg_get
# Parse KEGG Module
def parse_kegg_module(module_file):
"""
Example of a KEGG REST module file:
ENTRY M00001 Pathway Module
NAME Glycolysis (Embden-Meyerhof pathway), glucose => pyruvate
DEFINITION (K00844,K12407,K00845,K00886,K08074,K00918) (K01810,K06859,K13810,K15916) (K00850,K16370,K21071,K00918) (K01623,K01624,K11645,K16305,K16306) K01803 ((K00134,K00150) K00927,K11389) (K01834,K15633,K15634,K15635) K01689 (K00873,K12406)
ORTHOLOGY K00844,K12407,K00845 hexokinase/glucokinase [EC:2.7.1.1 2.7.1.2] [RN:R01786]
K00886 polyphosphate glucokinase [EC:2.7.1.63] [RN:R02189]
K08074,K00918 ADP-dependent glucokinase [EC:2.7.1.147] [RN:R09085]
K01810,K06859,K13810,K15916 glucose-6-phosphate isomerase [EC:5.3.1.9] [RN:R02740]
K00850,K16370,K21071 6-phosphofructokinase [EC:2.7.1.11] [RN:R04779]
K00918 ADP-dependent phosphofructokinase [EC:2.7.1.146] [RN:R09084]
K01623,K01624,K11645,K16305,K16306 fructose-bisphosphate aldolase [EC:4.1.2.13] [RN:R01070]
K01803 triosephosphate isomerase [EC:5.3.1.1] [RN:R01015]
K00134,K00150 glyceraldehyde 3-phosphate dehydrogenase [EC:1.2.1.12 1.2.1.59] [RN:R01061 R01063]
K00927 phosphoglycerate kinase [EC:2.7.2.3] [RN:R01512]
K11389 glyceraldehyde-3-phosphate dehydrogenase (ferredoxin) [EC:1.2.7.6] [RN:R07159]
K01834,K15633,K15634,K15635 phosphoglycerate mutase [EC:5.4.2.11 5.4.2.12] [RN:R01518]
K01689 enolase [EC:4.2.1.11] [RN:R00658]
K00873,K12406 pyruvate kinase [EC:2.7.1.40] [RN:R00200]
CLASS Pathway modules; Carbohydrate metabolism; Central carbohydrate metabolism
PATHWAY map00010 Glycolysis / Gluconeogenesis
map01200 Carbon metabolism
map01100 Metabolic pathways
REACTION R01786,R02189,R09085 C00267 -> C00668
R02740 C00668 -> C05345
R04779,R09084 C05345 -> C05378
R01070 C05378 -> C00111 + C00118
R01015 C00111 -> C00118
R01061,R01063 C00118 -> C00236
R01512 C00236 -> C00197
R07159 C00118 -> C00197
R01518 C00197 -> C00631
R00658 C00631 -> C00074
R00200 C00074 -> C00022
COMPOUND C00267 alpha-D-Glucose
C00668 alpha-D-Glucose 6-phosphate
C05345 beta-D-Fructose 6-phosphate
C05378 beta-D-Fructose 1,6-bisphosphate
C00111 Glycerone phosphate
C00118 D-Glyceraldehyde 3-phosphate
C00236 3-Phospho-D-glyceroyl phosphate
C00197 3-Phospho-D-glycerate
C00631 2-Phospho-D-glycerate
C00074 Phosphoenolpyruvate
C00022 Pyruvate
///
"""
kegg_module = None
kegg_name = None
kegg_definition = None
kegg_orthology = list()
kegg_ortholog_set = set()
kegg_classes = list()
kegg_pathways = list()
kegg_pathway_set = set()
kegg_reactions = list()
kegg_reaction_set = set()
kegg_compounds = list()
kegg_compound_set = set()
# Read KEGG module text
parsing = None
for line in module_file:
line = line.strip()
if not line.startswith("/"):
if not line.startswith(" "):
first_word = line.split(" ")[0]
if first_word.isupper() and first_word.isalpha():
parsing = first_word
if parsing == "ENTRY":
kegg_module = list(filter(bool, line.split(" ")))[1]
if parsing == "NAME":
kegg_name = line.replace(parsing, "").strip()
parsing = None
if parsing == "DEFINITION":
kegg_definition = line.replace(parsing,"").strip()
kegg_ortholog_set = str(kegg_definition)
for character in list("(+ -)"):
kegg_ortholog_set = kegg_ortholog_set.replace(character, ",")
kegg_ortholog_set = set(filter(bool, kegg_ortholog_set.split(",")))
