def armiparser(self):
"""Creates a dictionary that can be put into Mike's ARMI decipher function
:param parseddict: dictionary of parsed results
:param seqdict: dictionary containing import paths and file names
:param analysistype: string of the current analysis type
:param reportfolder: folder in which reports are stored
"""
from ARMICARD import decipher
# Initialise a variable to store the target path used in creating the dictionary of antimicrobial resistances
targetpath = ""
# Initialise the dictionary
targetdict = defaultdict(bamPysamStats.make_dict)
# Iterate through the strains in the analysis
for strain in self.seqdict:
# Iterate through all the targets
for target in sorted(self.seqdict[strain]["targets"][self.analysistype]):
# Initialise targetpresent as false - this will change only if the percent identity is greater than the
# identity cutoff
targetpresent = False
# Create the targetname variable from the target
targetname = os.path.basename(target).split(".")[0]
targetpath = os.path.split(target)[0]
# Iterate through all the alleles for each target in parseddict
for allele in self.parseddict[strain][target]:
# Initialise the totaldepth and the number of nonsnps (number of matches to the reference)
totaldepth = 0
nonsnps = 0
# Retrieve the length of the allele
contiglength = len(self.seqdict[strain]["targetSequences"][self.analysistype]
[target]["allele"][allele])
# Iterate through each position in the allele
for pos in self.parseddict[strain][target][allele]:
# Number of matches
for matches in self.parseddict[strain][target][allele][pos]:
# Number of mismatches and depth of coverage
for mismatches, depth in self.parseddict[strain][target][allele][pos][matches].iteritems():
# Each position represents a non-SNP due to pre-filtering
nonsnps += 1
# Increment the total depth
totaldepth += depth
# Calculate the total percent identity
currentidentity = float("%.2f" % (float(nonsnps)/contiglength * 100))
print allele, currentidentity
# If this identity is greater than the cutoff
if currentidentity >= self.identitycutoff:
# The target is present in the strain
targetpresent = True
# If the target is present, add a plus to Dict
if targetpresent:
targetdict[strain][targetname] = ["+"]
# Set the path of the resistance dictionary
# antidict = json.load(open("/media/nas0/Jackson/ARMI_Docker/ARMI/aro3.json"))
antidict = json.load(open("%s/aro3.json" % targetpath))
# Send the dictionaries, and report locations to the decipher function
# import json
# print json.dumps(targetdict, sort_keys=True, indent=4, separators=(',', ': '))
decipher(targetdict, antidict, self.reportfolder + "/geneSippr")
return targetdict
def helper(genes, targets, out, cuttoff, aro, threads):
from glob import glob
assert os.path.isdir(out), u'Output location is not a valid directory {0!r:s}'.format(out)
assert os.path.isfile(genes), u'ARMI-genes.fa not valid {0!r:s}'.format(genes)
assert os.path.isfile(aro), u'Antibiotic JSON not valid {0!r:s}'.format(aro)
assert isinstance(threads, int)
ispath = (lambda x: glob(x + "/*.fa*") if os.path.isdir(x) else [x])
genes = ispath(genes)
targets = ispath(targets)
result = GeneSeekr(genes, targets, threads)
result.mpblast(cuttoff)
json.dump(result.plus, open("%s/ARMI-gene_results_%s.json" % (out, time.strftime("%Y.%m.%d.%H.%M.%S")), 'w'),
sort_keys=True, indent=4, separators=(',', ': '))
decipher(result.plus, json.load(open(aro)), out)
def parseDict():
global holdingDict
global seqDict
global plusdict
global strain
for gene in seqDict:
for aros in seqDict[gene]:
length = seqDict[gene][aros]
# print gene, length, aros
# aro = aros.split(" ")
# for ar in aro:
# # pass
# # if len(aro) > 1:
matches = 0
totalDepth = 0
minDepth = 10
# for presence in sorted(holdingDict[gene].items(), key=operator.itemgetter(0)):
for presence in sorted(holdingDict[gene]):
depth = holdingDict[gene][presence]
matches += 1
totalDepth += depth
if depth < minDepth:
minDepth = depth
