def ExtractPathway_WTD(options):
# Extract pathways and WTD
# place to store list of expected taxonomic range(s)
printf('INFO\tEntering the WTD calculations!\n')
serialized_metacyc_taxa_ranges = "/tmp/metacyc_pwy_taxa_range.pk"
serialized_metacyc_taxa_ranges_tmp = "/tmp/metacyc_pwy_taxa_range.pk.tmp"
try:
if options.wtd and not path.isfile(serialized_metacyc_taxa_ranges):
# get MetaCyc's expected taxonomic range(s) and serialize for later use in /tmp
# try:
printf('INFO\tGetting MetaCyc Expected Taxonomic Range(s)\n')
pythonCyc = startPathwayTools('meta', options.ptoolsExec, True)
pwys = pythonCyc.getAllPathways()
pwy_taxa_range = {} # hash from pwy to expected taxonomic range(s)
pwy_taxa_range_pk = open(serialized_metacyc_taxa_ranges_tmp, "w")
# get expected taxonomic ranges for each pathway
for pwy in pwys:
# printf(" " + pwy)
my_expected_taxonomic_range = pythonCyc.getExpectedTaxonomicRange(
pwy)
pwy_taxa_range[pwy] = my_expected_taxonomic_range
# printf(" " + pwy)
# write the pathway
pickle.dump(pwy_taxa_range, pwy_taxa_range_pk)
pwy_taxa_range_pk.close()
StopPathwayTools()
rename(serialized_metacyc_taxa_ranges_tmp,
serialized_metacyc_taxa_ranges)
else:
# read expected taxonomic range from serialized file
exepected_taxa_in = open(serialized_metacyc_taxa_ranges, "r")
pwy_taxa_range = pickle.load(exepected_taxa_in)
# create mapping of preferred NCBI to MEGAN taxonomy
megan_map = {}
if options.ncbi_megan_map:
with open(options.ncbi_megan_map) as megan_map_file:
for line in megan_map_file:
fields = line.split("\t")
fields = map(str.strip, fields)
megan_map[fields[0]] = fields[1]
# get ORF to taxa map from annotation_table
printf("INFO\tGetting ORF to Taxa Map from AnnotationTable\n")
orf_lca = {}
with open(options.annotation_table) as f:
for line in f:
fields = line.split("\t")
orf_lca[fields[0].strip()] = fields[8].strip()
# get pathway ORFs and Rxns
pwy_to_orfs = {}
pwy_to_long = {}
pwy_to_rxns = {}
try:
pythonCyc = startPathwayTools(options.sample_name.lower(),
options.ptoolsExec, True)
pwys = pythonCyc.getAllPathways()
for pwy in pwys:
# printf(" " + pwy)
genes = pythonCyc.getPathwayORFs(pwy)
rxns = pythonCyc.getPathwayReactionInfo(pwy)
pwy_to_orfs[pwy] = genes
pwy_to_long[pwy] = cleanup(
pythonCyc.get_slot_value(pwy, "common-name"))
pwy_to_rxns[pwy] = rxns
# printf("\n")
StopPathwayTools()
except:
print """
Problem connecting to Pathway Tools. Check the /tmp/ptools-socket file.
"""
except:
print """
Problem calculating WTD via Pathway Tools. Check the /tmp/ptools-socket file.