if parsing == "ORTHOLOGY":
kegg_orthology.append(line.replace(parsing,"").strip())
if parsing == "CLASS":
kegg_classes = line.replace(parsing,"").strip().split("; ")
if parsing == "PATHWAY":
kegg_pathway = line.replace(parsing,"").strip()
kegg_pathways.append(kegg_pathway)
id_pathway = kegg_pathway.split(" ")[0]
kegg_pathway_set.add(id_pathway)
if parsing == "REACTION":
kegg_reaction = line.replace(parsing,"").strip()
kegg_reactions.append(kegg_reaction)
for id_reaction in kegg_reaction.split(" ")[0].split(","):
kegg_reaction_set.add(id_reaction)
if parsing == "COMPOUND":
kegg_compound = line.replace(parsing,"").strip()
id_compound = kegg_compound.split(" ")[0]
kegg_compounds.append(kegg_compound)
kegg_compound_set.add(id_compound)
module_info = pd.Series(
data = OrderedDict([
("NAME",kegg_name),
("DEFINITION",kegg_definition),
("ORTHOLOGY",kegg_orthology),
("ORTHOLOGY_SET",kegg_ortholog_set),
("CLASS",kegg_classes),
("PATHWAY",kegg_pathways),
("PATHWAY_SET",kegg_pathway_set),
("REACTION",kegg_reactions),
("REACTION_SET",kegg_reaction_set),
("COMPOUND",kegg_compounds),
("COMPOUND_SET",kegg_compound_set),
]),
name=kegg_module,
)
return module_info
def get_kegg_modules(expand_nested_modules=True):
results = list()
for line in list(kegg_list("module")):
line = line.strip()
module, name = line.split("\t")
prefix, id_module = module.split(":")
module_file = kegg_get(module)
module_info = parse_kegg_module(module_file)
results.append(module_info)
df = pd.DataFrame(results)
df.index.name = datetime.datetime.now().strftime("Accessed: %Y-%m-%d @ %H:%M")
# Expand nested modules
if expand_nested_modules:
for id_module, row in df.iterrows():
kegg_orthology_set = row["ORTHOLOGY_SET"]
expanded = set()
for x in kegg_orthology_set:
if x.startswith("K"):
expanded.add(x)
if x.startswith("M"):
for id_ko in df.loc[x,"ORTHOLOGY_SET"]:
expanded.add(id_ko)
df.loc[id_module, "ORTHOLOGY_SET"] = expanded
return df
我正在尝试为这种类型的文件编写解析器，但事实证明它比我想象的要复杂一些。如果这是几年前，我会多次阅读文件，但现在我考虑效率，这不是什么好事，因为必须有更好的方法。
如何在逐行读取文件的同时解析每个部分？
我的尝试是设置一个根据部分更改的变量。这开始起作用，但正如您所看到的，它在第二个分组（例如反应）中变得混乱。我觉得 while 循环可能是答案，但我不确定如何在这种情况下实现它。
from io import StringIO
f = StringIO(
"""
ENTRY M00001 Pathway Module
NAME Glycolysis (Embden-Meyerhof pathway), glucose => pyruvate
DEFINITION (K00844,K12407,K00845,K00886,K08074,K00918) (K01810,K06859,K13810,K15916) (K00850,K16370,K21071,K00918) (K01623,K01624,K11645,K16305,K16306) K01803 ((K00134,K00150) K00927,K11389) (K01834,K15633,K15634,K15635) K01689 (K00873,K12406)
ORTHOLOGY K00844,K12407,K00845 hexokinase/glucokinase [EC:2.7.1.1 2.7.1.2] [RN:R01786]
K00886 polyphosphate glucokinase [EC:2.7.1.63] [RN:R02189]
K08074,K00918 ADP-dependent glucokinase [EC:2.7.1.147] [RN:R09085]
K01810,K06859,K13810,K15916 glucose-6-phosphate isomerase [EC:5.3.1.9] [RN:R02740]
K00850,K16370,K21071 6-phosphofructokinase [EC:2.7.1.11] [RN:R04779]
K00918 ADP-dependent phosphofructokinase [EC:2.7.1.146] [RN:R09084]
K01623,K01624,K11645,K16305,K16306 fructose-bisphosphate aldolase [EC:4.1.2.13] [RN:R01070]
K01803 triosephosphate isomerase [EC:5.3.1.1] [RN:R01015]
K00134,K00150 glyceraldehyde 3-phosphate dehydrogenase [EC:1.2.1.12 1.2.1.59] [RN:R01061 R01063]
K00927 phosphoglycerate kinase [EC:2.7.2.3] [RN:R01512]
