def __init__(self, args):
"""
Initialises the variables required for this class
:param args: list of arguments passed to the script
"""
printtime(
'Welcome to the CFIA de novo bacterial assembly pipeline {}'.
format(args.commit.decode('utf-8')), args.startingtime,
'\033[1;94m')
# Define variables from the arguments - there may be a more streamlined way to do this
self.args = args
self.path = os.path.join(args.sequencepath)
self.reffilepath = os.path.join(args.referencefilepath)
self.numreads = args.numreads
self.preprocess = args.preprocess
# Define the start time
self.starttime = args.startingtime
self.customsamplesheet = args.customsamplesheet
if self.customsamplesheet:
assert os.path.isfile(self.customsamplesheet), 'Cannot find custom sample sheet as specified {}'\
.format(self.customsamplesheet)
self.basicassembly = args.basicassembly
if not self.customsamplesheet and not os.path.isfile(
os.path.join(self.path, 'SampleSheet.csv')):
self.basicassembly = True
printtime(
'Could not find a sample sheet. Performing basic assembly (no run metadata captured)',
self.starttime)
# Use the argument for the number of threads to use, or default to the number of cpus in the system
self.cpus = args.threads if args.threads else multiprocessing.cpu_count(
) - 1
# Assertions to ensure that the provided variables are valid
make_path(self.path)
assert os.path.isdir(
self.path
), 'Supplied path location is not a valid directory {0!r:s}'.format(
self.path)
self.reportpath = os.path.join(self.path, 'reports')
assert os.path.isdir(self.reffilepath), 'Reference file path is not a valid directory {0!r:s}'\
.format(self.reffilepath)
self.commit = args.commit.decode('utf-8')
self.homepath = args.homepath
self.logfile = os.path.join(self.path, 'logfile')
self.runinfo = str()
self.pipeline = True
self.qualityobject = MetadataObject()
# Initialise the metadata object
self.runmetadata = MetadataObject()
def test_sistr(variables):
metadata = MetadataObject()
method.runmetadata.samples = list()
fasta = os.path.join(variables.sequencepath, 'NC_003198.fasta')
metadata.name = os.path.split(fasta)[1].split('.')[0]
# Initialise the general and run categories
metadata.general = GenObject()
metadata.run = GenObject()
metadata.general.fastqfiles = list()
# Set the destination folder
outputdir = os.path.join(variables.sequencepath, metadata.name)
make_path(outputdir)
# Add the output directory to the metadata
metadata.general.outputdirectory = outputdir
metadata.run.outputdirectory = outputdir
metadata.general.bestassemblyfile = True
# Initialise an attribute to store commands
metadata.commands = GenObject()
# Assume that all samples are Salmonella
metadata.general.referencegenus = 'Salmonella'
# Set the .fasta file as the best assembly
metadata.general.bestassemblyfile = fasta
method.runmetadata.samples.append(metadata)
method.sistr()
for sample in method.runmetadata.samples:
assert sample.sistr.cgmlst_genome_match == 'SAL_BA2732AA'
variable_update()
def __init__(self, inputobject):
self.starttime = inputobject.starttime
try:
self.samples = inputobject.samples
except AttributeError:
self.samples = inputobject.runmetadata.samples
try:
self.completemetadata = inputobject.completemetadata
except AttributeError:
self.completemetadata = inputobject.runmetadata.samples
self.path = inputobject.path
try:
self.analysescomplete = inputobject.analysescomplete
except AttributeError:
self.analysescomplete = True
self.reportpath = inputobject.reportpath
self.runmetadata = MetadataObject()
try:
self.runmetadata.samples = inputobject.runmetadata.samples
except AttributeError:
self.runmetadata.samples = inputobject.runmetadata
try:
self.portallog = inputobject.portallog
except AttributeError:
self.portallog = ''
def __init__(self, inputobject):
self.path = inputobject.path
self.starttime = inputobject.starttime
self.sequencepath = inputobject.sequencepath
try:
self.customsamplesheet = inputobject.customsamplesheet
self.bcltofastq = inputobject.bcltofastq
self.miseqpath = inputobject.miseqpath
self.miseqfolder = inputobject.miseqfolder
self.fastqdestination = inputobject.fastqdestination
self.forwardlength = inputobject.forwardlength
self.reverselength = inputobject.reverselength
self.numreads = 2 if self.reverselength != 0 else 1
self.customsamplesheet = inputobject.customsamplesheet
self.homepath = inputobject.homepath
self.commit = inputobject.commit
self.copy = inputobject.copy
except AttributeError:
self.bcltofastq = False
try:
self.debug = inputobject.debug
except AttributeError:
self.debug = False
try:
self.portallog = inputobject.portallog
except AttributeError:
self.portallog = ''
self.samples = MetadataObject()
self.forward = str()
self.reverse = str()
self.index = str()
self.header = dict()
self.run = dict()
def basic(self):
# Grab any .fastq files in the path
fastqfiles = glob(os.path.join(self.path, '*.fastq*'))
# Extract the base name of the globbed name + path provided
fastqnames = map(lambda x: os.path.split(x)[1], filer(fastqfiles))
# Iterate through the names of the fastq files
for fastqname in sorted(fastqnames):
# Set the name
metadata = MetadataObject()
metadata.name = fastqname
# Set the destination folder
outputdir = os.path.join(self.path, fastqname)
# Make the destination folder
make_path(outputdir)
# Get the fastq files specific to the fastqname
specificfastq = glob(
os.path.join(self.path, '{}*.fastq*'.format(fastqname)))
# Link the files to the output folder
try:
# Link the .gz files to :self.path/:filename
list(
map(
lambda x: os.symlink(
'../{}'.format(os.path.basename(x)), '{}/{}'.
