def start(self):
status_message('Multiple alignment running','please wait')
args = self.args
executor = concurrent.futures.ProcessPoolExecutor(args['n'])
try:
for i in range(self.num_runs):
if self.debug >= 1:
print("DomainExtractionDriver: Run "+str(i+1)+" of "+str(self.num_runs))
targets_sub = generate_sequence_set(self.targets,args['subsample'],args['random_subset'],args['random_order'],args['subsample_start'])
f = executor.submit(_extract_domains, targets_sub, is_baseline=False,
input_state=self.input_state, threshold=args['thresh'],
thresh_type=args['type'], max_domain_size=args['win'], min_domain_size=args['minwin'])
f.add_done_callback(self._callback)
disallowed = []
if args['disjoint_subset']:
disallowed = targets_sub
baselines_sub = generate_sequence_set(self.baselines,args['subsample'],args['random_subset'],args['random_order'],args['subsample_start'],disallowed=disallowed)
f = executor.submit(_extract_domains, baselines_sub, is_baseline=True,
input_state=self.input_state, threshold=args['thresh'],
thresh_type=args['type'], max_domain_size=args['win'], min_domain_size=args['minwin'])
f.add_done_callback(self._callback)
executor.shutdown()
#self.close_output_buffers()
status_message('Analysis complete', 'OK')
except KeyboardInterrupt:
executor.shutdown()
def generate_consensus_newick(msa, consensus, filename):
newick_string = ""
cons_str = consensus.consensus
conservation, variability = calculate_conservation(msa, consensus)
a_stack = [(1, 0)]
print(cons_str)
for position in range(len(cons_str)):
# print(str(position)+' '+cons_str[position])
# print(str(a_stack))
if cons_str[position] == "A":
a_stack.append((0, 0))
node_string = ")"
elif cons_str[position] == "C":
node_string = ",:1)"
a_val = a_stack.pop()
a_stack.append((a_val[0], a_val[1] + 1))
elif cons_str[position] == "T":
node_string = "(:1,:1)"
not_done = 1
while not_done and len(a_stack) > 0:
a_val = a_stack.pop()
if a_val[0] == 0:
node_string = "," + a_val[1] * "(" + node_string
a_stack.append((1, 1))
not_done = 0
else:
node_string = a_val[1] * "(" + node_string
newick_string = (
node_string
+ cons_str[position]
+ "-"
+ str(round(conservation[position], 3))
+ ":"
+ str(round(variability[position], 3))
+ newick_string
)
newick_string += ";"
status_message("Generating newick string", "OK")
handle = open(args["newick"], "w")
handle.write(newick_string)
handle.close()
return newick_string
def extract_and_run_stats(args):
msa = XMLBuildReader(args['build']).parse()
consensus = msa.consensuses[0]
ungapped = consensus.ungapped_consensus
alignments = msa.alignments
contributions = []
for alignment in alignments:
contributions.append(1/len(alignment.replace('-','')))
current_var_sum = 0
variability_sums = []
conservation = []
# Go through each position in the alignment (based on the composite or gapped consensus)
for position in range(len(msa.composite)):
# If the current position is a gap, add contributions to the current variability sum
if consensus.seq[position] == '-':
# Go through each alignment to check whether it has a character at the current position
for alignment_num in range(len(alignments)):
# If it does have a character (not a gap '-'), add its variability contribution
if not alignments[alignment_num][position] == '-':
current_var_sum += contributions[alignment_num]
# The current position is a consensus character, so store the variability sum preceeding it
else:
# if the position within the ungapped consensus is needed, just use the current length of variability_sums
variability_sums.append(current_var_sum)
current_var_sum = 0
# determine the current position's conservation level
conservation_sum = 0
for alignment_num in range(len(alignments)):
# If it does have a character (not a gap '-'), add its conservation contribution
if not alignments[alignment_num][position] == '-':
conservation_sum += 1
conservation.append(conservation_sum/len(alignments))
if 'newick' in args.keys():
status_message('Generating newick string','OK')
newick_string = generate_consensus_newick(consensus,conservation,variability_sums)
handle = open(args['newick'],'w')
handle.write(newick_string)
handle.close()
msa.add_consensus(threshold, msa.build_consensus(float(threshold)))
print("Consensus at threshold " + threshold + ": " + msa.get_consensus(threshold).consensus)
if args["newick"]:
if args["threshold"]:
generate_consensus_newick(msa, msa.get_consensus(float(args["threshold"])), args["newick"])
else:
generate_consensus_newick(msa, list(msa.consensuses.values())[0], args["newick"])
if args["scores"]:
generate_score_and_conserved_chars_file(msa, args["scores"])
if args["k"]:
threshold, k = msa.find_conservation_boundary()
print(
"At threshold %.2f, average conservation and proportion of conserved characters are both %.2f%%"
% (threshold, k * 100)
)
if args["newbuild"]:
# consensus_object = msa.build_consensus(args['threshold'],args['type'])
# Write MSA and consensus to file
consensus_fact = ConsensusFilterFactory(msa, msa.get_consensus(threshold))
consensus_fact.write(fname=args["newbuild"])
status_message("Consensus statistics computation complete ", "OK")
except (IOError, KeyboardInterrupt, IndexError) as e:
print(str(e))
elif cons_str[position] == 'T':
node_string = '(:1,:1)'
not_done = 1
while not_done and len(a_stack) > 0:
a_val = a_stack.pop()
if a_val[0] == 0:
node_string = ','+a_val[1]*'('+node_string
a_stack.append((1,1))
not_done = 0
else:
node_string = a_val[1]*'('+node_string
newick_string = node_string + cons_str[position] + '-' + str(round(conservation[position],3)) + ':' + str(round(variability_sums[position],3)) + newick_string
newick_string += ';'
return newick_string
if __name__ == '__main__':
try:
args = ConsensusStatsCommandParser().parse_args()
# ConsensusStatsArgumentValidator(args) # test all arguments are correct
print('capellini - v.' + str(version) + '\n=============')
extract_and_run_stats(args)
status_message('Consensus statistics computation complete ', 'OK')
except (IOError, KeyboardInterrupt, IndexError) as e:
print(str(e))
if args['mode'] == 'single':
print('penne - v.' + str(version) + '\n=============')
cons_query = XMLBuildReader(args['query']).parse().consensuses[0]
cons_baseline = XMLBuildReader(args['baseline']).parse().consensuses[0]
# next, yield domains for both query and baseline datasets.
dsb_query = DomainSetBuilder(cons_query, args['win'], args['max_g'],
args['strip'], is_enum=args['enumerate'])
dsb_baseline = DomainSetBuilder(cons_baseline, args['win'], args['max_g'],
args['strip'], is_enum=args['enumerate'])
# dsb_baseline = DomainSetBuilder(win=args['win'], max_gap=args['max_g'],
# is_enum=args['enumerate'], consensus=cons_baseline,
# is_strip=args['strip'])
domains_query = dsb_query.build() # build abundance counts
domains_baseline = dsb_baseline.build()
status_message('Identifying domains', 'OK')
db = DomainAbundanceBuilder(query=domains_query, baseline=domains_baseline)
domains = db.build() # build contingency matrices
dpp = DomainPrettyPrinter(domains = domains, pval = args['p'],
out=args['o'])
dpp.display() # pretty-print domains
status_message('Domain over-representation computation complete ', 'OK')
else:
args.update({'f':args['query'],'f2':args['baseline'],'a':None})
input_state = InputWrapperState(args)
#input_state.assign_matrix() # parse in-built or custom matrix
targets = input_state.parse_fasta(input_state.fname)
baselines = input_state.parse_fasta(input_state.fname2)
if not args['overlap']:
target_names = list([target.name for target in targets])
baselines = list([baseline for baseline in baselines if baseline.name not in target_names])
cons_query = XMLBuildReader(args["query"]).parse().consensuses[0]
cons_baseline = XMLBuildReader(args["baseline"]).parse().consensuses[0]
# next, yield domains for both query and baseline datasets.
dsb_query = DomainSetBuilder(
cons_query, args["win"], args["max_g"], args["strip"], is_enum=args["enumerate"]
)
dsb_baseline = DomainSetBuilder(
cons_baseline, args["win"], args["max_g"], args["strip"], is_enum=args["enumerate"]
)
# dsb_baseline = DomainSetBuilder(win=args['win'], max_gap=args['max_g'],
# is_enum=args['enumerate'], consensus=cons_baseline,
# is_strip=args['strip'])
domains_query = dsb_query.build() # build abundance counts
domains_baseline = dsb_baseline.build()
status_message("Identifying domains", "OK")
db = DomainAbundanceBuilder(query=domains_query, baseline=domains_baseline)
domains = db.build() # build contingency matrices
dpp = DomainPrettyPrinter(domains=domains, pval=args["p"], out=args["o"])
dpp.display() # pretty-print domains
status_message("Domain over-representation computation complete ", "OK")
else:
args.update({"f": args["query"], "f2": args["baseline"], "a": None})
input_state = InputWrapperState(args)
# input_state.assign_matrix() # parse in-built or custom matrix
targets = input_state.parse_fasta(input_state.fname)
baselines = input_state.parse_fasta(input_state.fname2)
if not args["overlap"]:
target_names = list([target.name for target in targets])
baselines = list([baseline for baseline in baselines if baseline.name not in target_names])