K11389 glyceraldehyde-3-phosphate dehydrogenase (ferredoxin) [EC:1.2.7.6] [RN:R07159]
K01834,K15633,K15634,K15635 phosphoglycerate mutase [EC:5.4.2.11 5.4.2.12] [RN:R01518]
K01689 enolase [EC:4.2.1.11] [RN:R00658]
K00873,K12406 pyruvate kinase [EC:2.7.1.40] [RN:R00200]
CLASS Pathway modules; Carbohydrate metabolism; Central carbohydrate metabolism
PATHWAY map00010 Glycolysis / Gluconeogenesis
map01200 Carbon metabolism
map01100 Metabolic pathways
REACTION R01786,R02189,R09085 C00267 -> C00668
R02740 C00668 -> C05345
R04779,R09084 C05345 -> C05378
R01070 C05378 -> C00111 + C00118
R01015 C00111 -> C00118
R01061,R01063 C00118 -> C00236
R01512 C00236 -> C00197
R07159 C00118 -> C00197
R01518 C00197 -> C00631
R00658 C00631 -> C00074
R00200 C00074 -> C00022
COMPOUND C00267 alpha-D-Glucose
C00668 alpha-D-Glucose 6-phosphate
C05345 beta-D-Fructose 6-phosphate
C05378 beta-D-Fructose 1,6-bisphosphate
C00111 Glycerone phosphate
C00118 D-Glyceraldehyde 3-phosphate
C00236 3-Phospho-D-glyceroyl phosphate
C00197 3-Phospho-D-glycerate
C00631 2-Phospho-D-glycerate
C00074 Phosphoenolpyruvate
C00022 Pyruvate
///
"""
)
kegg_definition = None
kegg_orthology = list()
kegg_ortholog_set = set()
kegg_class = None
kegg_pathways = list()
kegg_pathway_set = set()
kegg_reactions = list()
kegg_reaction_set = set()
kegg_compounds = list()
kegg_compound_set = set()
# Read KEGG module text
parsing = None
for line_level_1 in f:
line_level_1 = line_level_1.strip()
if not line_level_1.startswith("/"):
# Get orthologs
if line_level_1.startswith("DEFINITION"):
kegg_definition = line_level_1.replace("DEFINITION","").strip()
kegg_ortholog_set = str(kegg_definition)
for character in list("(+ -)"):
kegg_ortholog_set.replace(character, ",")
kegg_ortholog_set = set(filter(bool, kegg_ortholog_set.split(",")))
parsing = "ORTHOLOGY"
if parsing == "ORTHOLOGY":
ko_annot = line_level_1.replace("DEFINITION","").strip()
kegg_orthology.append(ko_annot)
# Get class
if line_level_1.startswith("CLASS"):
# parsing = None
kegg_class = line_level_1.replace("CLASS","").strip().split("; ")
# Get pathways
if line_level_1.startswith("PATHWAY"):
parsing = "PATHWAY"
if parsing == "PATHWAY":
kegg_pathway = line_level_1.replace("PATHWAY","").strip().split("; ")
kegg_pathways.append(kegg_pathway)
# Get reactions
if line_level_1.startswith("REACTION"):
parsing = "REACTION"
if parsing == "REACTION":
kegg_reaction = line_level_1.replace("REACTION","").strip().split("; ")
kegg_reactions.append(kegg_reaction)
# Get compounds
if line_level_1.startswith("COMPOUND"):
parsing = "COMPOUND"
if parsing == "COMPOUND":
kegg_compound = line_level_1.replace("COMPOUND","").strip().split("; ")
kegg_compounds.append(kegg_compound)
kegg_pathways
# [['map00010 Glycolysis / Gluconeogenesis'],
# ['map01200 Carbon metabolism'],
# ['map01100 Metabolic pathways'],
# ['REACTION R01786,R02189,R09085 C00267 -> C00668']]
这是因为直到您已经将线路收集到 PATHWAY 之后才检查 REACTION。做你所有的“下一节”吗？在开始使用“解析”值之前进行检查：
# Read KEGG module text
parsing = None
for line_level_1 in f:
line_level_1 = line_level_1.strip()
if not line_level_1.startswith("/"):
if line_level_1.startswith("DEFINITION"):
kegg_definition = line_level_1.replace("DEFINITION","").strip()
kegg_ortholog_set = str(kegg_definition)
for character in list("(+ -)"):