format(outputdir, os.path.basename(x))),
specificfastq))
# Except os errors
except OSError as exception:
# If there is an exception other than the file exists, raise it
if exception.errno != errno.EEXIST:
raise
# Initialise the general and run categories
metadata.general = GenObject()
metadata.run = GenObject()
# Populate the .fastqfiles category of :self.metadata
metadata.general.fastqfiles = [
fastq for fastq in sorted(
glob(
os.path.join(outputdir, '{}*.fastq*'.format(
metadata.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
]
# Add the output directory to the metadata
metadata.general.outputdirectory = outputdir
metadata.general.logout = os.path.join(
self.path, metadata.name,
'{}_log_out.txt'.format(metadata.name))
metadata.general.logerr = os.path.join(
self.path, metadata.name,
'{}_log_err.txt'.format(metadata.name))
# Append the metadata to the list of samples
self.samples.append(metadata)
# Grab metadata from previous runs
previousmetadata = metadataReader.MetadataReader(self)
# Update self.samples (if required)
if previousmetadata.samples:
self.samples = previousmetadata.samples
# Run the read length method
self.readlength()
def __init__(self, args, pipelinecommit, startingtime, scriptpath):
"""
:param args: command line arguments
:param pipelinecommit: pipeline commit or version
:param startingtime: time the script was started
:param scriptpath: home path of the script
"""
# Initialise variables
self.commit = str(pipelinecommit)
self.starttime = startingtime
self.homepath = scriptpath
self.args = args
# Define variables based on supplied arguments
self.path = os.path.join(args.path, '')
assert os.path.isdir(self.path), u'Supplied path is not a valid directory {0!r:s}'.format(self.path)
self.sequencepath = os.path.join(args.sequencepath, '')
self.seqpath = self.sequencepath
self.targetpath = os.path.join(args.targetpath, '')
# ref file path is used to work with submodule code with a different naming scheme
self.reffilepath = self.targetpath
self.reportpath = os.path.join(self.path, 'reports')
make_path(self.reportpath)
assert os.path.isdir(self.targetpath), u'Target path is not a valid directory {0!r:s}' \
.format(self.targetpath)
self.bcltofastq = args.bcl2fastq
self.miseqpath = args.miseqpath
self.miseqfolder = args.miseqfolder
self.fastqdestination = args.destinationfastq
self.forwardlength = args.readlengthforward
self.reverselength = args.readlengthreverse
self.numreads = 2 if self.reverselength != 0 else 1
self.customsamplesheet = args.customsamplesheet
# Set the custom cutoff value
self.cutoff = args.customcutoffs
# Use the argument for the number of threads to use, or default to the number of cpus in the system
self.cpus = int(args.numthreads if args.numthreads else multiprocessing.cpu_count())
self.threads = int()
self.runmetadata = MetadataObject()
self.taxonomy = {'Escherichia': 'coli', 'Listeria': 'monocytogenes', 'Salmonella': 'enterica'}
self.analysistype = 'GeneSippr'
self.copy = args.copy
self.pipeline = False
self.forward = str()
self.reverse = str()
self.index = str()
self.header = dict()
self.rundata = dict()
self.completed = list()
self.incomplete = list()
self.analysescomplete = False
self.final = False
self.sum = int()
self.completemetadata = list()
self.samplesheetpath = str()
self.samples = list()
self.logfile = os.path.join(self.path, 'log')
self.reports = str()
# Run the method
self.main()
def createobject(self):
# Grab any .fastq files in the path
fastqfiles = glob(os.path.join(self.path, '*.fastq*'))
# Extract the base name of the globbed name + path provided
fastqnames = map(lambda x: os.path.split(x)[1], filer(fastqfiles))
# Iterate through the names of the fastq files
for fastqname in sorted(fastqnames):
# Set the name
metadata = MetadataObject()
metadata.name = fastqname
# Set the destination folder
outputdir = os.path.join(self.path, fastqname)
# Make the destination folder
make_path(outputdir)
# Get the fastq files specific to the fastqname
specificfastq = glob(
os.path.join(self.path, '{}*.fastq*'.format(fastqname)))
# Make relative symlinks to the files in :self.path
try:
for fastq in specificfastq:
# Get the basename of the file
fastqfile = os.path.split(fastq)[-1]
# Set the destination fastq path as the base name plus the destination folder
destinationfastq = os.path.join(outputdir, fastqfile)
# Symlink the files
os.symlink('../{}'.format(fastqfile), destinationfastq)
# Except os errors
except OSError as exception:
# If there is an exception other than the file exists, raise it
if exception.errno != errno.EEXIST:
raise
# Initialise the general and run categories
metadata.general = GenObject()
metadata.run = GenObject()
# Populate the .fastqfiles category of :self.metadata
metadata.general.fastqfiles = [
fastq for fastq in glob(
