def __init__(self, inputobject):
self.metadata = inputobject.runmetadata.samples
self.start = inputobject.starttime
self.commit = inputobject.commit
# Determine the versions of the software used
printtime('Populating metadata', self.start)
self.python = sys.version.replace('\n', '')
self.arch = ", ".join(os.uname())
self.blast = get_version(['blastn', '-version']).decode('utf-8').split('\n')[0].split()[1]
self.spades = get_version(['spades.py', '-v']).decode('utf-8').split('\n')[0].split()[1]
self.bowversion = Bowtie2CommandLine(version=True)()[0].split('\n')[0].split()[-1]
self.samversion = get_version(['samtools', '--version']).decode('utf-8').split('\n')[0].split()[1]
# Qualimap seems to have an Java warning message that doesn't necessarily show up on every system
# Only capture the line that starts with 'Qualimap'
qualimaplist = get_version(['qualimap', '--help']).decode('utf-8').split('\n')
for line in qualimaplist:
if 'QualiMap' in line:
self.qualimap = line.split()[1]
self.mash = get_version(['mash']).decode('utf-8').split('\n')[1].split()[2]
self.prodigal = get_version(['prodigal', '-v']).decode('utf-8').split('\n')[1].split()[1]
self.bbmap = get_version(['bbversion.sh']).decode('utf-8')
self.fastqc = get_version(['fastqc', '--version']).decode('utf-8').split('\n')[0].split()[1]
# Uncomment this once you figure ou where this file is stored.
self.bcl2fastq = "2"
self.perl = get_version(['perl', '-v']).decode('utf-8').split('\n')[1].split('This is ')[1]
self.biopython = Bio.__version__
self.java = get_version(['java', '-showversion']).decode('utf-8').split('\n')[0].split()[2].replace('"', '')
# self.docker = get_version(['docker', 'version']).split('\n')[1].split()[1]
self.versions()
def getrmlsthelper(self):
"""
Makes a system call to rest_auth.py, a Python script modified from
https://github.com/kjolley/BIGSdb/tree/develop/scripts/test
And downloads the most up-to-date rMLST profile and alleles
"""
printtime('Downloading {} alleles'.format(self.analysistype),
self.start)
# Extract the path of the current script from the full path + file name
homepath = os.path.split(os.path.abspath(__file__))[0]
# Set the path/name of the folder to contain the new alleles and profile
newfolder = os.path.join(self.path, self.analysistype)
# Create the path
make_path(newfolder)
# Create arguments to feed into the rest_auth_class script
args = ArgumentParser
args.secret_file = os.path.join(homepath, 'secret.txt')
args.file_path = homepath
args.output_path = newfolder
args.start = self.start
rmlst = rest_auth_class.REST(args)
# Download the profile and alleles
rmlst.main()
# Get the new alleles into a list, and create the combinedAlleles file
alleles = glob(os.path.join(newfolder, '*.tfa'))
self.combinealleles(newfolder, alleles)
def remove(self):
"""Removes unnecessary temporary files generated by the pipeline"""
import shutil
printtime('Removing large and/or temporary files', self.start)
removefolder = list()
for sample in self.metadata:
# Use os.walk to iterate through all the files in the sample output directory
for path, dirs, files in os.walk(sample.general.outputdirectory):
for item in files:
# Use regex to find files to remove
if re.search(".fastq$", item) or re.search(".fastq.gz$", item) or re.search(".bam$", item) \
or re.search(".bt2$", item) or re.search(".tab$", item) or re.search("^before", item) \
or re.search("^baitedtargets", item) or re.search("_combined.csv$", item) \
or re.search("^scaffolds", item) or re.search(".fastg$", item) or re.search(".gfa$", item) \
or re.search(".bai$", item) or 'coregenome' in path or 'prophages' in path:
# Keep the baitedtargets.fa, core genome, and merged metagenome files
if item != 'baitedtargets.fa' and not re.search("coregenome", item) \
and not re.search("paired", item):
# Remove the unnecessary files
try:
os.remove(os.path.join(path, item))
except IOError:
pass
# Clear out the folders
for folder in removefolder:
try:
shutil.rmtree(folder)
except (OSError, TypeError):
pass
def reporter(self):
"""
Creates a report of the results
"""
printtime('Creating {} report'.format(self.analysistype),
self.starttime)
# Create the path in which the reports are stored
make_path(self.reportpath)
header = 'Strain,Serotype\n'
data = ''
with open(
os.path.join(self.reportpath,
'{}.csv'.format(self.analysistype)),
'w') as report:
for sample in self.runmetadata.samples:
if sample.general.bestassemblyfile != 'NA':
data += sample.name + ','