"""
# get LCA per pathway
pwy_lca = {}
# load NCBI taxonomy map
printf("INFO\tLoading NCBI Taxonomy Map\n")
lca = LCAComputation([options.ncbi_tree], )
for pwy in pwy_to_orfs:
orfs = pwy_to_orfs[pwy]
taxa_ids = []
for orf in orfs:
if orf in orf_lca:
# could strip out id here
res = re.search("(.+?)\(([0-9]+?)\)", orf_lca[orf])
if res:
taxa_annotation = res.group(1)
id = res.group(2)
else:
id = lca.get_a_Valid_ID([orf_lca[orf]])
taxa_ids.append(id)
pwy_lca_id = lca.get_lca(taxa_ids, True)
# print "In run_pathologic"
# print pwy_lca_id
# print pwy_lca_id
lca.clear_cells(taxa_ids)
pwy_lca[pwy] = [pwy_lca_id, lca.translateIdToName(pwy_lca_id)]
# calculate weighted taxonomic distance
pwy_to_wtd = {}
for pwy in pwy_lca:
C = [] # list of distances
C_taxa = [] # list of parallel observed-expected taxa pairs
C_pos = [] # list of non-negative distances
C_pos_taxa = [] # list of parallel observed-expected taxa pairs
C_neg = [] # list of negative distances
C_neg_taxa = [] # list of parallel observed-expected taxa pairs
if len(pwy_taxa_range[pwy]) > 0:
for expected in pwy_taxa_range[pwy]:
dist = lca.wtd(expected[0], pwy_lca[pwy][0])
if dist or dist == 0:
# valid distance
# add distance respective lists
C.append(dist) # add distance
C_taxa.append([expected[0], pwy_lca[pwy][0]])
if dist >= 0:
C_pos.append(dist) # add to non-negative list
C_pos_taxa.append([expected[0], pwy_lca[pwy][0]])
else:
C_neg.append(dist) # add to negative list
C_neg_taxa.append([expected[0], pwy_lca[pwy][0]])
else:
print "Not a valid distance"
continue
else:
# no expected taxonomy, set to root
min_taxa = "1"
dist = lca.wtd(min_taxa, pwy_lca[pwy][0])
# add distance respective lists
C.append(dist) # add distance
C_taxa.append([min_taxa, pwy_lca[pwy][0]])
if dist >= 0:
C_pos.append(dist) # add to non-negative list
C_pos_taxa.append([min_taxa, pwy_lca[pwy][0]])
else:
C_neg.append(dist) # add to negative list
C_neg_taxa.append([min_taxa, pwy_lca[pwy][0]])
# find index with max distance (closest to expected taxonomy)
max_index, max_dist = max(enumerate(C), key=operator.itemgetter(1))
max_taxa = C_taxa[max_index]
# remap to preferred names
observed = get_preferred_taxa_name(max_taxa[1], megan_map,
lca.id_to_name)
expected = get_preferred_taxa_name(max_taxa[0], megan_map,
lca.id_to_name)
pwy_to_wtd[pwy] = [max_dist, observed, expected]
# write out pathway table
table_out_tmp = options.table_out + ".tmp"
try:
out = open(table_out_tmp, "w")
except:
print "Had problems opening file: " + options.table_out
# write appropreate header
if options.wtd:
header = "SAMPLE\tPWY_NAME\tPWY_COMMON_NAME\tNUM_REACTIONS\tNUM_COVERED_REACTIONS\tORF_COUNT\tWTD\tOBSERVED\tEXPECTED\tORFS\n"
else:
header = "SAMPLE\tPWY_NAME\tPWY_COMMON_NAME\tNUM_REACTIONS\tNUM_COVERED_REACTIONS\tORF_COUNT\tORFS\n"
out.write(header)
sample = options.sample_name # sample name
for pwy in pwy_to_orfs:
# generate output line
line = []
line.append(sample) # sample name
line.append(pwy) # pathway name
line.append(pwy_to_long[pwy]) # pathway longname
line.append(pwy_to_rxns[pwy][0]) # pathway num reactions