os.path.join(outputdir, '{}*.fastq*'.format(fastqname)))
if 'trimmed' not in fastq
]
# Add the output directory to the metadata
metadata.general.outputdirectory = outputdir
metadata.run.outputdirectory = outputdir
metadata.general.bestassemblyfile = True
metadata.general.trimmedcorrectedfastqfiles = metadata.general.fastqfiles
metadata.general.logout = os.path.join(
metadata.general.outputdirectory, 'logout')
metadata.general.logerr = os.path.join(
metadata.general.outputdirectory, 'logerr')
# Initialise an attribute to store commands
metadata.commands = GenObject()
# Append the metadata to the list of samples
self.samples.append(metadata)
def alleleretriever(self):
"""
Retrieve the required alleles from a file of all alleles, and create organism-specific allele files
"""
logging.info('Retrieving alleles')
# Index all the records in the allele file
logging.info('Loading rMLST records')
recorddict = SeqIO.index(self.allelefile, 'fasta')
logging.info('Creating allele output files')
# Create the organism-specific files of alleles
for organism in sorted(self.alleledict):
# Make an object to store information for each strain
metadata = MetadataObject()
metadata.organism = organism
metadata.path = self.path
metadata.outpath = os.path.join(self.path, 'outputalleles',
organism, '')
# Delete and recreate the output path - as the files are appended to each time, they will be too large if
# this script is run more than once
try:
shutil.rmtree(metadata.outpath)
except OSError:
pass
make_path(metadata.outpath)
metadata.combined = os.path.join(metadata.outpath,
'gdcs_alleles.fasta')
metadata.allelefiles = list()
with open(metadata.combined, 'w') as combined:
for gene, alleles in sorted(self.alleledict[organism].items()):
# Open the file to append
allelefiles = os.path.join(metadata.outpath,
'{}.tfa'.format(gene))
metadata.allelefiles.append(allelefiles)
with open(allelefiles, 'a') as allelefile:
# Write each allele record to the file
for allele in sorted(alleles):
# Skip adding alleles that are no longer in the database
try:
SeqIO.write(
recorddict['{}_{}'.format(gene, allele)],
allelefile, 'fasta')
SeqIO.write(
recorddict['{}_{}'.format(gene, allele)],
combined, 'fasta')
except KeyError:
pass
# Add the populated metadata to the list
self.samples.append(metadata)
def reader(self):
import os
import json
from accessoryFunctions.accessoryFunctions import GenObject, MetadataObject
for sample in self.metadata:
metadatafile = '{}{}/{}_metadata.json'.format(
self.path, sample.name, sample.name)
if os.path.isfile(metadatafile):
size = os.stat(metadatafile).st_size
if size != 0:
try:
with open(metadatafile) as metadatareport:
jsondata = json.load(metadatareport)
# Create the metadata objects
metadata = MetadataObject()
# Initialise the metadata categories as GenObjects created using the appropriate key
for attr in jsondata:
if not isinstance(jsondata[attr], dict):
setattr(metadata, attr, jsondata[attr])
else:
setattr(metadata, attr,
GenObject(jsondata[attr]))
# As files often need to be reanalysed after being moved, test to see if it possible to use the
# metadata from the previous assembly
jsonfile = '{}/{}_metadata.json'.format(
metadata.general.outputdirectory, sample.name)
try:
# Open the metadata file to write
with open(
jsonfile, 'w'
) as metadatafile: # Change from wb to w since this is text in python3
# Write the json dump of the object dump to the metadata file
json.dump(sample.dump(),
metadatafile,
sort_keys=True,
indent=4,
separators=(',', ': '))
# Set the name
metadata.name = sample.name
self.samples.append(metadata)
except IOError:
self.samples.append(sample)
except ValueError:
self.samples.append(sample)
else:
self.samples.append(sample)
def rmlst(self):
"""
Get the most up-to-date profiles and alleles from pubmlst. Note that you will need the necessary access token
and secret for this to work
"""
printtime('Downloading rMLST database', self.start)
# Set the name of the file to be used to determine if the database download and set-up was successful
completefile = os.path.join(self.databasepath, 'rMLST', 'complete')
if not os.path.isfile(completefile):
# Create an object to send to the rMLST download script
args = MetadataObject()
# Add the path and start time attributes
args.path = self.databasepath
args.start = self.start
# Run the rMLST download
get_rmlst.Get(args)
# Create and populate the complete.txt file
with open(completefile, 'w') as complete:
complete.write('\n'.join(glob(os.path.join(self.databasepath, 'rMLST', '*'))))
def __init__(self):
from argparse import ArgumentParser
from time import time
# Parser for arguments
parser = ArgumentParser(
description='Performs ePCR using a supplied primer file. The primers must be in the format: '
'<name>\t<forward primer>\t<reverse primer>\t<max size allowed between primers>\n.'