kegg_ortholog_set.replace(character, ",")
kegg_ortholog_set = set(filter(bool, kegg_ortholog_set.split(",")))
elif line_level_1.startswith("CLASS"):
kegg_class = line_level_1.replace("CLASS","").strip().split("; ")
elif line_level_1.startswith("ORTHOLOGY"):
parsing = "ORTHOLOGY"
elif line_level_1.startswith("PATHWAY"):
parsing = "PATHWAY"
elif line_level_1.startswith("REACTION"):
parsing = "REACTION"
elif line_level_1.startswith("COMPOUND"):
parsing = "COMPOUND"
if parsing == "ORTHOLOGY":
ko_annot = line_level_1.replace("DEFINITION","").strip()
kegg_orthology.append(ko_annot)
elif parsing == "PATHWAY":
# Get pathways
kegg_pathway = line_level_1.replace("PATHWAY","").strip().split("; ")
kegg_pathways.append(kegg_pathway)
# Get reactions
elif parsing == "REACTION":
kegg_reaction = line_level_1.replace("REACTION","").strip().split("; ")
kegg_reactions.append(kegg_reaction)
# Get compounds
elif parsing == "COMPOUND":
kegg_compound = line_level_1.replace("COMPOUND","").strip().split("; ")
kegg_compounds.append(kegg_compound)
print(kegg_pathways)
以防万一这对将来的任何人都有帮助。这是我制作的解析器。但是，如果您希望从一组直系同源物（例如宏基因组组装基因组或 bin）中计算模块完成率，那么您需要考虑 logicals in the KEGG definition and the fact that some modules are bifurcated...in which you should probably use some of the code in MicrobeAnnotator
import datetime
from Bio.KEGG.REST import kegg_list, kegg_get
# Parse KEGG Module
def parse_kegg_module(module_file):
"""
Example of a KEGG REST module file:
ENTRY M00001 Pathway Module
NAME Glycolysis (Embden-Meyerhof pathway), glucose => pyruvate
DEFINITION (K00844,K12407,K00845,K00886,K08074,K00918) (K01810,K06859,K13810,K15916) (K00850,K16370,K21071,K00918) (K01623,K01624,K11645,K16305,K16306) K01803 ((K00134,K00150) K00927,K11389) (K01834,K15633,K15634,K15635) K01689 (K00873,K12406)
ORTHOLOGY K00844,K12407,K00845 hexokinase/glucokinase [EC:2.7.1.1 2.7.1.2] [RN:R01786]
K00886 polyphosphate glucokinase [EC:2.7.1.63] [RN:R02189]
K08074,K00918 ADP-dependent glucokinase [EC:2.7.1.147] [RN:R09085]
K01810,K06859,K13810,K15916 glucose-6-phosphate isomerase [EC:5.3.1.9] [RN:R02740]
K00850,K16370,K21071 6-phosphofructokinase [EC:2.7.1.11] [RN:R04779]
K00918 ADP-dependent phosphofructokinase [EC:2.7.1.146] [RN:R09084]
K01623,K01624,K11645,K16305,K16306 fructose-bisphosphate aldolase [EC:4.1.2.13] [RN:R01070]
K01803 triosephosphate isomerase [EC:5.3.1.1] [RN:R01015]
K00134,K00150 glyceraldehyde 3-phosphate dehydrogenase [EC:1.2.1.12 1.2.1.59] [RN:R01061 R01063]
K00927 phosphoglycerate kinase [EC:2.7.2.3] [RN:R01512]
K11389 glyceraldehyde-3-phosphate dehydrogenase (ferredoxin) [EC:1.2.7.6] [RN:R07159]
K01834,K15633,K15634,K15635 phosphoglycerate mutase [EC:5.4.2.11 5.4.2.12] [RN:R01518]
K01689 enolase [EC:4.2.1.11] [RN:R00658]
K00873,K12406 pyruvate kinase [EC:2.7.1.40] [RN:R00200]
CLASS Pathway modules; Carbohydrate metabolism; Central carbohydrate metabolism
PATHWAY map00010 Glycolysis / Gluconeogenesis
map01200 Carbon metabolism
map01100 Metabolic pathways
REACTION R01786,R02189,R09085 C00267 -> C00668
R02740 C00668 -> C05345
R04779,R09084 C05345 -> C05378
R01070 C05378 -> C00111 + C00118
R01015 C00111 -> C00118
R01061,R01063 C00118 -> C00236
R01512 C00236 -> C00197
R07159 C00118 -> C00197
R01518 C00197 -> C00631
R00658 C00631 -> C00074
R00200 C00074 -> C00022
