def generate_backbone(self):
_LOG.info("Reading input sequences: %s" %(self.options.sequence_file))
sequences = MutableAlignment()
sequences.read_file_object(self.options.sequence_file)
if (options().backbone_size is None):
options().backbone_size = min(100,int(.20*sequences.get_num_taxa()))
_LOG.info("Backbone size set to: %d" %(options().backbone_size))
backbone_sequences = sequences.get_hard_sub_alignment(random.sample(sequences.keys(), options().backbone_size))
[sequences.pop(i) for i in backbone_sequences.keys()]
_LOG.info("Writing query and backbone set. ")
query = get_temp_file("query", "backbone", ".fas")
backbone = get_temp_file("backbone", "backbone", ".fas")
_write_fasta(sequences, query)
_write_fasta(backbone_sequences, backbone)
_LOG.info("Generating sate backbone alignment and tree. ")
satealignJob = SateAlignJob()
moleculeType = options().molecule
if (options().molecule == 'amino'):
moleculeType = 'protein'
satealignJob.setup(backbone,options().backbone_size,self.options.outdir,moleculeType,options().cpu)
satealignJob.run()
satealignJob.read_results()
options().placement_size = self.options.backbone_size
options().alignment_file = open(self.options.outdir + "/sate.fasta")
options().tree_file = open(self.options.outdir + "/sate.fasttree")
_LOG.info("Backbone alignment written to %s.\nBackbone tree written to %s" % (options().alignment_file, options().tree_file))
options().fragment_file = query
def hmmer_to_markers(input, temp_dir):
global refpkg
fragments = MutableAlignment()
fragments.read_filepath(input)
reverse = dict([(name + '_rev', reverse_sequence(seq))
for (name, seq) in fragments.items()])
all_frags = MutableAlignment()
all_frags.set_alignment(fragments)
all_frags.set_alignment(reverse)
frag_file = temp_dir + "/frags.fas"
_write_fasta(all_frags, frag_file)
# Now bin the fragments
frag_scores = dict([(name, [-10000, 'NA', 'NA'])
for name in fragments.keys()])
for gene in refpkg["genes"]:
# Now run HMMER search
hmmer_output = temp_dir + '/' + gene + ".out"
hmmer_search(frag_file, refpkg[gene]["hmm"], hmmer_output)
results = read_hmmsearch_results(hmmer_output)
# Now select best direction for each frag
for name, value in results.items():
bitscore = value[1]
direction = 'forward'
true_name = name
if (name.find('_rev') != -1):
true_name = true_name.replace('_rev', '')
direction = 'reverse'
if frag_scores[true_name][0] < bitscore:
frag_scores[true_name] = [bitscore, gene, direction]
# Now bin the fragments
genes = dict([])
for name, val in frag_scores.items():
if (val[1] not in genes):
genes[val[1]] = {}
if (val[2] == 'forward'):
genes[val[1]][name] = fragments[name]
else:
genes[val[1]][name] = reverse_sequence(fragments[name])
genes.pop("NA", None)
for gene, seq in genes.items():
gene_file = temp_dir + '/' + gene + ".frags.fas.fixed"
_write_fasta(seq, gene_file)
binned_fragments = {}
for gene, seq in genes.items():
binned_fragments[gene] = {}
binned_fragments[gene]["file"] = temp_dir + '/' + gene \
+ ".frags.fas.fixed"
binned_fragments[gene]["nfrags"] = len(seq.keys())
return binned_fragments
def blast_to_markers(input, temp_dir):
fragments = MutableAlignment()
fragments.read_filepath(input)
if (options().gene is None):
# First blast sequences against all markers
blast_results = temp_dir + "/blast.out"
if (options().blast_file is None):
print("Blasting fragments against marker dataset\n")
blast_fragments(input, blast_results)
else:
blast_results = options().blast_file
# Next bin the blast hits to the best gene
gene_binning = bin_blast_results(blast_results)
else:
gene_binning = {options().gene: list(fragments.keys())}
# Now figure out direction of fragments
binned_fragments = dict([
(gene, dict([(seq_name, fragments[seq_name])
for seq_name in gene_binning[gene]]))
for gene in gene_binning])
print("Finding best orientation of reads\n")
align_name = 'sate'