if sample[self.analysistype].results:
serotype = '{oset} ({opid}):{hset} ({hpid}),' \
.format(oset=';'.join(sample.serosippr.o_set),
opid=sample.serosippr.best_o_pid,
hset=';'.join(sample.serosippr.h_set),
hpid=sample.serosippr.best_h_pid)
data += '{}\n'.format(serotype)
else:
data += '\n'
report.write(header)
report.write(data)
def parse_qaml(self):
"""
Parse the GenomeQAML report, and populate metadata objects
"""
printtime('Parsing GenomeQAML outputs', self.start)
# A dictionary to store the parsed excel file in a more readable format
nesteddictionary = dict()
# Use pandas to read in the CSV file, and convert the pandas data frame to a dictionary (.to_dict())
dictionary = pandas.read_csv(self.qaml_report).to_dict()
# Iterate through the dictionary - each header from the CSV file
for header in dictionary:
# Sample is the primary key, and value is the value of the cell for that primary key + header combination
for sample, value in dictionary[header].items():
# Update the dictionary with the new data
try:
nesteddictionary[sample].update({header: value})
# Create the nested dictionary if it hasn't been created yet
except KeyError:
nesteddictionary[sample] = dict()
nesteddictionary[sample].update({header: value})
# Get the results into the metadata object
for sample in self.metadata:
# Initialise the plasmid extractor genobject
setattr(sample, self.analysistype, GenObject())
# Initialise the list of all plasmids
sample[self.analysistype].prediction = str()
# Iterate through the dictionary of results
for line in nesteddictionary:
# Extract the sample name from the dictionary
name = nesteddictionary[line]['Sample']
# Ensure that the names match
if name == sample.name:
# Append the plasmid name extracted from the dictionary to the list of plasmids
sample[self.analysistype].prediction = nesteddictionary[
line]['Predicted_Class']
def mashing(self):
printtime('Performing {} analyses'.format(self.analysistype), self.starttime)
# Create the threads for the analysis
for i in range(self.cpus):
threads = Thread(target=self.mash, args=())
threads.setDaemon(True)
threads.start()
# Populate threads for each gene, genome combination
for sample in self.metadata:
sample[self.analysistype].mashresults = os.path.join(sample[self.analysistype].reportdir, '{}.tab'.format(
sample.name))
sample.commands.mash = \
'mash dist -p {} {} {} | sort -gk3 > {}'.format(self.threads,
sample[self.analysistype].refseqsketch,
sample[self.analysistype].sketchfile,
sample[self.analysistype].mashresults)
try:
self.mashqueue.put(sample)
except (KeyboardInterrupt, SystemExit):
printtime('Received keyboard interrupt, quitting threads', self.starttime)
quit()
# Join the threads
self.mashqueue.join()
self.parse()
def targets(self):
"""
Using the data from the BLAST analyses, set the targets folder, and create the 'mapping file'. This is the
genera-specific FASTA file that will be used for all the reference mapping; it replaces the 'bait file' in the
code
"""
printtime('Performing analysis with {} targets folder'.format(
self.analysistype),
self.start,
output=self.portallog)
for sample in self.runmetadata:
if sample.general.bestassemblyfile != 'NA':
sample[self.analysistype].targetpath = \
os.path.join(self.targetpath, 'genera', sample[self.analysistype].genus, '')
# There is a relatively strict databasing scheme necessary for the custom targets. Eventually,
# there will be a helper script to combine individual files into a properly formatted combined file
try:
sample[self.analysistype].mappingfile = glob(
'{}*.fa'.format(
sample[self.analysistype].targetpath))[0]
# If the fasta file is missing, raise a custom error
except IndexError as e:
# noinspection PyPropertyAccess
e.args = [
'Cannot find the combined fasta file in {}. Please note that the file must have a '
'.fasta extension'.format(
sample[self.analysistype].targetpath)
]
if os.path.isdir(sample[self.analysistype].targetpath):
raise
else:
sample.general.bestassemblyfile = 'NA'
def download_profile(self):
"""
Download the profile from the database
"""
printtime('Downloading profile', self.start)
# Set the name of the profile file
profile_file = os.path.join(self.output_path, 'profile.txt')
size = 0
# Ensure that the file exists, and that it is not too small; likely indicating a failed download
try:
stats = os.stat(profile_file)
size = stats.st_size
except FileNotFoundError:
pass