line.append(pwy_to_rxns[pwy][1]) # pathway covered reactions
line.append(len(pwy_to_orfs[pwy])) # num orfs
if options.wtd:
line.append(pwy_to_wtd[pwy][0]) # wtd
line.append(pwy_to_wtd[pwy][1]) # wtd observed taxa
line.append(pwy_to_wtd[pwy][2]) # wtd expected taxa
line.append("[" + ",".join(pwy_to_orfs[pwy]) + "]") # list of ORFs
line = map(str, line) # cast all to string
out.write("\t".join(line) + "\n") # write out line
try:
out.close() # close file
rename(table_out_tmp, options.table_out)
except:
print "Had problems closing file: " + options.table_out
def ExtractPathway_WTD(options):
# Extract pathways and WTD
# place to store list of expected taxonomic range(s)
printf('\n')
printf('INFO\tEntering the WTD calculations!\n')
serialized_metacyc_taxa_ranges = "/tmp/metacyc_pwy_taxa_range.pk"
try:
#print options.wtd, not path.isfile(serialized_metacyc_taxa_ranges), serialized_metacyc_taxa_ranges
if options.wtd and not path.isfile(serialized_metacyc_taxa_ranges):
# get MetaCyc's expected taxonomic range(s) and serialize for later use in /tmp
# try:
printf('INFO\tGetting MetaCyc Expected Taxonomic Range(s)\n')
pythonCyc = startPathwayTools('meta', options.ptoolsExec, True)
pwys = pythonCyc.getAllPathways()
pwy_taxa_range = {} # hash from pwy to expected taxonomic range(s)
pwy_taxa_range_pk = open(serialized_metacyc_taxa_ranges ,"w")
# get expected taxonomic ranges for each pathway
for pwy in pwys:
printf(" " + pwy)
my_expected_taxonomic_range = pythonCyc.getExpectedTaxonomicRange(pwy)
pwy_taxa_range[pwy] = my_expected_taxonomic_range
# printf(" " + pwy)
# write the pathway
pickle.dump(pwy_taxa_range, pwy_taxa_range_pk)
pwy_taxa_range_pk.close()
StopPathwayTools()
# read expected taxonomic range from serialized file
exepected_taxa_in = open(serialized_metacyc_taxa_ranges ,"r")
pwy_taxa_range = pickle.load(exepected_taxa_in)
# create mapping of preferred NCBI to MEGAN taxonomy
megan_map = {}
if options.ncbi_megan_map:
with open(options.ncbi_megan_map) as megan_map_file:
for line in megan_map_file:
fields = line.split("\t")
fields = map(str.strip, fields)
megan_map[ fields[0] ] = fields[1]
# get ORF to taxa map from annotation_table
printf("INFO\tGetting ORF to Taxa Map from AnnotationTable\n")
orf_lca = {}
with open(options.annotation_table) as f:
for line in f:
fields = line.split("\t")
orf_lca[fields[0].strip()] = fields[8].strip()
# get pathway ORFs and Rxns
pwy_to_orfs = {}
pwy_to_long = {}
pwy_to_rxns = {}
try:
pythonCyc = startPathwayTools(options.sample_name.lower(), options.ptoolsExec, True)
pwys = pythonCyc.getAllPathways()
for pwy in pwys:
printf(" " + pwy)
genes = pythonCyc.getPathwayORFs(pwy)
rxns = pythonCyc.getPathwayReactionInfo(pwy)
pwy_to_orfs[pwy] = genes
pwy_to_long[pwy] = cleanup(pythonCyc.get_slot_value(pwy, "common-name"))
pwy_to_rxns[pwy] = rxns
# printf("\n")
StopPathwayTools()
except:
insert_error(9)
print """
Problem connecting to Pathway Tools. Check the /tmp/ptools-socket file.
"""
except:
print """
Problem calculating WTD via Pathway Tools. Check the /tmp/ptools-socket file.
