class Tetranucleotide(object):
"""Calculate tetranucleotide signature of sequences."""
def __init__(self, cpus=1):
"""Initialization.
Parameters
----------
cpus : int
Number of cpus to use.
"""
self.logger = logging.getLogger()
self.cpus = cpus
self.signatures = GenomicSignature(4)
def canonical_order(self):
"""Canonical order of tetranucleotides."""
return self.signatures.canonical_order()
def _producer(self, seq_info):
"""Calculate tetranucleotide signature of a sequence.
Parameters
----------
seq_id : str
Unique id of sequence.
seq : str
Sequence in nuceltoide space.
Returns
-------
str
Unique id of sequence.
list
Count of each kmer in the canonical order.
"""
seq_id, seq = seq_info
sig = self.signatures.seq_signature(seq)
total_kmers = sum(sig)
for i in xrange(0, len(sig)):
sig[i] = float(sig[i]) / total_kmers
return (seq_id, sig)
def _consumer(self, produced_data, consumer_data):
"""Consume results from producer processes.
Parameters
----------
produced_data : list -> kmers in the canonical order
Tetranucleotide signature in canconical order.
consumer_data : d[seq_id] -> tetranucleotide signature
Set of kmers observed across all genomes (kmer_set),
along with the kmer usage of each genome (genome_kmer_usage).
Returns
-------
consumer_data: dict
The consumer data structure or None must be returned
"""
if consumer_data == None:
consumer_data = {}
seq_id, sig = produced_data
consumer_data[seq_id] = sig
return consumer_data
def _progress(self, processed_items, total_items):
"""Report progress of consumer processes.
Parameters
----------
processed_items : int
Number of sequences processed.
total_items : int
Total number of sequences to process.
Returns
-------
str
String indicating progress of data processing.
"""
return ' Finished processing %d of %d (%.2f%%) sequences.' % (processed_items, total_items, float(processed_items) * 100 / total_items)
def run(self, seq_file):
"""Calculate tetranucleotide signatures of sequences.
Parameters
----------
seq_file : str
Name of fasta/q file to read.
Returns
-------
dict : d[seq_id] -> tetranucleotide signature in canonical order
Count of each kmer.
"""
self.logger.info(' Calculating tetranucleotide signature for each sequence:')
parallel = Parallel(self.cpus)
seq_signatures = parallel.run_seqs_file(self._producer, self._consumer, seq_file, self._progress)
return seq_signatures
def read(self, signature_file):
"""Read tetranucleotide signatures.
Parameters
----------
signature_file : str
Name of file to read.
Returns
-------
dict : d[seq_id] -> tetranucleotide signature in canonical order
Count of each kmer.
"""
try:
sig = {}
with open(signature_file) as f:
header = f.readline().split('\t')
kmer_order = [x.strip().upper() for x in header[1:]]
if len(kmer_order) != len(self.canonical_order()):
raise ParsingError("[Error] Tetranucleotide file must contain exactly %d tetranucleotide columns." % len(self.canonical_order()))
canonical_order_index = np.argsort(kmer_order)
canonical_order = [kmer_order[i] for i in canonical_order_index]
if canonical_order != self.canonical_order():
raise ParsingError("[Error] Failed to process tetranucleotide signature file: " + signature_file)
for line in f:
line_split = line.split('\t')
sig[line_split[0]] = [float(line_split[i + 1]) for i in canonical_order_index]
return sig
except IOError:
print '[Error] Failed to open signature file: %s' % signature_file
sys.exit()
except ParsingError:
sys.exit()
def write(self, signatures, output_file):
"""Write tetranucleotide signatures.
Parameters
----------
signature_file : d[seq_id] -> tetranucleotide signature in canonical order
Count of each kmer.
output_file : str
Name of output file.
"""
#singlegenome.write_links(output_file)
fout = open(output_file, 'w')
fout.write('Scaffold id')
for kmer in self.canonical_order():
fout.write('\t' + kmer)
fout.write('\n')
for seq_id, tetra_signature in signatures.iteritems():
fout.write(seq_id + '\t')
fout.write('\t'.join(map(str, tetra_signature)))
fout.write('\n')
fout.close()
class Tetranucleotide(object):
"""Calculate tetranucleotide signature of sequences."""
def __init__(self, cpus=1):
"""Initialization.
Parameters
----------
cpus : int
Number of cpus to use.
"""
self.logger = logging.getLogger('timestamp')
self.cpus = cpus
self.signatures = GenomicSignature(4)
def canonical_order(self):
"""Canonical order of tetranucleotides."""
return self.signatures.canonical_order()
def _producer(self, seq_info):
"""Calculate tetranucleotide signature of a sequence.
Parameters
----------
seq_id : str
Unique id of sequence.
seq : str
Sequence in nuceltoide space.
Returns
-------
str
Unique id of sequence.
list
Count of each kmer in the canonical order.
"""
seq_id, seq = seq_info
sig = self.signatures.seq_signature(seq)
total_kmers = sum(sig)
for i in xrange(0, len(sig)):
sig[i] = float(sig[i]) / total_kmers
return (seq_id, sig)
def _consumer(self, produced_data, consumer_data):
"""Consume results from producer processes.
Parameters
----------
produced_data : list -> kmers in the canonical order
Tetranucleotide signature in canconical order.
consumer_data : d[seq_id] -> tetranucleotide signature
Set of kmers observed across all genomes (kmer_set),
along with the kmer usage of each genome (genome_kmer_usage).