COMPOUND C00267 alpha-D-Glucose
C00668 alpha-D-Glucose 6-phosphate
C05345 beta-D-Fructose 6-phosphate
C05378 beta-D-Fructose 1,6-bisphosphate
C00111 Glycerone phosphate
C00118 D-Glyceraldehyde 3-phosphate
C00236 3-Phospho-D-glyceroyl phosphate
C00197 3-Phospho-D-glycerate
C00631 2-Phospho-D-glycerate
C00074 Phosphoenolpyruvate
C00022 Pyruvate
///
"""
kegg_module = None
kegg_name = None
kegg_definition = None
kegg_orthology = list()
kegg_ortholog_set = set()
kegg_classes = list()
kegg_pathways = list()
kegg_pathway_set = set()
kegg_reactions = list()
kegg_reaction_set = set()
kegg_compounds = list()
kegg_compound_set = set()
# Read KEGG module text
parsing = None
for line in module_file:
line = line.strip()
if not line.startswith("/"):
if not line.startswith(" "):
first_word = line.split(" ")[0]
if first_word.isupper() and first_word.isalpha():
parsing = first_word
if parsing == "ENTRY":
kegg_module = list(filter(bool, line.split(" ")))[1]
if parsing == "NAME":
kegg_name = line.replace(parsing, "").strip()
parsing = None
if parsing == "DEFINITION":
kegg_definition = line.replace(parsing,"").strip()
kegg_ortholog_set = str(kegg_definition)
for character in list("(+ -)"):
kegg_ortholog_set = kegg_ortholog_set.replace(character, ",")
kegg_ortholog_set = set(filter(bool, kegg_ortholog_set.split(",")))
if parsing == "ORTHOLOGY":
kegg_orthology.append(line.replace(parsing,"").strip())
if parsing == "CLASS":
kegg_classes = line.replace(parsing,"").strip().split("; ")
if parsing == "PATHWAY":
kegg_pathway = line.replace(parsing,"").strip()
kegg_pathways.append(kegg_pathway)
id_pathway = kegg_pathway.split(" ")[0]
kegg_pathway_set.add(id_pathway)
if parsing == "REACTION":
kegg_reaction = line.replace(parsing,"").strip()
kegg_reactions.append(kegg_reaction)
for id_reaction in kegg_reaction.split(" ")[0].split(","):
kegg_reaction_set.add(id_reaction)
if parsing == "COMPOUND":
kegg_compound = line.replace(parsing,"").strip()
id_compound = kegg_compound.split(" ")[0]
kegg_compounds.append(kegg_compound)
kegg_compound_set.add(id_compound)
module_info = pd.Series(
data = OrderedDict([
("NAME",kegg_name),
("DEFINITION",kegg_definition),
("ORTHOLOGY",kegg_orthology),
("ORTHOLOGY_SET",kegg_ortholog_set),
("CLASS",kegg_classes),
("PATHWAY",kegg_pathways),
("PATHWAY_SET",kegg_pathway_set),
("REACTION",kegg_reactions),
("REACTION_SET",kegg_reaction_set),
("COMPOUND",kegg_compounds),
("COMPOUND_SET",kegg_compound_set),
]),
name=kegg_module,
)
return module_info
def get_kegg_modules(expand_nested_modules=True):
results = list()
for line in list(kegg_list("module")):
line = line.strip()
module, name = line.split("\t")
prefix, id_module = module.split(":")
module_file = kegg_get(module)
module_info = parse_kegg_module(module_file)
results.append(module_info)
df = pd.DataFrame(results)
df.index.name = datetime.datetime.now().strftime("Accessed: %Y-%m-%d @ %H:%M")
# Expand nested modules
if expand_nested_modules:
for id_module, row in df.iterrows():
kegg_orthology_set = row["ORTHOLOGY_SET"]
expanded = set()
for x in kegg_orthology_set:
if x.startswith("K"):
expanded.add(x)
if x.startswith("M"):
for id_ko in df.loc[x,"ORTHOLOGY_SET"]:
expanded.add(id_ko)
df.loc[id_module, "ORTHOLOGY_SET"] = expanded
return df