"""
insert_error(9)
# get LCA per pathway
pwy_lca = {}
# load NCBI taxonomy map
printf("\nINFO\tLoading NCBI Taxonomy Map\n")
lca = LCAComputation([ options.ncbi_tree ], )
for pwy in pwy_to_orfs:
orfs = pwy_to_orfs[pwy]
taxa_ids = []
for orf in orfs:
if orf in orf_lca:
# could strip out id here
res = re.search("(.+?)\(([0-9]+?)\)", orf_lca[orf] )
if res:
taxa_annotation = res.group(1)
id = res.group(2)
else:
id = lca.get_a_Valid_ID([ orf_lca[orf] ])
taxa_ids.append(id)
pwy_lca_id = lca.get_lca(taxa_ids, True)
# print "In run_pathologic"
# print pwy_lca_id
# print pwy_lca_id
lca.clear_cells(taxa_ids)
pwy_lca[pwy] = [pwy_lca_id, lca.translateIdToName(pwy_lca_id)]
# calculate weighted taxonomic distance
pwy_to_wtd = {}
printf("INFO\tCalculating WTD\n")
for pwy in pwy_lca:
C = [] # list of distances
C_taxa = [] # list of parallel observed-expected taxa pairs
C_pos = [] # list of non-negative distances
C_pos_taxa = [] # list of parallel observed-expected taxa pairs
C_neg = [] # list of negative distances
C_neg_taxa = [] # list of parallel observed-expected taxa pairs
if pwy in pwy_taxa_range and len(pwy_taxa_range[pwy]) :
for expected in pwy_taxa_range[pwy]:
dist = lca.wtd(expected[0], pwy_lca[pwy][0])
if dist or dist == 0:
# valid distance
# add distance respective lists
C.append(dist) # add distance
C_taxa.append([ expected[0], pwy_lca[pwy][0] ])
if dist >= 0:
C_pos.append(dist) # add to non-negative list
C_pos_taxa.append([ expected[0], pwy_lca[pwy][0] ])
else:
C_neg.append(dist) # add to negative list
C_neg_taxa.append([ expected[0], pwy_lca[pwy][0] ])
else:
print "Not a valid distance"
continue
else:
# no expected taxonomy, set to root
min_taxa = "1"
dist = lca.wtd(min_taxa, pwy_lca[pwy][0])
# add distance respective lists
C.append(dist) # add distance
C_taxa.append([ min_taxa, pwy_lca[pwy][0] ])
if dist >= 0:
C_pos.append(dist) # add to non-negative list
C_pos_taxa.append([ min_taxa, pwy_lca[pwy][0] ])
else:
C_neg.append(dist) # add to negative list
C_neg_taxa.append([ min_taxa, pwy_lca[pwy][0] ])
# find index with max distance (closest to expected taxonomy)
max_index, max_dist = max(enumerate(C), key=operator.itemgetter(1))
max_taxa = C_taxa[max_index]
# remap to preferred names
observed = get_preferred_taxa_name(max_taxa[1], megan_map, lca.id_to_name)
expected = get_preferred_taxa_name(max_taxa[0], megan_map, lca.id_to_name)
pwy_to_wtd[pwy] = [ max_dist, observed, expected ]
# write out pathway table
table_out_tmp = options.table_out + ".tmp"
try:
out = open(table_out_tmp, "w")
except:
print "Had problems opening file: " + options.table_out
insert_error(9)
# write appropreate header
if options.wtd:
header = "SAMPLE\tPWY_NAME\tPWY_COMMON_NAME\tNUM_REACTIONS\tNUM_COVERED_REACTIONS\tORF_COUNT\tWTD\tOBSERVED\tEXPECTED\tORFS\n"
else:
header = "SAMPLE\tPWY_NAME\tPWY_COMMON_NAME\tNUM_REACTIONS\tNUM_COVERED_REACTIONS\tORF_COUNT\tORFS\n"
out.write(header)
sample = options.sample_name # sample name
for pwy in pwy_to_orfs:
# generate output line
line = []
line.append(sample) # sample name
line.append(pwy) # pathway name