# if gene == "AP009048.1.gene3309" and aros == "3000502":
# print gene, aros, length, presence, type(presence), matches, totalDepth
averageDepth = float("%0.2f" % (float(totalDepth) / float(length)))
percentID = float("%0.2f" % (float(matches) / float(length))) * 100
# print gene, aros, matches, length, percentID, averageDepth
if percentID > 70 and minDepth > 4:
plusdict[strain][aros] = ["+"]
# print gene, aros, matches, length, percentID, averageDepth
else:
plusdict[strain][aros] = []
antidict = json.load(open("/media/nas0/Jackson/ARMI_Docker/ARMI/aro3.json"))
resDict = decipher(plusdict, antidict, "/media/nas/akoziol/Pipeline_development/GeneSipperV2/baitTest/results/armi70_5")
def armiparser(parseddict, seqdict, analysistype, reportfolder):
"""Creates a dictionary that can be put into Mike's ARMI decipher function
:param parseddict: dictionary of parsed results
:param seqdict: dictionary containing import paths and file names
:param analysistype: string of the current analysis type
:param reportfolder: folder in which reports are stored
"""
# import bamPysamStats
# seqdict = bamPysamStats.targetlength(seqdict, analysistype)
# Initialise a variable to store the target path used in creating the dictionary of antimicrobial resistances
targetpath = ""
# Initialise the dictionary
targetdict = defaultdict(make_dict)
# Iterate through the strains in the analysis
for strain in seqdict:
# Get the identity cutoff from seqdict - need to get baittype first
identitycutoff = seqdict[strain]["cutoff"][analysistype]
# Iterate through all the targets
for target in seqdict[strain]["targets"][analysistype]:
# Initialise targetpresent as false - this will change only if the percent identity is greater than the
# identity cutoff
targetpresent = False
# Create the targetname variable from the target
targetname = os.path.basename(target).split(".")[0]
targetpath = os.path.split(target)[0]
# Iterate through all the alleles for each target in parseddict
for allele in parseddict[strain][targetname]:
# Iterate through the percent identity of each allele
for percentidentity in parseddict[strain][targetname][allele]:
# If this identity is greater than the cutoff
if percentidentity >= identitycutoff:
# The target is present in the strain
targetpresent = True
# If the target is present, add a plus to Dict
if targetpresent:
targetdict[strain][targetname] = ["+"]
# Set the path of the resistance dictionary
antidict = json.load(open("%s/aro3.json" % targetpath))
# Send the dictionaries, and report locations to the decipher function
decipher(targetdict, antidict, reportfolder + "/geneSippr")
# print json.dumps(antidict, sort_keys=True, indent=4, separators=(',', ': '))
return targetdict
def armiparser(parseddict, seqdict, analysistype, reportfolder):
"""Creates a dictionary that can be put into Mike's ARMI decipher function
:param parseddict: dictionary of parsed results
:param seqdict: dictionary containing import paths and file names
:param analysistype: string of the current analysis type
:param reportfolder: folder in which reports are stored
"""
# import bamPysamStats
# seqdict = bamPysamStats.targetlength(seqdict, analysistype)
# Initialise a variable to store the target path used in creating the dictionary of antimicrobial resistances
targetpath = ""
# Initialise the dictionary
targetdict = defaultdict(make_dict)
# Iterate through the strains in the analysis
for strain in seqdict:
# Get the identity cutoff from seqdict - need to get baittype first
identitycutoff = seqdict[strain]["cutoff"][analysistype]
# Iterate through all the targets
for target in seqdict[strain]["targets"][analysistype]:
# Initialise targetpresent as false - this will change only if the percent identity is greater than the
# identity cutoff
targetpresent = False
# Create the targetname variable from the target
targetname = os.path.basename(target).split(".")[0]
targetpath = os.path.split(target)[0]
# Iterate through all the alleles for each target in parseddict
for allele in parseddict[strain][targetname]:
# Iterate through the percent identity of each allele
for percentidentity in parseddict[strain][targetname][allele]:
# If this identity is greater than the cutoff
if percentidentity >= identitycutoff:
# The target is present in the strain
targetpresent = True
# If the target is present, add a plus to Dict
if targetpresent:
targetdict[strain][targetname] = ["+"]
# Set the path of the resistance dictionary
antidict = json.load(open("%s/aro3.json" % targetpath))
# Send the dictionaries, and report locations to the decipher function
decipher(targetdict, antidict, reportfolder + "/geneSippr")
# print json.dumps(antidict, sort_keys=True, indent=4, separators=(',', ': '))
return targetdict
def blaster(path, targets, out, threshold, db, aro):
if db == "both":
db = ['ardb', 'card']
else:
db = [db]
jsonfile = '%splusdict.json' % targets
# if os.path.isfile(jsonfile):
# plusdict = json.load(open(jsonfile))
#
# else:
markers = glob(path + "/*.fa*")
for marker in markers:
cardcheck = match("^\d{7}$", marker)
if db == 'ardb' and cardcheck is not None:
markers.remove(marker)
elif db == 'card' and cardcheck is None:
markers.remove(marker)
plusdict = ARMISeekr.blaster(markers, targets, out, 'ARMI2')
json.dump(plusdict, open(jsonfile, 'w'), sort_keys=True, indent=4, separators=(',', ': '))
print json.dumps(plusdict, sort_keys=True, indent=4, separators=(',', ': '))
antidict = json.load(open(aro))
decipher(plusdict, antidict, out)
def armiparser(self):
"""Creates a dictionary that can be put into Mike's ARMI decipher function
:param parseddict: dictionary of parsed results
:param seqdict: dictionary containing import paths and file names
:param analysistype: string of the current analysis type
:param reportfolder: folder in which reports are stored
"""
from ARMICARD import decipher
# Initialise a variable to store the target path used in creating the dictionary of antimicrobial resistances
targetpath = ""
# Initialise the dictionary
targetdict = defaultdict(bamPysamStats.make_dict)
# Iterate through the strains in the analysis
for strain in self.seqdict:
# Iterate through all the targets
for target in sorted(
self.seqdict[strain]["targets"][self.analysistype]):
# Initialise targetpresent as false - this will change only if the percent identity is greater than the
# identity cutoff
targetpresent = False
# Create the targetname variable from the target
targetname = os.path.basename(target).split(".")[0]
targetpath = os.path.split(target)[0]
# Iterate through all the alleles for each target in parseddict
for allele in self.parseddict[strain][target]:
# Initialise the totaldepth and the number of nonsnps (number of matches to the reference)
totaldepth = 0
nonsnps = 0
# Retrieve the length of the allele
contiglength = len(self.seqdict[strain]["targetSequences"][
self.analysistype][target]["allele"][allele])
# Iterate through each position in the allele
for pos in self.parseddict[strain][target][allele]:
# Number of matches
for matches in self.parseddict[strain][target][allele][
pos]:
# Number of mismatches and depth of coverage
for mismatches, depth in self.parseddict[strain][
target][allele][pos][matches].iteritems():
# Each position represents a non-SNP due to pre-filtering
nonsnps += 1
# Increment the total depth
totaldepth += depth
# Calculate the total percent identity
currentidentity = float(
"%.2f" % (float(nonsnps) / contiglength * 100))
print allele, currentidentity
# If this identity is greater than the cutoff
if currentidentity >= self.identitycutoff:
# The target is present in the strain
targetpresent = True
# If the target is present, add a plus to Dict
if targetpresent:
targetdict[strain][targetname] = ["+"]
# Set the path of the resistance dictionary
# antidict = json.load(open("/media/nas0/Jackson/ARMI_Docker/ARMI/aro3.json"))
antidict = json.load(open("%s/aro3.json" % targetpath))
# Send the dictionaries, and report locations to the decipher function
# import json
# print json.dumps(targetdict, sort_keys=True, indent=4, separators=(',', ': '))
decipher(targetdict, antidict, self.reportfolder + "/geneSippr")
return targetdict