if (options().genes == 'cogs'):
align_name = 'pasta'
for (gene, frags) in binned_fragments.items():
# Add reverse complement sequence
frags_rev = dict([(name + '_rev', reverse_sequence(seq))
for (name, seq) in frags.items()])
gene_frags = MutableAlignment()
gene_frags.set_alignment(frags)
gene_frags.set_alignment(frags_rev)
gene_file = temp_dir + "/%s.frags.fas" % gene
_write_fasta(gene_frags, gene_file)
# Now run HMMER search
hmmer_search(
gene_file,
os.path.join(
options().__getattribute__('reference').path,
'refpkg/%s.refpkg/%s.hmm' % (gene, align_name)),
temp_dir + "/%s.out" % gene)
results = read_hmmsearch_results(temp_dir + "/%s.out" % gene)
# Now select best direction for each frag
for key in frags:
forward_score = -10000
backward_score = -10000
if (key in results):
forward_score = results[key][1]
if (key+"_rev" in results):
backward_score = results[key + "_rev"][1]
if (backward_score > forward_score):
frags[key] = gene_frags[key + "_rev"]
# Now write to file
_write_fasta(frags, gene_file + ".fixed")
binned_fragments[gene] = frags
return binned_fragments
def blast_to_markers(input, temp_dir):
fragments = MutableAlignment()
fragments.read_filepath(input)
if (options().gene is None):
# First blast sequences against all markers
blast_results = temp_dir + "/blast.out"
if (options().blast_file is None):
print("Blasting fragments against marker dataset\n")
blast_fragments(input, blast_results)
else:
blast_results = options().blast_file
# Next bin the blast hits to the best gene
gene_binning = bin_blast_results(blast_results)
else:
gene_binning = {options().gene: list(fragments.keys())}
# Now figure out direction of fragments
binned_fragments = dict([(gene,
dict([(seq_name, fragments[seq_name])
for seq_name in gene_binning[gene]]))
for gene in gene_binning])
print("Finding best orientation of reads\n")
align_name = 'sate'
if (options().genes == 'cogs'):
align_name = 'pasta'
for (gene, frags) in binned_fragments.items():
# Add reverse complement sequence
frags_rev = dict([(name + '_rev', reverse_sequence(seq))
for (name, seq) in frags.items()])
gene_frags = MutableAlignment()
gene_frags.set_alignment(frags)
gene_frags.set_alignment(frags_rev)
gene_file = temp_dir + "/%s.frags.fas" % gene
_write_fasta(gene_frags, gene_file)
# Now run HMMER search
hmmer_search(
gene_file,
os.path.join(options().__getattribute__('reference').path,
'refpkg/%s.refpkg/%s.hmm' % (gene, align_name)),
temp_dir + "/%s.out" % gene)
results = read_hmmsearch_results(temp_dir + "/%s.out" % gene)
# Now select best direction for each frag
for key in frags:
forward_score = -10000
backward_score = -10000
if (key in results):
forward_score = results[key][1]
if (key + "_rev" in results):
backward_score = results[key + "_rev"][1]
if (backward_score > forward_score):
frags[key] = gene_frags[key + "_rev"]
# Now write to file
_write_fasta(frags, gene_file + ".fixed")
binned_fragments[gene] = frags
return binned_fragments
def hmmer_to_markers(input, temp_dir):
global marker_genes
fragments = MutableAlignment()
fragments.read_filepath(input)
reverse = dict([(name+'_rev', reverse_sequence(seq))
for (name, seq) in fragments.items()])
all_frags = MutableAlignment()
all_frags.set_alignment(fragments)
all_frags.set_alignment(reverse)
frag_file = temp_dir+"/frags.fas"
_write_fasta(all_frags, frag_file)
# Now bin the fragments
frag_scores = dict([(name, [-10000, 'NA', 'NA'])
for name in fragments.keys()])
gene_set = marker_genes
align_name = 'sate'
if (options().genes == 'cogs'):
gene_set = cog_genes
align_name = 'pasta'
for gene in gene_set:
# Now run HMMER search
hmmer_search(
frag_file,
os.path.join(
options().__getattribute__('reference').path,
'refpkg/%s.refpkg/%.profile' % (gene, align_name)),
temp_dir + "/%s.out" % gene)
results = read_hmmsearch_results(temp_dir + "/%s.out" % gene)