line.append(pwy_to_long[pwy]) # pathway longname
line.append(pwy_to_rxns[pwy][0]) # pathway num reactions
line.append(pwy_to_rxns[pwy][1]) # pathway covered reactions
line.append(len(pwy_to_orfs[pwy])) # num orfs
if options.wtd:
line.append(pwy_to_wtd[pwy][0]) # wtd
line.append(pwy_to_wtd[pwy][1]) # wtd observed taxa
line.append(pwy_to_wtd[pwy][2]) # wtd expected taxa
line.append("[" + ",".join(pwy_to_orfs[pwy]) + "]") # list of ORFs
line = map(str, line) # cast all to string
out.write("\t".join(line) + "\n") # write out line
try:
out.close() # close file
rename(table_out_tmp, options.table_out)
except:
print "Had problems closing file: " + options.table_out
insert_error(9)
def main(argv):
global parser
(opts, args) = parser.parse_args()
if not check_arguments(opts, args):
print usage
sys.exit(0)
# place to store list of expected taxonomic range(s)
serialized_metacyc_taxa_ranges = "/tmp/metacyc_pwy_taxa_range.pk"
if opts.wtd and not os.path.isfile(serialized_metacyc_taxa_ranges):
# get MetaCyc's expected taxonomic range(s) and serialize for later use in /tmp
try:
print "Getting MetaCyc Expected Taxonomic Range(s)"
# connect to Pathway Tools
cyc = PythonCyc()
cyc.setOrganism("meta")
cyc.setPToolsExec(opts.pathway_tools)
cyc.startPathwayTools()
pwys = cyc.getAllPathways()
pwy_taxa_range = {} # hash from pwy to expected taxonomic range(s)
pwy_taxa_range_pk = open(serialized_metacyc_taxa_ranges, "w")
# get expected taxonomic ranges for each pathway
for pwy in pwys:
my_expected_taxonomic_range = cyc.getExpectedTaxonomicRange(pwy)
pwy_taxa_range[pwy] = my_expected_taxonomic_range
# write the pathway
pickle.dump(pwy_taxa_range, pwy_taxa_range_pk)
pwy_taxa_range_pk.close()
# close Pathway Tools
cyc.stopPathwayTools()
except:
print """
Problem connecting to Pathway Tools. Check the /tmp/ptools-socket file.
"""
else:
# read expected taxonomic range from serialized file
exepected_taxa_in = open(serialized_metacyc_taxa_ranges, "r")
pwy_taxa_range = pickle.load(exepected_taxa_in)
# create mapping of preferred NCBI to MEGAN taxonomy
megan_map = {}
if opts.ncbi_megan_map:
with open(opts.ncbi_megan_map) as megan_map_file:
for line in megan_map_file:
fields = line.split("\t")
fields = map(str.strip, fields)
megan_map[fields[0]] = fields[1]
# get ORF to taxa map from annotation_table
print "Getting ORF to Taxa Map from AnnotationTable"
orf_lca = {}
with open(opts.annotation_table) as f:
for line in f:
fields = line.split("\t")
orf_lca[fields[0].strip()] = fields[8].strip()
# get pathway ORFs and Rxns
pwy_to_orfs = {}
pwy_to_long = {}
pwy_to_rxns = {}
try:
cyc = PythonCyc()
cyc.setOrganism(opts.pgdb_name)
cyc.setPToolsExec(opts.pathway_tools)
cyc.startPathwayTools()
pwys = cyc.getAllPathways()
for pwy in pwys:
genes = cyc.getPathwayORFs(pwy)
rxns = cyc.getPathwayReactionInfo(pwy)
pwy_to_orfs[pwy] = genes
pwy_to_long[pwy] = cleanup(cyc.get_slot_value(pwy, "common-name"))
pwy_to_rxns[pwy] = rxns
cyc.stopPathwayTools()
except:
print """
Problem connecting to Pathway Tools. Check the /tmp/ptools-socket file.