'Sequence files must be stored in <path>/sequences'
)
parser.add_argument('path',
help='Specify path in which reports are to be stored')
parser.add_argument('-s', '--sequencepath',
required=True,
help='Path to assembly files')
parser.add_argument('-f', '--primerfile',
required=True,
help='The name and path of the file containing the primers')
# Get the arguments into an object
arguments = parser.parse_args()
self.starttime = time()
# Add trailing slashes to the path variables to ensure consistent formatting (os.path.join)
self.path = os.path.join(arguments.path, '')
self.sequencepath = os.path.join(arguments.sequencepath, '')
self.primerfile = arguments.primerfile
# Initialise variables
self.runmetadata = MetadataObject()
self.reffilepath = False
self.analysistype = 'ePCR'
self.reportpath = os.path.join(self.path, 'reports')
make_path(self.reportpath)
# Initialise metadata
self.runmetadata.samples = self.setup()
self.logfile = os.path.join(self.path, 'vtyper_logfile.txt')
# Run the analyses
Vtyper(self, self.analysistype)
# Create a report
self.reporter()
# Print the metadata to file
printtime('Printing metadata to file', self.starttime)
metadataprinter.MetadataPrinter(self)
# Print a bold, green exit statement
print(u'\033[92m' + u'\033[1m' + u'\nElapsed Time: %0.2f seconds' % (time() - self.starttime) + u'\033[0m')
def __init__(self, inputobject, extension='fasta', light=False):
# Create an object to mimic the command line arguments necessary for the script
args = MetadataObject()
args.path = inputobject.path
args.sequencepath = inputobject.path
args.databasepath = os.path.join(inputobject.reffilepath, 'clark')
make_path(args.databasepath)
args.clarkpath = os.path.dirname(which('CLARK'))
args.clarkpath += '/../opt/clark/'
args.cutoff = 0.005
args.database = 'bacteria'
args.rank = 'species'
args.filter = False
args.threads = inputobject.cpus
args.runmetadata = inputobject.runmetadata
args.clean_seqs = False
args.reffilepath = inputobject.reffilepath
args.runmetadata.extension = extension
args.light = light
# Run CLARK
CLARK(args, inputobject.commit, inputobject.starttime,
inputobject.homepath)
def setup(self):
"""
Set up the metadata object to be passed to Vtyper()
"""
from glob import glob
files = sorted(glob('{}*.fasta'.format(self.sequencepath)))
samples = list()
# Create the metadata for each file
for fasta in files:
# Create a metadata object to store all metadata associated with each strain
metadata = MetadataObject()
metadata.general = GenObject()
metadata.commands = GenObject()
# Set the name
metadata.name = os.path.basename(fasta).split('.')[0]
metadata.general.bestassemblyfile = fasta
metadata.general.stx = True
metadata.general.outputdirectory = self.path
metadata.general.filenoext = fasta.split('.')[0]
metadata.general.fastqfiles = list()
samples.append(metadata)
return samples
def __init__(self, inputobject):
self.start = inputobject.starttime
self.commit = inputobject.commit
self.starttime = inputobject.starttime
self.homepath = inputobject.homepath
self.path = inputobject.path
self.cpus = inputobject.cpus
self.metadata = inputobject.runmetadata.samples
self.runmetadata = MetadataObject()
self.runmetadata.samples = list()
self.reffilepath = inputobject.reffilepath
self.reportpath = inputobject.reportpath
self.logfile = inputobject.logfile
self.analysistype = 'coregenome'
self.cutoff = 90
self.coregenomes = list()
# Fields used for custom outfmt 6 BLAST output:
self.fieldnames = ['query_id', 'subject_id', 'positives', 'mismatches', 'gaps',
'evalue', 'bit_score', 'subject_length', 'alignment_length',
'query_start', 'query_end', 'query_sequence',
'subject_start', 'subject_end', 'subject_sequence']
# Run the analyses
self.annotatedcore()
def mlst(self, genera={'Escherichia', 'Vibrio', 'Campylobacter', 'Listeria', 'Bacillus', 'Staphylococcus',
'Salmonella'}):
"""
Download the necessary up-to-date MLST profiles and alleles
"""
printtime('Downloading MLST databases', self.start)
for genus in genera:
# Create an object to pass to the get_mlst script
args = MetadataObject()
# Populate the object with the necessary attributes
args.species = genus
args.repository_url = 'http://pubmlst.org/data/dbases.xml'
args.force_scheme_name = False
args.path = os.path.join(self.databasepath, 'MLST', genus)
# Create the name of the file to be used to determine if the database download and setup was successful
completefile = os.path.join(args.path, 'complete')
# Only download the files if the download was not previously successful
if not os.path.isfile(completefile):
# Run the download
get_mlst.main(args)
# Create and populate the complete.txt file
with open(completefile, 'w') as complete:
complete.write('\n'.join(glob(os.path.join(args.path, '*'))))