# Only download the profile if the file doesn't exist, or is likely truncated
if not os.path.isfile(profile_file) or size <= 100:
# Create a new session
session = OAuth1Session(self.consumer_key,
self.consumer_secret,
access_token=self.session_token,
access_token_secret=self.session_secret)
# The profile file is called profiles_csv on the server. Updated the URL appropriately
r = session.get(self.profile + '/1/profiles_csv')
# On a successful GET request, parse the returned data appropriately
if r.status_code == 200 or r.status_code == 201:
if re.search('json', r.headers['content-type'], flags=0):
decoded = r.json()
else:
decoded = r.text
# Write the profile file to disk
with open(profile_file, 'w') as profile:
profile.write(decoded)
def numberofsamples(self):
"""Count the number of samples is the samplesheet"""
# Initialise variables to store line data
idline = 0
linenumber = 0
# Parse the sample sheet to find the number of samples
with open(self.samplesheet, "rb") as ssheet:
# Use enumerate to iterate through the lines in the sample sheet to retrieve the line number and the data
for linenumber, entry in enumerate(ssheet):
# Once Sample_ID is encountered
if "Sample_ID" in entry:
# Set the id line as the current line number
idline = linenumber
# :samplecount is the last line number in the file minus the line number of Sample_ID
self.samplecount = linenumber - idline
printtime(
'There are {} samples in this run. '
'Running off-hours module with the following parameters:\n'
'MiSeqPath: {},\n'
'MiSeqFolder: {},\n'
'SampleSheet: {}'.format(self.samplecount, self.miseqpath,
self.miseqfolder, self.samplesheet),
self.start)
# Run the fastqmover module now that the number of sequences is known
self.fastqlinker()
def clean_sequences(self):
"""Removes reads/contigs that contain plasmids, and masks phage sequences."""
printtime('Removing plasmids and masking phages', self.start)
plasmid_db = os.path.join(self.reffilepath, 'plasmidfinder',
'plasmid_database.fa')
phage_db = os.path.join(self.reffilepath, 'prophages',
'combinedtargets.tfa')
for sample in self.runmetadata.samples:
plasmid_removal = 'bbduk.sh ref={} in={} out={} overwrite'\
.format(plasmid_db, sample.general.combined, sample.general.combined.replace('.f', '_noplasmid.f'))
subprocess.call(plasmid_removal,
shell=True,
stdout=self.devnull,
stderr=self.devnull)
phage_masking = 'bbduk.sh ref={} in={} out={} kmask=N overwrite'\
.format(phage_db, sample.general.combined.replace('.f', '_noplasmid.f'),
sample.general.combined.replace('.f', '_clean.f'))
subprocess.call(phage_masking,
shell=True,
stdout=self.devnull,
stderr=self.devnull)
os.remove(sample.general.combined)
os.rename(sample.general.combined.replace('.f', '_clean.f'),
sample.general.combined)
os.remove(sample.general.combined.replace('.f', '_noplasmid.f'))
def classifymetagenome(self):
"""Run the classify metagenome of the CLARK package on the samples"""
printtime('Classifying metagenomes', self.start)
# Define the system call
self.classifycall = 'cd {} && ./classify_metagenome.sh -O {} -R {} -n {} --light'\
.format(self.clarkpath,
self.filelist,
self.reportlist,
self.cpus)
# Variable to store classification state
classify = True
for sample in self.runmetadata.samples:
try:
# Define the name of the .csv classification file
sample.general.classification = sample.general.combined.split(
'.')[0] + '.csv'
# If the file exists, then set classify to False
if os.path.isfile(sample.general.classification):
classify = False
except KeyError:
pass
# Run the system call if the samples have not been classified
if classify:
# Run the call
subprocess.call(self.classifycall,
shell=True,
stdout=self.devnull,
stderr=self.devnull)
def normalise_reads(self):
"""
Use bbnorm from the bbmap suite of tools to perform read normalisation
"""
printtime('Normalising reads to a kmer depth of 100', self.start)
for sample in self.metadata:
# Set the name of the normalised read files
sample.general.normalisedreads = [
fastq.split('.fastq.gz')[0] + '_normalised.fastq.gz'
for fastq in sorted(sample.general.fastqfiles)
]
try:
# Run the normalisation command
out, err, cmd = bbtools.bbnorm(
forward_in=sorted(
sample.general.trimmedcorrectedfastqfiles)[0],
forward_out=sample.general.normalisedreads[0],
returncmd=True,
threads=self.cpus)
sample[self.analysistype].normalisecmd = cmd
write_to_logfile(out, err, self.logfile, sample.general.logout,
sample.general.logerr, None, None)
except CalledProcessError:
sample.general.normalisedreads = sample.general.trimmedfastqfiles
except IndexError:
sample.general.normalisedreads = list()
def getrmlsthelper(self):
"""
Makes a system call to rest_auth.py, a Python script modified from
https://github.com/kjolley/BIGSdb/tree/develop/scripts/test
And downloads the most up-to-date rMLST profile and alleles
"""
printtime('Downloading {} alleles'.format(self.analysistype), self.start)
# Extract the path of the current script from the full path + file name
homepath = os.path.split(os.path.abspath(__file__))[0]
# Set the path/name of the folder to contain the new alleles and profile
newfolder = os.path.join(self.path, self.analysistype)
# Create the path
make_path(newfolder)
# Create arguments to feed into the rest_auth_class script
args = ArgumentParser
args.secret_file = os.path.join(homepath, 'secret.txt')
args.file_path = homepath
args.output_path = newfolder
args.start = self.start
rmlst = rest_auth_class.REST(args)
# Download the profile and alleles
rmlst.main()
# Get the new alleles into a list, and create the combinedAlleles file
alleles = glob(os.path.join(newfolder, '*.tfa'))
self.combinealleles(newfolder, alleles)
def quality(self):
"""
Creates quality objects and runs quality assessments and quality processes on the
supplied sequences
"""
# Validate that the FASTQ files are in the proper format, and that there are no issues e.g. different numbers
# of forward and reverse reads, read length longer than quality score length, proper extension
self.fastq_validate()
# Run FastQC on the unprocessed fastq files
self.fastqc_raw()
# Perform quality trimming and FastQC on the trimmed files
self.quality_trim()
# Run FastQC on the trimmed files
self.fastqc_trimmed()
# Perform error correcting on the reads
self.error_correct()
# Detect contamination in the reads
self.contamination_detection()
# Run FastQC on the processed fastq files
self.fastqc_trimmedcorrected()
# Exit if only pre-processing of data is requested
metadataprinter.MetadataPrinter(self)
if self.preprocess:
printtime('Pre-processing complete', self.starttime)
quit()
def estimateabundance(self):
"""
Estimate the abundance of taxonomic groups
"""
printtime('Estimating abundance of taxonomic groups', self.start)
# Create and start threads
for i in range(self.cpus):
# Send the threads to the appropriate destination function
threads = Thread(target=self.estimate, args=())
# Set the daemon to true - something to do with thread management
threads.setDaemon(True)
# Start the threading
threads.start()
for sample in self.runmetadata.samples:
try:
if sample.general.combined != 'NA':
# Set the name of the abundance report
sample.general.abundance = sample.general.combined.split(
'.')[0] + '_abundance.csv'
# if not hasattr(sample, 'commands'):
if not sample.commands.datastore:
sample.commands = GenObject()
# Define system calls
sample.commands.target = self.targetcall
sample.commands.classify = self.classifycall
sample.commands.abundancecall = \
'cd {} && ./estimate_abundance.sh -D {} -F {} > {}'.format(self.clarkpath,
self.databasepath,
sample.general.classification,
sample.general.abundance)
self.abundancequeue.put(sample)
except KeyError:
pass
self.abundancequeue.join()
def reporter(self):
"""
Runs the necessary methods to parse raw read outputs
"""
printtime('Preparing reports', self.starttime)
# Populate self.plusdict in order to reuse parsing code from an assembly-based method
for sample in self.runmetadata.samples:
if sample.general.bestassemblyfile != 'NA':
for gene in sample[self.analysistype].allelenames:
for allele, percentidentity in sample[
self.analysistype].results.items():
if gene in allele:
# Split the allele number from the gene name using the appropriate delimiter
if '_' in allele:
splitter = '_'
elif '-' in allele:
splitter = '-'
else:
splitter = ''
# Create the plusdict dictionary as in the assembly-based (r)MLST method. Allows all the
# parsing and sequence typing code to be reused.
try:
self.plusdict[sample.name][gene][allele.split(splitter)[1]][percentidentity] \
= sample[self.analysistype].avgdepth[allele]
except IndexError:
pass
self.profiler()
self.sequencetyper()
self.mlstreporter()
def extract_rmlst_reads(self, fastq_pairs, fastq_singles):
"""
Extracts rmlst reads and puts them in a folder.
:param fastq_pairs: List of fastqpairs in nested array [[forward1, reverse1], [forward2, reverse2]]
:param fastq_singles: List of fastq singles.
:return: Zip, zilch, nada.