"""
# get LCA per pathway
pwy_lca = {}
# load NCBI taxonomy map
print "Loading NCBI Taxonomy Map"
lca = LCAComputation([opts.ncbi_tree])
lca.setParameters(opts.lca_min_score, opts.lca_top_percent, opts.lca_min_support)
for pwy in pwy_to_orfs:
orfs = pwy_to_orfs[pwy]
taxa_ids = []
for orf in orfs:
if orf in orf_lca:
id = lca.get_a_Valid_ID([orf_lca[orf]])
taxa_ids.append(id)
pwy_lca_id = lca.get_lca(taxa_ids, True)
lca.clear_cells(taxa_ids)
pwy_lca[pwy] = [pwy_lca_id, lca.translateIdToName(pwy_lca_id)]
# calculate weighted taxonomic distance
pwy_to_wtd = {}
for pwy in pwy_lca:
C = [] # list of distances
C_taxa = [] # list of parallel observed-expected taxa pairs
C_pos = [] # list of non-negative distances
C_pos_taxa = [] # list of parallel observed-expected taxa pairs
C_neg = [] # list of negative distances
C_neg_taxa = [] # list of parallel observed-expected taxa pairs
if pwy in pwy_taxa_range:
if len(pwy_taxa_range[pwy]) > 0:
for expected in pwy_taxa_range[pwy]:
dist = lca.wtd(expected[0], pwy_lca[pwy][0])
if dist or dist == 0:
# valid distance
# add distance respective lists
C.append(dist) # add distance
C_taxa.append([expected[0], pwy_lca[pwy][0]])
if dist >= 0:
C_pos.append(dist) # add to non-negative list
C_pos_taxa.append([expected[0], pwy_lca[pwy][0]])
else:
C_neg.append(dist) # add to negative list
C_neg_taxa.append([expected[0], pwy_lca[pwy][0]])
else:
print "Not a valid distance"
continue
else:
# no expected taxonomy, set to root
min_taxa = "1"
dist = lca.wtd(min_taxa, pwy_lca[pwy][0])
# add distance respective lists
C.append(dist) # add distance
C_taxa.append([min_taxa, pwy_lca[pwy][0]])
if dist >= 0:
C_pos.append(dist) # add to non-negative list
C_pos_taxa.append([min_taxa, pwy_lca[pwy][0]])
else:
C_neg.append(dist) # add to negative list
C_neg_taxa.append([min_taxa, pwy_lca[pwy][0]])
# find index with max distance (closest to expected taxonomy)
max_index, max_dist = max(enumerate(C), key=operator.itemgetter(1))
max_taxa = C_taxa[max_index]
# remap to preferred names
observed = get_preferred_taxa_name(max_taxa[1], megan_map, lca.id_to_name)
expected = get_preferred_taxa_name(max_taxa[0], megan_map, lca.id_to_name)
pwy_to_wtd[pwy] = [max_dist, observed, expected]
# write out pathway table
try:
out = open(opts.table_out, "w")
except:
print "Had problems opening file: " + opts.table_out
# write appropreate header
if opts.wtd:
header = "SAMPLE\tPWY_NAME\tPWY_COMMON_NAME\tNUM_REACTIONS\tNUM_COVERED_REACTIONS\tORF_COUNT\tWTD\tOBSERVED\tEXPECTED\tORFS\n"
else:
header = "SAMPLE\tPWY_NAME\tPWY_COMMON_NAME\tNUM_REACTIONS\tNUM_COVERED_REACTIONS\tORF_COUNT\tORFS\n"
out.write(header)
sample = opts.pgdb_name # sample name
for pwy in pwy_to_orfs:
# generate output line
line = []
line.append(sample) # sample name
line.append(pwy) # pathway name
line.append(pwy_to_long[pwy]) # pathway longname
line.append(pwy_to_rxns[pwy][0]) # pathway num reactions
line.append(pwy_to_rxns[pwy][1]) # pathway covered reactions