def methodreporter(self):
"""
Create final reports collating results from all the individual iterations through the method pipeline
"""
# Ensure that the analyses are set to complete
self.analysescomplete = True
# Reset the report path to original value
self.reportpath = os.path.join(self.path, 'reports')
# Clear the runmetadata - it will be populated with all the metadata from completemetadata
self.runmetadata = MetadataObject()
self.runmetadata.samples = list()
# As the samples were entered into self.completemetadata depending on when they passed the quality threshold,
# this list is not ordered numerically/alphabetically like the original runmetadata. Reset the order.
for strain in self.samples:
for sample in self.completemetadata:
if sample.name == strain:
# Append the sample to the ordered list of objects
self.runmetadata.samples.append(sample)
# Create the reports
self.reporter()
self.genusspecific()
self.sixteensreporter()
self.gdcsreporter()
parser.add_argument('path', help='Specify input directory')
parser.add_argument('-s',
'--sequencepath',
required=True,
help='Path of .fastq(.gz) files to process.')
# Get the arguments into an object
arguments = parser.parse_args()
# Define the start time
arguments.starttime = time.time()
# Find the files
fastas = sorted(glob(os.path.join(arguments.sequencepath, '*.fa*')))
# Create a metadata object
arguments.runmetadata = MetadataObject()
arguments.runmetadata.samples = list()
for fasta in fastas:
metadata = MetadataObject()
metadata.name = os.path.split(fasta)[1].split('.')[0]
# Initialise the general and run categories
metadata.general = GenObject()
metadata.run = GenObject()
# Set the destination folder
outputdir = os.path.join(arguments.sequencepath, metadata.name)
make_path(outputdir)
# Add the output directory to the metadata
metadata.general.outputdirectory = outputdir
metadata.run.outputdirectory = outputdir
metadata.general.bestassemblyfile = True
# Initialise an attribute to store commands
def probefinder(self):
"""
Find the longest probe sequences
"""
logging.info('Finding and filtering probe sequences')
for sample in self.samples:
# A list to store the metadata object for each alignment
sample.gene = list()
for align in sample.alignedalleles:
# Create an object to store all the information for each alignment file
metadata = GenObject()
metadata.name = os.path.splitext(os.path.basename(align))[0]
metadata.alignmentfile = align
# Create an alignment object from the alignment file
try:
metadata.alignment = AlignIO.read(align, 'fasta')
except ValueError:
# If a ValueError: Sequences must all be the same length is raised, pad the shorter sequences
# to be the length of the longest sequence
# https://stackoverflow.com/questions/32833230/biopython-alignio-valueerror-says-strings-must-be-same-length
records = SeqIO.parse(align, 'fasta')
# Make a copy, otherwise our generator is exhausted after calculating maxlen
records = list(records)
# Calculate the length of the longest sequence
maxlen = max(len(record.seq) for record in records)
# Pad sequences so that they all have the same length
for record in records:
if len(record.seq) != maxlen:
sequence = str(record.seq).ljust(maxlen, '.')
record.seq = Seq(sequence)
assert all(len(record.seq) == maxlen for record in records)
# Write to file and do alignment
metadata.alignmentfile = '{}_padded.tfa'.format(
os.path.splitext(align)[0])
with open(metadata.alignmentfile, 'w') as padded:
SeqIO.write(records, padded, 'fasta')
# Align the padded sequences
metadata.alignment = AlignIO.read(metadata.alignmentfile,
'fasta')
metadata.summaryalign = AlignInfo.SummaryInfo(
metadata.alignment)
# The dumb consensus is a very simple consensus sequence calculated from the alignment. Default
# parameters of threshold=.7, and ambiguous='X' are used
consensus = metadata.summaryalign.dumb_consensus()
metadata.consensus = str(consensus)
# The position-specific scoring matrix (PSSM) stores the frequency of each based observed at each
# location along the entire consensus sequence
metadata.pssm = metadata.summaryalign.pos_specific_score_matrix(
consensus)
metadata.identity = list()
# Find the prevalence of each base for every location along the sequence
for line in metadata.pssm:
try:
bases = [
line['A'], line['C'], line['G'], line['T'],
line['-']
]
# Calculate the frequency of the most common base - don't count gaps
metadata.identity.append(
float('{:.2f}'.format(
max(bases[:4]) / sum(bases) * 100)))
except KeyError:
bases = [line['A'], line['C'], line['G'], line['T']]
# Calculate the frequency of the most common base - don't count gaps
metadata.identity.append(
float('{:.2f}'.format(
max(bases) / sum(bases) * 100)))
# List to store metadata objects
metadata.windows = list()