# Now select best direction for each frag
for name, value in results.items():
bitscore = value[1]
direction = 'forward'
true_name = name
if (name.find('_rev') != -1):
true_name = true_name.replace('_rev', '')
direction = 'reverse'
if frag_scores[true_name][0] < bitscore:
frag_scores[true_name] = [bitscore, gene, direction]
# Now bin the fragments
genes = dict([])
for name, val in frag_scores.items():
if (val[1] not in genes):
genes[val[1]] = {}
if (val[2] == 'forward'):
genes[val[1]][name] = fragments[name]
else:
genes[val[1]][name] = reverse_sequence(fragments[name])
genes.pop("NA", None)
for gene, seq in genes.items():
gene_file = temp_dir + "/%s.frags.fas" % gene
_write_fasta(seq, gene_file + ".fixed")
return genes
def hmmer_to_markers(input, temp_dir):
global marker_genes
fragments = MutableAlignment()
fragments.read_filepath(input)
reverse = dict([(name + '_rev', reverse_sequence(seq))
for (name, seq) in fragments.items()])
all_frags = MutableAlignment()
all_frags.set_alignment(fragments)
all_frags.set_alignment(reverse)
frag_file = temp_dir + "/frags.fas"
_write_fasta(all_frags, frag_file)
#Now bin the fragments
frag_scores = dict([(name, [-10000, 'NA', 'NA'])
for name in fragments.keys()])
gene_set = marker_genes
align_name = 'sate'
if (options().genes == 'cogs'):
gene_set = cog_genes
align_name = 'pasta'
for gene in gene_set:
#Now run HMMER search
hmmer_search(
frag_file,
os.path.join(options().__getattribute__('reference').path,
'refpkg/%s.refpkg/%.profile' % (gene, align_name)),
temp_dir + "/%s.out" % gene)
results = read_hmmsearch_results(temp_dir + "/%s.out" % gene)
#Now select best direction for each frag
for name in results.keys():
bitscore = results[name][1]
direction = 'forward'
true_name = name
if (name.find('_rev') != -1):
true_name = true_name.replace('_rev', '')
direction = 'reverse'
if frag_scores[true_name][0] < bitscore:
frag_scores[true_name] = [bitscore, gene, direction]
#Now bin the fragments
genes = dict([])
for name in frag_scores.keys():
if (frag_scores[name][1] not in genes):
genes[frag_scores[name][1]] = {}
if (frag_scores[name][2] == 'forward'):
genes[frag_scores[name][1]][name] = fragments[name]
else:
genes[frag_scores[name][1]][name] = reverse_sequence(
fragments[name])
genes.pop("NA", None)
for gene in genes.keys():
gene_file = temp_dir + "/%s.frags.fas" % gene
_write_fasta(genes[gene], gene_file + ".fixed")
return genes
def hmmer_to_markers(input, temp_dir):
global marker_genes
fragments = MutableAlignment()
fragments.read_filepath(input)
reverse = dict([(name + "_rev", reverse_sequence(seq)) for (name, seq) in fragments.items()])
all_frags = MutableAlignment()
all_frags.set_alignment(fragments)
all_frags.set_alignment(reverse)
frag_file = temp_dir + "/frags.fas"
_write_fasta(all_frags, frag_file)
# Now bin the fragments
frag_scores = dict([(name, [-10000, "NA", "NA"]) for name in fragments.keys()])
gene_set = marker_genes
align_name = "sate"
if options().genes == "cogs":
gene_set = cog_genes
align_name = "pasta"
for gene in gene_set:
# Now run HMMER search
hmmer_search(
frag_file,
os.path.join(
options().__getattribute__("reference").path, "refpkg/%s.refpkg/%.profile" % (gene, align_name)
),
temp_dir + "/%s.out" % gene,
)
results = read_hmmsearch_results(temp_dir + "/%s.out" % gene)
# Now select best direction for each frag
for name in results.keys():
bitscore = results[name][1]
direction = "forward"
true_name = name
if name.find("_rev") != -1:
true_name = true_name.replace("_rev", "")
direction = "reverse"
if frag_scores[true_name][0] < bitscore:
frag_scores[true_name] = [bitscore, gene, direction]
# Now bin the fragments
genes = dict([])
for name in frag_scores.keys():
if frag_scores[name][1] not in genes:
genes[frag_scores[name][1]] = {}
if frag_scores[name][2] == "forward":
genes[frag_scores[name][1]][name] = fragments[name]
else:
genes[frag_scores[name][1]][name] = reverse_sequence(fragments[name])
genes.pop("NA", None)
for gene in genes.keys():
gene_file = temp_dir + "/%s.frags.fas" % gene
_write_fasta(genes[gene], gene_file + ".fixed")
return genes
def generate_backbone(self):
_LOG.info("Reading input sequences: %s" %(self.options.sequence_file))
sequences = MutableAlignment()
sequences.read_file_object(self.options.sequence_file)
fragments = MutableAlignment()
if (options().median_full_length is not None):
if (options().median_full_length == -1):
seq_lengths = sorted([len(seq) for seq in sequences.values()])
lengths = len(seq_lengths)
if lengths % 2:
options().median_full_length = (seq_lengths[lengths / 2] + seq_lengths[lengths / 2 - 1]) / 2.0
else:
options().median_full_length = seq_lengths[lengths / 2]
(min_length,max_length) = (int(options().median_full_length*(1-options().backbone_threshold)),int(options().median_full_length*(1+options().backbone_threshold)))
frag_names = [name for name in sequences if len(sequences[name]) > max_length or len(sequences[name]) < min_length]
if (len(frag_names) > 0):
fragments = sequences.get_hard_sub_alignment(frag_names)
[sequences.pop(i) for i in fragments.keys()]
if (options().backbone_size is None):
options().backbone_size = min(1000,int(sequences.get_num_taxa()))
_LOG.info("Backbone size set to: %d" %(options().backbone_size))
if (options().backbone_size > len(sequences.keys())):
options().backbone_size = len(sequences.keys())
backbone_sequences = sequences.get_hard_sub_alignment(random.sample(sequences.keys(), options().backbone_size))
[sequences.pop(i) for i in backbone_sequences.keys()]
_LOG.info("Writing backbone set. ")
backbone = get_temp_file("backbone", "backbone", ".fas")
_write_fasta(backbone_sequences, backbone)
_LOG.info("Generating pasta backbone alignment and tree. ")
pastaalignJob = PastaAlignJob()
moleculeType = options().molecule
if (options().molecule == 'amino'):
moleculeType = 'protein'
pastaalignJob.setup(backbone,options().backbone_size,self.options.outdir,moleculeType,options().cpu)
pastaalignJob.run()
pastaalignJob.read_results()
options().placement_size = self.options.backbone_size
options().alignment_file = open(self.options.outdir + "/pasta.fasta")
options().tree_file = open(self.options.outdir + "/pasta.fasttree")
_LOG.info("Backbone alignment written to %s.\nBackbone tree written to %s" % (options().alignment_file, options().tree_file))
sequences.set_alignment(fragments)
if (len(sequences) == 0):
_LOG.info("No query sequences to align. Final alignment saved as %s" % self.get_output_filename("alignment.fasta"))
shutil.copyfile(self.options.outdir + "/pasta.fasta", self.get_output_filename("alignment.fasta"))
sys.exit(0)
else:
query = get_temp_file("query", "backbone", ".fas")
options().fragment_file = query
_write_fasta(sequences, query)
def generate_backbone(self):
_LOG.info("Reading input sequences: %s" % (self.options.sequence_file))
sequences = MutableAlignment()
sequences.read_file_object(self.options.sequence_file)
if (options().backbone_size is None):
options().backbone_size = min(100,
int(.20 * sequences.get_num_taxa()))
_LOG.info("Backbone size set to: %d" % (options().backbone_size))
backbone_sequences = sequences.get_hard_sub_alignment(
random.sample(sequences.keys(),
options().backbone_size))
[sequences.pop(i) for i in backbone_sequences.keys()]
_LOG.info("Writing query and backbone set. ")
query = get_temp_file("query", "backbone", ".fas")
backbone = get_temp_file("backbone", "backbone", ".fas")
_write_fasta(sequences, query)
_write_fasta(backbone_sequences, backbone)
_LOG.info("Generating sate backbone alignment and tree. ")
satealignJob = SateAlignJob()
moleculeType = options().molecule
if (options().molecule == 'amino'):
moleculeType = 'protein'
satealignJob.setup(backbone,
options().backbone_size, self.options.outdir,
moleculeType,