line.append(len(pwy_to_orfs[pwy])) # num orfs
if opts.wtd:
if pwy in pwy_to_wtd:
line.append(pwy_to_wtd[pwy][0]) # wtd
line.append(pwy_to_wtd[pwy][1]) # wtd observed taxa
line.append(pwy_to_wtd[pwy][2]) # wtd expected taxa
else:
line.append("NA")
line.append("NA")
line.append("NA")
line.append("[" + ",".join(pwy_to_orfs[pwy]) + "]") # list of ORFs
line = map(str, line) # cast all to string
out.write("\t".join(line) + "\n") # write out line
try:
out.close() # close file
except:
print "Had problems closing file: " + opts.table_out
def main(argv):
global parser
(opts, args) = parser.parse_args()
if not check_arguments(opts, args):
print(usage)
sys.exit(0)
# place to store list of expected taxonomic range(s)
serialized_metacyc_taxa_ranges = "/tmp/metacyc_pwy_taxa_range.pk"
if opts.wtd and not os.path.isfile(serialized_metacyc_taxa_ranges):
# get MetaCyc's expected taxonomic range(s) and serialize for later use in /tmp
try:
print('Getting MetaCyc Expected Taxonomic Range(s)')
# connect to Pathway Tools
cyc = PythonCyc()
cyc.setOrganism('meta')
cyc.setPToolsExec(opts.pathway_tools)
cyc.startPathwayTools()
pwys = cyc.getAllPathways()
pwy_taxa_range = {} # hash from pwy to expected taxonomic range(s)
pwy_taxa_range_pk = open(serialized_metacyc_taxa_ranges ,"w")
# get expected taxonomic ranges for each pathway
for pwy in pwys:
my_expected_taxonomic_range = cyc.getExpectedTaxonomicRange(pwy)
pwy_taxa_range[pwy] = my_expected_taxonomic_range
# write the pathway
pickle.dump(pwy_taxa_range, pwy_taxa_range_pk)
pwy_taxa_range_pk.close()
# close Pathway Tools
cyc.stopPathwayTools()
except:
print( """
Problem connecting to Pathway Tools. Check the /tmp/ptools-socket file.
""")
else:
# read expected taxonomic range from serialized file
exepected_taxa_in = open(serialized_metacyc_taxa_ranges ,"r")
pwy_taxa_range = pickle.load(exepected_taxa_in)
# create mapping of preferred NCBI to MEGAN taxonomy
megan_map = {}
if opts.ncbi_megan_map:
with open(opts.ncbi_megan_map) as megan_map_file:
for line in megan_map_file:
fields = line.split("\t")
fields = map(str.strip, fields)
megan_map[ fields[0] ] = fields[1]
# get ORF to taxa map from annotation_table
print("Getting ORF to Taxa Map from AnnotationTable")
orf_lca = {}
with open(opts.annotation_table) as f:
for line in f:
fields = line.split("\t")
orf_lca[fields[0].strip()] = fields[8].strip()
# get pathway ORFs and Rxns
pwy_to_orfs = {}
pwy_to_long = {}
pwy_to_rxns = {}
try:
cyc = PythonCyc()
cyc.setOrganism(opts.pgdb_name)
cyc.setPToolsExec(opts.pathway_tools)
cyc.startPathwayTools()
pwys = cyc.getAllPathways()
for pwy in pwys:
genes = cyc.getPathwayORFs(pwy)
rxns = cyc.getPathwayReactionInfo(pwy)
pwy_to_orfs[pwy] = genes
pwy_to_long[pwy] = cleanup(cyc.get_slot_value(pwy, "common-name"))
pwy_to_rxns[pwy] = rxns
cyc.stopPathwayTools()
except:
print("""
Problem connecting to Pathway Tools. Check the /tmp/ptools-socket file.