# Variable to store whether a suitable probe has been found for the current organism + gene pair.
# As the probe sizes are evaluated in descending size, as soon as a probe has been discovered, the
# search for more probes can stop, and subsequent probes will be smaller than the one(s) already found
passing = False
# Create sliding windows of size self.max - self.min from the list of identities for each column
# of the alignment
for i in reversed(range(self.min, self.max + 1)):
if not passing:
windowdata = MetadataObject()
windowdata.size = i
windowdata.max = 0
windowdata.sliding = list()
# Create a counter to store the starting location of the window in the sequence
n = 0
# Create sliding windows from the range of sizes for the list of identities
windows = self.window(metadata.identity, i)
# Go through each window from the collection of sliding windows to determine which window(s)
# has (have) the best results
for window in windows:
# Create another object to store all the data for the window
slidingdata = MetadataObject()
# Only consider the window if every position has a percent identity greater than the cutoff
if min(window) > self.cutoff:
# Populate the object with the necessary variables
slidingdata.location = '{}:{}'.format(n, n + i)
slidingdata.min = min(window)
slidingdata.mean = float('{:.2f}'.format(
numpy.mean(window)))
slidingdata.sequence = str(consensus[n:n + i])
# Create attributes for evaluating windows. A greater/less windowdata.max/windowdata.min
# means a better/less overall percent identity, respectively
windowdata.max = slidingdata.mean if slidingdata.mean >= windowdata.max \
else windowdata.max
windowdata.min = slidingdata.mean if slidingdata.mean <= windowdata.max \
else windowdata.min
# Add the object to the list of objects
windowdata.sliding.append(slidingdata)
passing = True
n += 1
# All the object to the list of objects
metadata.windows.append(windowdata)
# All the object to the list of objects
sample.gene.append(metadata)
'-a',
'--averagedepth',
default=10,
help=
'Supply an integer of the minimum mapping depth in order to return a positive result '
)
parser.add_argument(
'-C',
'--copy',
action='store_true',
help=
'Normally, the program will create symbolic links of the files into the sequence path, '
'however, the are occasions when it is necessary to copy the files instead'
)
# Get the arguments into an object
arguments = parser.parse_args()
arguments.pipeline = False
arguments.runmetadata.samples = MetadataObject()
arguments.analysistype = 'genesippr'
arguments.logfile = os.path.join(arguments.path, 'logfile')
# Define the start time
start = time.time()
# Run the script
GeneSippr(arguments, commit, start, homepath, arguments.analysistype,
arguments.cutoff, arguments.pipeline, False)
# Print a bold, green exit statement
print('\033[92m' + '\033[1m' + "\nElapsed Time: %0.2f seconds" %
(time.time() - start) + '\033[0m')
def __init__(self, args, pipelinecommit, startingtime, scriptpath):
# Initialise variables
self.commit = str(pipelinecommit)
self.start = startingtime
self.homepath = scriptpath
# Define variables based on supplied arguments
self.args = args
self.path = os.path.join(args.path, '')
assert os.path.isdir(
self.path
), u'Supplied path is not a valid directory {0!r:s}'.format(self.path)
self.sequencepath = os.path.join(args.sequencepath, '')
assert os.path.isdir(self.sequencepath), u'Supplied sequence path is not a valid directory {0!r:s}' \
.format(self.sequencepath)
self.databasepath = os.path.join(args.databasepath, '')
assert os.path.isdir(self.databasepath), u'Supplied database path is not a valid directory {0!r:s}' \
.format(self.databasepath)
# There seems to be an issue with CLARK when running with a very high number of cores. Limit self.cpus to 1
self.cpus = 1
# Set variables from the arguments
self.database = args.database
self.rank = args.rank
self.clarkpath = args.clarkpath
self.cutoff = float(args.cutoff) * 100
# Initialise variables for the analysis
self.targetcall = str()
self.classifycall = str()
self.devnull = open(os.devnull, 'wb')
self.filelist = os.path.join(self.path, 'sampleList.txt')
self.reportlist = os.path.join(self.path, 'reportList.txt')
self.abundancequeue = Queue()
self.datapath = str()
self.reportpath = os.path.join(self.path, 'reports')
self.clean_seqs = args.clean_seqs
self.light = args.light
if self.clean_seqs:
try:
self.reffilepath = args.reffilepath
except AttributeError:
self.clean_seqs = False
# If run as part of the assembly pipeline, a few modifications are necessary to ensure that the metadata objects
# and variables play nice
try:
if args.runmetadata:
self.runmetadata = args.runmetadata
self.extension = self.runmetadata.extension
# Create the name of the final report
self.report = os.path.join(
self.reportpath,
'{}'.format('abundance{}.xlsx'.format(self.extension)))
# Only re-run the CLARK analyses if the CLARK report doesn't exist. All files created by CLARK
if not os.path.isfile(self.report):
printtime(
'Performing CLARK analysis on {} files'.format(
self.extension), self.start)
if self.extension != 'fastq':
for sample in self.runmetadata.samples:
sample.general.combined = sample.general.bestassemblyfile
# Run the pipeline
self.main()
else:
# Only perform FASTQ analyses if the sample is declared to be a metagenome
metagenome = False
for sample in self.runmetadata.samples:
try:
status = sample.run.Description
except KeyError:
status = 'unknown'
if status == 'metagenome':
metagenome = True
# If any of the samples are metagenomes, run the CLARK analysis on the raw files
if metagenome:
fileprep.Fileprep(self)
# Run the pipeline
self.main()
# Clean up the files and create/delete attributes to be consistent with pipeline Metadata objects
for sample in self.runmetadata.samples:
if sample.general.bestassemblyfile != 'NA':
# Create a GenObject to store metadata when this script is run as part of the pipeline
clarkextension = 'clark{}'.format(self.extension)
setattr(sample, clarkextension, GenObject())
# Create a folder to store all the CLARK files
sample[clarkextension].outputpath = os.path.join(
sample.general.outputdirectory, 'CLARK')
make_path(sample[clarkextension].outputpath)
# Move the files to the CLARK folder
try:
move(
sample.general.abundance,
os.path.join(
sample[clarkextension].outputpath,
os.path.basename(
sample.general.abundance)))
move(
sample.general.classification,
os.path.join(
sample[clarkextension].outputpath,
os.path.basename(
sample.general.classification)))
except (KeyError, FileNotFoundError):
pass
# Set the CLARK-specific attributes
try:
sample[
clarkextension].abundance = sample.general.abundance
sample[
clarkextension].classification = sample.general.classification
sample[
clarkextension].combined = sample.general.combined
except KeyError:
pass
if self.extension == 'fastq':
# Remove the combined .fastq files
try:
if type(sample[clarkextension].combined
) is list:
os.remove(
sample[clarkextension].combined)
except (OSError, KeyError):
pass
# Remove all the attributes from .general
map(lambda x: delattr(sample.general, x),
['abundance', 'classification', 'combined'])
# Remove the text files lists of files and reports created by CLARK
try:
map(
lambda x: os.remove(os.path.join(self.path, x)
),
['reportList.txt', 'sampleList.txt'])
except OSError:
pass
else:
self.runmetadata = MetadataObject()
self.report = os.path.join(self.reportpath, 'abundance.xlsx')
# Create the objects
self.objectprep()
self.main()
except AttributeError:
self.runmetadata = MetadataObject()
self.report = os.path.join(self.reportpath, 'abundance.xlsx')
# Create the objects
self.objectprep()
self.main()
# Optionally filter the .fastq reads based on taxonomic assignment
if args.filter:
filtermetagenome.PipelineInit(self)
# Print the metadata to file
metadataprinter.MetadataPrinter(self)
def parsesamplesheet(self):
"""Parses the sample sheet (SampleSheet.csv) to determine certain values
important for the creation of the assembly report"""
# Open the sample sheet
with open(self.samplesheet, "r") as samplesheet:
# Iterate through the sample sheet
samples, prev, header = False, 0, []
for count, line in enumerate(samplesheet):
# Remove new lines, and split on commas
# line = line.decode('utf-8') # Turn from bytes to string, since python3 is finicky.
data = line.rstrip().split(",")
if any(data):
if "[Settings]" in line:
samples = False
if not line.startswith(
"[") and not samples and not data == ['']:
# Grab an data not in the [Data] Section
setattr(self.header, data[0].replace(" ", ""),
"".join(data[1:]))
elif "[Data]" in line or "[Reads]" in line:
samples = True
elif samples and "Sample_ID" in line:
header.extend([
x.replace("_", "").replace(' ', "") for x in data
])
prev = count
elif header:
# Try and replicate the Illumina rules to create file names from "Sample_Name"
samplename = samplenamer(data)
# Create an object for storing nested static variables
strainmetadata = MetadataObject()
# Set the sample name in the object
strainmetadata.name = samplename
# Add the header object to strainmetadata
# strainmetadata.__setattr__("run", GenObject(dict(self.header)))
strainmetadata.run = GenObject(
copy.copy(self.header.datastore))