"""
for pair in fastq_pairs:
cmd = 'bbduk.sh ref={} in1={} in2={} outm={}' \
' outm2={}'.format(self.database, pair[0], pair[1], self.output_file + 'rmlsttmp/' + pair[0].split('/')[-1],
self.output_file + 'rmlsttmp/' + pair[1].split('/')[-1])
with open(self.output_file + 'tmp/junk.txt', 'w') as outjunk:
try: # This should give bbduk more than enough time to run, unless user's computer is super slow.
# Maybe adjust the value later.
subprocess.call(cmd, shell=True, stderr=outjunk, timeout=3600)
except subprocess.TimeoutExpired:
printtime(pair[0] + ' appears to be making BBDUK run forever. Killing...', self.start)
os.remove(self.output_file + 'rmlsttmp/' + pair[0].split('/')[-1])
os.remove(self.output_file + 'rmlsttmp/' + pair[1].split('/')[-1])
for single in fastq_singles:
cmd = 'bbduk.sh ref=database.fasta in={} outm={}' \
''.format(single, self.output_file + 'rmlsttmp/' + single.split('/')[-1])
with open(self.output_file + 'tmp/junk.txt', 'w') as outjunk:
try: # This should give bbduk more than enough time to run, unless user's computer is super slow.
# Maybe adjust the value later.
subprocess.call(cmd, shell=True, stderr=outjunk, timeout=3600)
except subprocess.TimeoutExpired:
printtime(pair[0] + ' appears to be making BBDUK run forever. Killing...', self.start)
os.remove(self.output_file + 'rmlsttmp/' + single.split('/')[-1])
def blast(self):
"""
Run BLAST analyses of the subsampled FASTQ reads against the NCBI 16S reference database
"""
printtime('BLASTing FASTA files against {} database'.format(
self.analysistype),
self.starttime,
output=self.portallog)
for _ in range(self.cpus):
threads = Thread(target=self.blastthreads, args=())
threads.setDaemon(True)
threads.start()
for sample in self.runmetadata.samples:
if sample.general.bestassemblyfile != 'NA':
# Set the name of the BLAST report
sample[self.analysistype].blastreport = os.path.join(
sample[self.analysistype].outputdir,
'{}_{}_blastresults.csv'.format(sample.name,
self.analysistype))
# Use the NCBI BLASTn command line wrapper module from BioPython to set the parameters of the search
blastn = NcbiblastnCommandline(
query=sample[self.analysistype].fasta,
db=os.path.splitext(sample[self.analysistype].baitfile)[0],
max_target_seqs=1,
num_threads=self.threads,
outfmt="'6 qseqid sseqid positive mismatch gaps "
"evalue bitscore slen length qstart qend qseq sstart send sseq'",
out=sample[self.analysistype].blastreport)
# Add a string of the command to the metadata object
sample[self.analysistype].blastcall = str(blastn)
# Add the object and the command to the BLAST queue
self.blastqueue.put((sample, blastn))
self.blastqueue.join()
def quast(self):
printtime('Performing Quast analyses', self.start)
for i in range(
len([
sample.general for sample in self.metadata
if sample.general.bestassemblyfile != 'NA'
])):
# Send the threads to the merge method. :args is empty
threads = Thread(target=self.runquast, args=())
# Set the daemon to true - something to do with thread management
threads.setDaemon(True)
# Start the threading
threads.start()
for sample in self.metadata:
if sample.general.bestassemblyfile != 'NA':
# Create the quast output directory
quastoutputdirectory = '{}/quast_results/'.format(
sample.general.outputdirectory)
make_path(quastoutputdirectory)
# Set the quast system call
quastcall = 'quast.py {} -o {}'.format(
sample.general.filteredfile, quastoutputdirectory)
# Add the command to the metadata
sample.commands.quast = quastcall
self.quastqueue.put((sample, quastoutputdirectory))
else:
sample.commands.quast = 'NA'
self.quastqueue.join()
def get_session_token(self):
"""
Use the accession token to request a new session token
"""
printtime('Getting session token', self.start)
# Rather than testing any previous session tokens to see if they are still valid, simply delete old tokens in
# preparation of the creation of new ones
try:
os.remove(os.path.join(self.file_path, 'session_token'))
except FileNotFoundError:
pass
# Create a new session
session_request = OAuth1Session(self.consumer_key,
self.consumer_secret,
access_token=self.access_token,
access_token_secret=self.access_secret)
# Set the URL appropriately
url = self.test_rest_url + '/oauth/get_session_token'
# Perform a GET request with the appropriate keys and tokens
r = session_request.get(url)
# If the status code is '200' (OK), proceed
if r.status_code == 200:
# Save the JSON-decoded token secret and token
self.session_token = r.json()['oauth_token']
self.session_secret = r.json()['oauth_token_secret']
# Write the token and secret to file
self.write_token('session_token', self.session_token, self.session_secret)
# Any other status than 200 is considered a failure
else:
print('Failed:')
print(r.json()['message'])
def error_correction(self):
"""
Use tadpole from the bbmap suite of tools to perform error correction of the reads
"""
printtime('Error correcting reads', self.start)
for sample in self.metadata:
sample.general.trimmedcorrectedfastqfiles = [
fastq.split('.fastq.gz')[0] + '_trimmed_corrected.fastq.gz'
for fastq in sorted(sample.general.fastqfiles)
]
try:
out, err, cmd = bbtools.tadpole(
forward_in=sorted(sample.general.trimmedfastqfiles)[0],
forward_out=sample.general.trimmedcorrectedfastqfiles[0],
returncmd=True,
mode='correct',
threads=self.cpus)
# Set the command in the object
sample[self.analysistype].errorcorrectcmd = cmd
write_to_logfile(out, err, self.logfile, sample.general.logout,
sample.general.logerr, None, None)
except CalledProcessError:
sample.general.trimmedcorrectedfastqfiles = sample.general.trimmedfastqfiles
except KeyError:
sample.general.trimmedcorrectedfastqfiles = list()
def profiler(self):
"""Creates a dictionary from the profile scheme(s)"""
printtime('Loading profiles', self.starttime)
from csv import DictReader
# Initialise variables
profiledata = defaultdict(make_dict)
profileset = set()
genedict = dict()
# Find all the unique profiles to use with a set
for sample in self.runmetadata.samples:
if sample.general.bestassemblyfile != 'NA':
if sample[self.analysistype].profile != 'NA':
profileset.add(sample[self.analysistype].profile)
# Extract the profiles for each set
for sequenceprofile in profileset:
# Clear the list of genes
genelist = list()
for sample in self.runmetadata.samples:
if sample.general.bestassemblyfile != 'NA':
if sequenceprofile == sample[self.analysistype].profile:
genelist = [
allele
for allele in sample[self.analysistype].alleles
]
try:
# Open the sequence profile file as a dictionary
profile = DictReader(open(sequenceprofile),
dialect='excel-tab')
# Revert to standard comma separated values
except KeyError:
# Open the sequence profile file as a dictionary
profile = DictReader(open(sequenceprofile))
# Iterate through the rows
for row in profile:
# Iterate through the genes
for gene in genelist:
# Add the sequence profile, and type, the gene name and the allele number to the dictionary
try:
profiledata[sequenceprofile][
row['ST']][gene] = row[gene]
except KeyError:
try:
profiledata[sequenceprofile][
row['rST']][gene] = row[gene]
except KeyError:
raise
# Add the gene list to a dictionary
genedict[sequenceprofile] = sorted(genelist)
# Add the profile data, and gene list to each sample
for sample in self.runmetadata.samples:
if sample.general.bestassemblyfile != 'NA':
if sequenceprofile == sample[self.analysistype].profile:
# Populate the metadata with the profile data
sample[self.analysistype].profiledata = profiledata[
sample[self.analysistype].profile]
dotter()
def create_database_folder(self, database):
"""
Create an appropriately named folder in which the database is to be stored
:param database: the name of the database folder to create
:return: the absolute path of the folder
"""
printtime('Setting up {} database'.format(database), self.start)
# Define the path to store the database files
databasepath = os.path.join(self.databasepath, database)
# Create the path as required
make_path(databasepath)
return databasepath
def settargets(self):
"""Set the targets to be used in the analyses. Involves the path of the database files, the database files to
use, and the level of classification for the analysis"""
# Define the set targets call. Include the path to the script, the database path and files, as well
# as the taxonomic rank to use
printtime('Setting up database', self.start)
self.targetcall = 'cd {} && ./set_targets.sh {} {} --{}'.format(
self.clarkpath, self.databasepath, self.database, self.rank)
#
subprocess.call(self.targetcall,
shell=True,
stdout=self.devnull,
stderr=self.devnull)
def reporter(self):
"""
Create a report of the results
"""
printtime('Writing report', self.starttime)
data = 'Strain,Profile\n'
for sample in self.runmetadata.samples:
# Only add to the string if there are results
if sample[self.analysistype].toxinprofile:
data += '{},{}\n'.format(sample.name, sample[self.analysistype].toxinprofile)
# Create the report, and write to it
with open('{}/{}.csv'.format(self.reportpath, self.analysistype), 'wb') as report:
report.write(data)
def main(self):
"""
Run the necessary methods in the correct order
"""
printtime('Starting {} analysis pipeline'.format(self.analysistype), self.starttime)
# Create the objects to be used in the analyses
objects = Objectprep(self)
objects.objectprep()
self.runmetadata = objects.samples
self.threads = int(self.cpus / len(self.runmetadata.samples)) if self.cpus / len(self.runmetadata.samples) > 1 \
else 1
# Run the genesippr analyses
self.analysistype = 'genesippr'
self.targetpath = os.path.join(self.reffilepath, self.analysistype, '')
Sippr(self, 0.90)
# Create the reports
self.reports = Reports(self)
Reports.reporter(self.reports)
# Run the 16S analyses using the filtered database
self.targetpath = self.reffilepath
# Run the 16S analyses
self.analysistype = 'sixteens_full'
SixteensFull(self, self.commit, self.starttime, self.homepath, 'sixteens_full', 0.985)
# ResFinding
Resistance(self, self.commit, self.starttime, self.homepath, 'resfinder', 0.90, False, True)
# Run the GDCS analysis
self.analysistype = 'GDCS'
self.pipeline = True
self.targetpath = os.path.join(self.targetpath, self.analysistype)
Sippr(self, 0.95)
# Create the reports
Reports.gdcsreporter(self.reports)
# Perform serotyping for samples classified as Escherichia
for sample in self.runmetadata.samples:
if sample.general.bestassemblyfile != 'NA':
sample.mash = GenObject()
try:
sample.mash.closestrefseqgenus = sample.general.closestrefseqgenus
for genus, species in self.taxonomy.items():
if genus == sample.mash.closestrefseqgenus:
sample.mash.closestrefseqspecies = species
except KeyError:
sample.mash.closestrefseqgenus = 'NA'
sample.mash.closestrefseqspecies = 'NA'
else:
sample.mash.closestrefseqgenus = 'NA'
sample.mash.closestrefseqspecies = 'NA'
SeroSippr(self, self.commit, self.starttime, self.homepath, 'serosippr', 0.95, True)
# Print the metadata
printer = MetadataPrinter(self)
printer.printmetadata()
def sistr(self):
"""Perform sistr analyses on Salmonella"""
printtime('Performing sistr analyses', self.start)
for sample in self.metadata:
# Create the analysis-type specific attribute
setattr(sample, self.analysistype, GenObject())
if sample.general.bestassemblyfile != 'NA':
try:
# Only process strains that have been determined to be Salmonella
if sample.general.referencegenus == 'Salmonella':
# Set and create the path of the directory to store the strain-specific reports
sample[self.analysistype].reportdir = os.path.join(
sample.general.outputdirectory, self.analysistype)
# Name of the .json output file
sample[self.analysistype].jsonoutput = os.path.join(
sample[self.analysistype].reportdir,
'{}.json'.format(sample.name))
# Set the sistr system call
sample.commands.sistr = \
'sistr -f json -o {} -t {} -T {} {}'\
.format(sample[self.analysistype].jsonoutput,
self.cpus,
os.path.join(sample[self.analysistype].reportdir, 'tmp'),
sample.general.bestassemblyfile)
#
sample[self.analysistype].logout = os.path.join(
sample[self.analysistype].reportdir, 'logout')
sample[self.analysistype].logerr = os.path.join(
sample[self.analysistype].reportdir, 'logerr')
# Only run the analyses if the output json file does not exist
if not os.path.isfile(
sample[self.analysistype].jsonoutput):
out, err = run_subprocess(sample.commands.sistr)
write_to_logfile(sample.commands.sistr,
sample.commands.sistr,
self.logfile,
sample.general.logout,
sample.general.logerr,
sample[self.analysistype].logout,
sample[self.analysistype].logerr)
write_to_logfile(out, err, self.logfile,
sample.general.logout,
sample.general.logerr,
sample[self.analysistype].logout,
sample[self.analysistype].logerr)
self.queue.task_done()
except (ValueError, KeyError):
pass
self.queue.join()
self.report()
def objectprep(self):
"""Create objects to store data and metadata for each sample. Also, perform necessary file manipulations"""
# Move the files to subfolders and create objects
self.runmetadata = createobject.ObjectCreation(self)
if self.runmetadata.extension == 'fastq':
# To streamline the CLARK process, decompress and combine .gz and paired end files as required
printtime(
'Decompressing and combining .fastq files for CLARK analysis',