""")
# get LCA per pathway
pwy_lca = {}
# load NCBI taxonomy map
print("Loading NCBI Taxonomy Map")
lca = LCAComputation([ opts.ncbi_tree ])
lca.setParameters(opts.lca_min_score, opts.lca_top_percent, opts.lca_min_support)
for pwy in pwy_to_orfs:
orfs = pwy_to_orfs[pwy]
taxa_ids = []
for orf in orfs:
if orf in orf_lca:
id = lca.get_a_Valid_ID([ orf_lca[orf] ])
taxa_ids.append(id)
pwy_lca_id = lca.get_lca(taxa_ids, True)
lca.clear_cells(taxa_ids)
pwy_lca[pwy] = [pwy_lca_id, lca.translateIdToName(pwy_lca_id)]
# calculate weighted taxonomic distance
pwy_to_wtd = {}
for pwy in pwy_lca:
C = [] # list of distances
C_taxa = [] # list of parallel observed-expected taxa pairs
C_pos = [] # list of non-negative distances
C_pos_taxa = [] # list of parallel observed-expected taxa pairs
C_neg = [] # list of negative distances
C_neg_taxa = [] # list of parallel observed-expected taxa pairs
if pwy in pwy_taxa_range:
if len(pwy_taxa_range[pwy]) > 0:
for expected in pwy_taxa_range[pwy]:
dist = lca.wtd(expected[0], pwy_lca[pwy][0])
if dist or dist == 0:
# valid distance
# add distance respective lists
C.append(dist) # add distance
C_taxa.append([ expected[0], pwy_lca[pwy][0] ])
if dist >= 0:
C_pos.append(dist) # add to non-negative list
C_pos_taxa.append([ expected[0], pwy_lca[pwy][0] ])
else:
C_neg.append(dist) # add to negative list
C_neg_taxa.append([ expected[0], pwy_lca[pwy][0] ])
else:
print("Not a valid distance")
continue
else:
# no expected taxonomy, set to root
min_taxa = "1"
dist = lca.wtd(min_taxa, pwy_lca[pwy][0])
# add distance respective lists
C.append(dist) # add distance
C_taxa.append([ min_taxa, pwy_lca[pwy][0] ])
if dist >= 0:
C_pos.append(dist) # add to non-negative list
C_pos_taxa.append([ min_taxa, pwy_lca[pwy][0] ])
else:
C_neg.append(dist) # add to negative list
C_neg_taxa.append([ min_taxa, pwy_lca[pwy][0] ])
# find index with max distance (closest to expected taxonomy)
max_index, max_dist = max(enumerate(C), key=operator.itemgetter(1))
max_taxa = C_taxa[max_index]
# remap to preferred names
observed = get_preferred_taxa_name(max_taxa[1], megan_map, lca.id_to_name)
expected = get_preferred_taxa_name(max_taxa[0], megan_map, lca.id_to_name)
pwy_to_wtd[pwy] = [ max_dist, observed, expected ]
# write out pathway table
try:
out = open(opts.table_out, "w")
except:
print("Had problems opening file: " + opts.table_out)
# write appropreate header
if opts.wtd:
header = "SAMPLE\tPWY_NAME\tPWY_COMMON_NAME\tNUM_REACTIONS\tNUM_COVERED_REACTIONS\tORF_COUNT\tWTD\tOBSERVED\tEXPECTED\tORFS\n"
else:
header = "SAMPLE\tPWY_NAME\tPWY_COMMON_NAME\tNUM_REACTIONS\tNUM_COVERED_REACTIONS\tORF_COUNT\tORFS\n"
out.write(header)
sample = opts.pgdb_name # sample name
for pwy in pwy_to_orfs:
# generate output line
line = []
line.append(sample) # sample name
line.append(pwy) # pathway name
line.append(pwy_to_long[pwy]) # pathway longname
line.append(pwy_to_rxns[pwy][0]) # pathway num reactions
line.append(pwy_to_rxns[pwy][1]) # pathway covered reactions
line.append(len(pwy_to_orfs[pwy])) # num orfs
if opts.wtd:
if pwy in pwy_to_wtd:
line.append(pwy_to_wtd[pwy][0]) # wtd
line.append(pwy_to_wtd[pwy][1]) # wtd observed taxa
line.append(pwy_to_wtd[pwy][2]) # wtd expected taxa
else:
line.append("NA")
line.append("NA")
line.append("NA")
line.append("[" + ",".join(pwy_to_orfs[pwy]) + "]") # list of ORFs
line = map(str, line) # cast all to string
out.write("\t".join(line) + "\n") # write out line
try:
out.close() # close file
except:
print("Had problems closing file: " + opts.table_out)