# Create the run object, so it will be easier to populate the object (eg run.SampleName = ...
# instead of strainmetadata.run.SampleName = ...
run = strainmetadata.run
# Capture Sample_ID, Sample_Name, I7_Index_ID, index1, I5_Index_ID, index2, Sample_Project
for idx, item in enumerate(data):
setattr(run, header[idx],
item) if item else setattr(
run, header[idx], "NA")
# Add the sample number
run.SampleNumber = count - prev
# Create the 'General' category for strainmetadata
strainmetadata.general = GenObject({
'outputdirectory':
os.path.join(self.path, samplename),
'pipelinecommit':
self.commit
})
strainmetadata.general.logout = os.path.join(
self.path, samplename,
'{}_log_out.txt'.format(samplename))
strainmetadata.general.logerr = os.path.join(
self.path, samplename,
'{}_log_err.txt'.format(samplename))
# Add the output directory to the general category
# Append the strainmetadata object to a list
self.samples.append(strainmetadata)
elif samples:
setattr(self.header, 'forwardlength', data[0]) \
if 'forwardlength' not in self.header.datastore else \
setattr(self.header, 'reverselength', data[0])
self.totalreads += int(data[0])
self.date = self.header.Date if "Date" in self.header.datastore else self.date
for sample in self.samples:
if 'InvestigatorName' not in sample.run.datastore:
sample.run.InvestigatorName = 'NA'
def __init__(self, args, pipelinecommit, startingtime, scriptpath,
analysistype, cutoff, pipeline):
"""
:param args: command line arguments
:param pipelinecommit: pipeline commit or version
:param startingtime: time the script was started
:param scriptpath: home path of the script
:param analysistype: name of the analysis being performed - allows the program to find databases
:param cutoff: percent identity cutoff for matches
:param pipeline: boolean of whether this script needs to run as part of a particular assembly pipeline
"""
import multiprocessing
# Initialise variables
self.commit = str(pipelinecommit)
self.starttime = startingtime
self.homepath = scriptpath
# Define variables based on supplied arguments
self.path = os.path.join(args.path, '')
assert os.path.isdir(
self.path
), u'Supplied path is not a valid directory {0!r:s}'.format(self.path)
try:
self.sequencepath = os.path.join(args.sequencepath, '')
except AttributeError:
self.sequencepath = self.path
assert os.path.isdir(self.sequencepath), u'Sequence path is not a valid directory {0!r:s}' \
.format(self.sequencepath)
try:
self.targetpath = os.path.join(args.reffilepath)
except AttributeError:
self.targetpath = os.path.join(args.targetpath)
self.reportpath = os.path.join(self.path, 'reports')
assert os.path.isdir(self.targetpath), u'Target path is not a valid directory {0!r:s}' \
.format(self.targetpath)
try:
self.bcltofastq = args.bcltofastq
except AttributeError:
self.bcltofastq = False
try:
self.miseqpath = args.miseqpath
except AttributeError:
self.miseqpath = str()
try:
self.miseqfolder = args.miseqfolder
except AttributeError:
self.miseqfolder = str()
try:
self.fastqdestination = args.fastqdestination
except AttributeError:
self.fastqdestination = str()
try:
self.forwardlength = args.forwardlength
except AttributeError:
self.forwardlength = 'full'
try:
self.reverselength = args.reverselength
except AttributeError:
self.reverselength = 'full'
self.numreads = 2 if self.reverselength != 0 else 1
self.customsamplesheet = args.customsamplesheet
self.taxonomy = {
'Escherichia': 'coli',
'Listeria': 'monocytogenes',
'Salmonella': 'enterica'
}
self.logfile = args.logfile
# Set the custom cutoff value
self.cutoff = float(cutoff)
try:
self.averagedepth = int(args.averagedepth)
except AttributeError:
self.averagedepth = 10
try:
self.copy = args.copy
except AttributeError:
self.copy = False
self.pipeline = pipeline
if not self.pipeline:
self.runmetadata = MetadataObject()
# Create the objects to be used in the analyses
objects = Objectprep(self)
objects.objectprep()
self.runmetadata = objects.samples
else:
self.runmetadata = args.runmetadata
# Use the argument for the number of threads to use, or default to the number of cpus in the system
try:
self.cpus = int(args.cpus)
except AttributeError:
self.cpus = multiprocessing.cpu_count()
try:
self.threads = int(
self.cpus / len(self.runmetadata.samples)
) if self.cpus / len(self.runmetadata.samples) > 1 else 1
except TypeError:
self.threads = self.cpus
self.analysistype = analysistype
self.threads = int(self.cpus / len(self.runmetadata.samples)) if self.cpus / len(self.runmetadata.samples) > 1 \
else 1
# Run the analyses
self.runner()