self.start)
fileprep.Fileprep(self)
else:
printtime('Using .fasta files for CLARK analysis', self.start)
for sample in self.runmetadata.samples:
sample.general.combined = sample.general.fastqfiles[0]
def predictthreads(self):
printtime('Performing gene predictions', self.start)
# Create the threads for the analyses
for sample in self.metadata:
if sample.general.bestassemblyfile != 'NA':
threads = Thread(target=self.predict, args=())
threads.setDaemon(True)
threads.start()
for sample in self.metadata:
# Create the .prodigal attribute
sample.prodigal = GenObject()
if sample.general.bestassemblyfile != 'NA':
self.predictqueue.put(sample)
self.predictqueue.join()
def movefastq(self):
"""Find .fastq files for each sample and move them to an appropriately named folder"""
printtime('Moving FASTQ files', self.start)
# Iterate through each sample
for sample in self.metadata.runmetadata.samples:
# Retrieve the output directory
outputdir = os.path.join(self.path, sample.name)
# Find any fastq files with the sample name
fastqfiles = sorted(glob(os.path.join(self.path, '{}_*.fastq*'.format(sample.name)))) \
if sorted(glob(os.path.join(self.path, '{}_*.fastq*'.format(sample.name)))) \
else sorted(glob(os.path.join(self.path, '{}.fastq*'.format(sample.name)))) \
if sorted(glob(os.path.join(self.path, '{}.fastq*'.format(sample.name)))) \
else sorted(glob(os.path.join(self.path, '{}*.fastq*'.format(sample.name))))
# Only try and move the files if the files exist
if fastqfiles:
make_path(outputdir)
# Symlink the fastq files to the directory
try:
list(
map(
lambda x: os.symlink(
os.path.join('..', os.path.basename(x)),
os.path.join(outputdir, os.path.basename(x))),
fastqfiles))
except OSError:
pass
# Find any fastq files with the sample name
fastqfiles = [
fastq for fastq in sorted(
glob(
os.path.join(outputdir, '{}*.fastq*'.format(
sample.name)))) if 'trimmed' not in fastq
and 'normalised' not in fastq and 'corrected' not in fastq
and 'paired' not in fastq and 'unpaired' not in fastq
]
else:
if outputdir:
# Find any fastq files with the sample name
fastqfiles = [
fastq for fastq in sorted(
glob(
os.path.join(
outputdir, '{}*.fastq*'.format(
outputdir, sample.name))))
if 'trimmed' not in fastq and 'normalised' not in fastq
and 'corrected' not in fastq and 'paired' not in fastq
and 'unpaired' not in fastq
]
sample.general.fastqfiles = fastqfiles
def __init__(self, inputobject):
self.metadata = inputobject.runmetadata.samples
self.start = inputobject.starttime
self.kmers = inputobject.kmers
self.cpus = inputobject.cpus
try:
self.threads = int(self.cpus / len(
self.metadata)) if self.cpus / len(self.metadata) > 1 else 1
except TypeError:
self.threads = self.cpus
self.path = inputobject.path
self.logfile = inputobject.logfile
self.assemblequeue = Queue(maxsize=self.threads)
printtime('Assembling sequences', self.start)
self.spades()
def combinealleles(self, allelepath, alleles):
printtime('Creating combined rMLST allele file', self.start)
with open(os.path.join(allelepath, 'rMLST_combined.fasta'), 'w') as combinedfile:
# Open each allele file
for allele in sorted(alleles):
# with open(allele, 'rU') as fasta:
for record in SeqIO.parse(open(allele, "rU"), "fasta"):
# Extract the sequence record from each entry in the multifasta
# Replace and dashes in the record.id with underscores
record.id = record.id.replace('-', '_')
# Remove and dashes or 'N's from the sequence data - makeblastdb can't handle sequences
# with gaps
# noinspection PyProtectedMember
record.seq._data = record.seq._data.replace('-', '').replace('N', '')
# Clear the name and description attributes of the record
record.name = ''
record.description = ''
# Write each record to the combined file
SeqIO.write(record, combinedfile, 'fasta')
def find_loci(self):
"""
Finds the URLs for all allele files
"""
printtime('Downloading alleles', self.start)
session = OAuth1Session(self.consumer_key,
self.consumer_secret,
access_token=self.session_token,
access_token_secret=self.session_secret)
# Use the URL for all loci determined above
r = session.get(self.loci)
if r.status_code == 200 or r.status_code == 201:
if re.search('json', r.headers['content-type'], flags=0):
decoded = r.json()
else:
decoded = r.text
# Extract all the URLs in the decoded dictionary under the key 'loci'
for locus in decoded['loci']:
# Add each URL to the list
self.loci_url.append(locus)