options().cpu)
satealignJob.run()
satealignJob.read_results()
options().placement_size = self.options.backbone_size
options().alignment_file = open(self.options.outdir + "/sate.fasta")
options().tree_file = open(self.options.outdir + "/sate.fasttree")
_LOG.info(
"Backbone alignment written to %s.\nBackbone tree written to %s" %
(options().alignment_file, options().tree_file))
options().fragment_file = query
def generate_backbone(self):
_LOG.info("Reading input sequences: %s" % (self.options.sequence_file))
sequences = MutableAlignment()
sequences.read_file_object(self.options.sequence_file)
sequences.degap()
fragments = MutableAlignment()
if (options().median_full_length is not None
or options().full_length_range is not None):
if (options().median_full_length == -1):
seq_lengths = sorted(
[len(seq) for seq in list(sequences.values())])
lengths = len(seq_lengths)
l2 = int(lengths / 2)
if lengths % 2:
options().median_full_length = (seq_lengths[l2] +
seq_lengths[l2 + 1]) / 2.0
else:
options().median_full_length = seq_lengths[l2]
if options().full_length_range is not None:
L = sorted(int(x) for x in options().full_length_range.split())
min_length = L[0]
max_length = L[1]
else:
(min_length,
max_length) = (int(options().median_full_length *
(1 - options().backbone_threshold)),
int(options().median_full_length *
(1 + options().backbone_threshold)))
_LOG.info(
"Full length sequences are set to be from %d to %d character long"
% (min_length, max_length))
frag_names = [
name for name in sequences if len(sequences[name]) > max_length
or len(sequences[name]) < min_length
]
if (len(frag_names) > 0):
_LOG.info("Detected %d fragmentary sequences" %
len(frag_names))
fragments = sequences.get_hard_sub_alignment(frag_names)
[sequences.pop(i) for i in list(fragments.keys())]
if (options().backbone_size is None):
options().backbone_size = min(1000, int(sequences.get_num_taxa()))
_LOG.info("Backbone size set to: %d" % (options().backbone_size))
if (options().backbone_size > len(list(sequences.keys()))):
options().backbone_size = len(list(sequences.keys()))
sample = sorted(
random.sample(sorted(list(sequences.keys())),
options().backbone_size))
backbone_sequences = sequences.get_hard_sub_alignment(sample)
_LOG.debug("Backbone: %s" % (sorted(list(backbone_sequences.keys()))))
[sequences.pop(i) for i in list(backbone_sequences.keys())]
_LOG.info("Writing backbone set. ")
backbone = get_temp_file("backbone", "backbone", ".fas")
_write_fasta(backbone_sequences, backbone)
_LOG.info("Generating pasta backbone alignment and tree. ")
pastaalignJob = PastaAlignJob()
moleculeType = options().molecule
if (options().molecule == 'amino'):
moleculeType = 'protein'
pastaalignJob.setup(backbone,
options().backbone_size, moleculeType,
options().cpu, **vars(options().pasta))
pastaalignJob.run()
(a_file, t_file) = pastaalignJob.read_results()
shutil.copyfile(t_file, self.get_output_filename("pasta.fasttree"))
shutil.copyfile(a_file, self.get_output_filename("pasta.fasta"))
options().placement_size = self.options.backbone_size
options().alignment_file = open(
self.get_output_filename("pasta.fasta"))
options().tree_file = open(self.get_output_filename("pasta.fasttree"))
_LOG.info(
"Backbone alignment written to %s.\nBackbone tree written to %s" %
(options().alignment_file, options().tree_file))
sequences.set_alignment(fragments)
if (len(sequences) == 0):
sequences = MutableAlignment()
sequences.read_file_object(open(self.options.alignment_file.name))
self.results = ExtendedAlignment(fragment_names=[])
self.results.set_alignment(sequences)
_LOG.info(
"No query sequences to align. Final alignment saved as %s" %
self.get_output_filename("alignment.fasta"))
self.output_results()
sys.exit(0)
else:
query = get_temp_file("query", "backbone", ".fas")
options().fragment_file = query
_write_fasta(sequences, query)