Returns
-------
consumer_data: dict
The consumer data structure or None must be returned
"""
if consumer_data == None:
consumer_data = {}
seq_id, sig = produced_data
consumer_data[seq_id] = sig
return consumer_data
def _progress(self, processed_items, total_items):
"""Report progress of consumer processes.
Parameters
----------
processed_items : int
Number of sequences processed.
total_items : int
Total number of sequences to process.
Returns
-------
str
String indicating progress of data processing.
"""
if self.logger.is_silent:
return None
else:
return ' Finished processing %d of %d (%.2f%%) sequences.' % (
processed_items, total_items,
float(processed_items) * 100 / total_items)
def run(self, seq_file):
"""Calculate tetranucleotide signatures of sequences.
Parameters
----------
seq_file : str
Name of fasta/q file to read.
Returns
-------
dict : d[seq_id] -> tetranucleotide signature in canonical order
Count of each kmer.
"""
self.logger.info(
'Calculating tetranucleotide signature for each sequence:')
parallel = Parallel(self.cpus)
seq_signatures = parallel.run_seqs_file(self._producer, self._consumer,
seq_file, self._progress)
return seq_signatures
def read(self, signature_file):
"""Read tetranucleotide signatures.
Parameters
----------
signature_file : str
Name of file to read.
Returns
-------
dict : d[seq_id] -> tetranucleotide signature in canonical order
Count of each kmer.
"""
try:
sig = {}
with open(signature_file) as f:
header = f.readline().split('\t')
kmer_order = [x.strip().upper() for x in header[1:]]
if len(kmer_order) != len(self.canonical_order()):
raise ParsingError(
"[Error] Tetranucleotide file must contain exactly %d tetranucleotide columns."
% len(self.canonical_order()))
canonical_order_index = np.argsort(kmer_order)
canonical_order = [
kmer_order[i] for i in canonical_order_index
]
if canonical_order != self.canonical_order():
raise ParsingError(
"[Error] Failed to process tetranucleotide signature file: "
+ signature_file)
for line in f:
line_split = line.split('\t')
sig[line_split[0]] = [
float(line_split[i + 1]) for i in canonical_order_index
]
return sig
except IOError:
print '[Error] Failed to open signature file: %s' % signature_file
sys.exit()
except ParsingError:
sys.exit()
def write(self, signatures, output_file):
"""Write tetranucleotide signatures.
Parameters
----------
signature_file : d[seq_id] -> tetranucleotide signature in canonical order
Count of each kmer.
output_file : str
Name of output file.
"""
fout = open(output_file, 'w')
fout.write('Scaffold id')
for kmer in self.canonical_order():
fout.write('\t' + kmer)
fout.write('\n')
for seq_id, tetra_signature in signatures.iteritems():
fout.write(seq_id + '\t')
fout.write('\t'.join(map(str, tetra_signature)))
fout.write('\n')
fout.close()
class KmerUsage(object):
"""Calculate kmer usage over a set of genomes.
The implementation for calculating genomic signatures
is not optimized for speed. As such, this class is
useful for k <= 8.
"""
def __init__(self, k, cpus=1):
"""Initialization.
Parameters
----------
cpus : int
Number of cpus to use.
"""
self.logger = logging.getLogger('timestamp')
self.k = k
self.cpus = cpus
self.logger.info('Calculating unique kmers of size k = %d.' % self.k)
self.signatures = GenomicSignature(self.k)
def _producer(self, genome_file):
"""Calculates kmer usage of a genome.
Parameters
----------
genome_file : str
Fasta file containing genomic sequences.
Returns
-------
str
Unique identifier of genome.
dict : d[kmer] -> count
Occurrence of each kmer.
"""
genome_id = ntpath.basename(genome_file)
genome_id = os.path.splitext(genome_id)[0]
seqs = seq_io.read_fasta(genome_file)
kmer_usage = self.signatures.counts(seqs)
return (genome_id, kmer_usage)
def _consumer(self, produced_data, consumer_data):
"""Consume results from producer processes.
Parameters
----------
produced_data : list -> [genome_id, kmer_usage]
Unique id of a genome followed by a dictionary
indicating its kmer usage.
Returns
-------
consumer_data
dictionary indicating the frequency of kmers in each genome
"""
if consumer_data == None:
consumer_data = defaultdict(dict)
genome_id, kmer_usage = produced_data
for idx, kmer in enumerate(self.signatures.canonical_order()):
consumer_data[genome_id][kmer] = kmer_usage[idx]
return consumer_data
def _progress(self, processed_items, total_items):
"""Report progress of consumer processes.
Parameters
----------
processed_items : int
Number of genomes processed.
total_items : int
Total number of genomes to process.
Returns
-------
str
String indicating progress of data processing.
"""
return ' Finished processing %d of %d (%.2f%%) genomes.' % (processed_items, total_items, float(processed_items) * 100 / total_items)
def run(self, genome_files):
"""Calculate kmer usage over a set of genomes.
Parameters
----------
genome_files : list
Fasta files containing genomic sequences in nucleotide space.
Returns
-------
dict of dict : d[genome_id][kmer] -> count
Kmer usage of each genome.
set
Set with all identified kmers.
"""
self.logger.info('Calculating kmer usage for each genome.')
progress_func = self._progress
if self.logger.is_silent:
progress_func = None
parallel = Parallel(self.cpus)
kmer_counts = parallel.run(self._producer, self._consumer, genome_files, progress_func)
return kmer_counts, self.signatures.canonical_order()
