def main(args=None):
info()
if args is None:
args = sys.argv[1:]
if "-h" in args or "--help" in args:
usage()
sys.exit(2)
if "-id" in args:
run_id = get_arg(args, "-id")
else:
run_id = str(int(time.time())) # use timestamp as unique run identifier
if "-bm" in args:
blast_mode = get_arg(args, "-bm")
else:
blast_mode = 'n' # nucleotide blast by default
if "-resume" in args:
step = int(get_arg(args, "-resume"))
resume = True
else:
step = 0
resume = False
if "-short" in args:
limit = 6
else:
limit = 100 # unnecessarily high cap
if "-ctg" in args:
ctg_subset = get_arg(args, "-ctg")
else:
ctg_subset = 'exclude'
if "-g" in args:
g_select = get_arg(args, "-g")
else:
g_select = None
start_timestamp = str(datetime.now())
# ensure existence of all directories
ensure_dir(fixed_dirs.values())
run_dirs_go = ["".join([r_root_dir, run_id, "/", rdir])
for rdir in run_dirs.values()]
ensure_dir(run_dirs_go)
# define pickle paths
pickle_root = r_root_dir+run_id+"/"+run_dirs['pickles']+run_id
ref_pickles = pickle_root+"_refs.p"
genome_pickles = pickle_root+"_genomes.p"
blast_pickles = pickle_root+"_blast.p"
match_pickles = pickle_root+"_matches.p"
# check for pickles
run_refs = []
run_gs = []
run_blast = False
run_matches = []
if resume:
# matches
if step > 4:
try: run_matches = pickle.load(open(match_pickles, 'rb'))
except IOError:
print "WARNING: Could not load matches pickle"
run_matches = []
step = 4
# blast
if step > 3:
try: run_blast = pickle.load(open(blast_pickles, 'rb'))
except IOError:
print "WARNING: Could not load blast pickle"
run_blast = False
step = 3
# genomes
if step > 2:
try: run_gs = pickle.load(open(genome_pickles, 'rb'))
except IOError:
print "WARNING: Could not load genomes pickle"
run_gs = []
step = 2
# references
if step > 1:
try: run_refs = pickle.load(open(ref_pickles, 'rb'))
except IOError:
print "WARNING: Could not load refs pickle"
run_refs = []
step = 1
else:
step = 0
## pipeline
print "starting pipeline"
print step, limit
if resume:
log_resume_run(run_id, base_root, project_id, start_timestamp, step)
else:
print "\n###", step, ". Set up logging & reporting ###\n"
log_start_run(run_id, base_root, project_id, run_dirs, start_timestamp)
save_datasumm(run_id, blast_mode, r_root_dir, run_dirs, genomes,
references, project_id, project_date, start_timestamp)
init_reports(run_id, fixed_dirs, ctg_thresholds, start_timestamp)
step +=1
while step < limit:
if step is 1:
print "\n###", step, ". Prepare references ###\n"
for ref in references:
timestamp = str(datetime.now())
ref_obj = process_ref(ref, ref_annot_flag, r_root_dir,
fixed_dirs, run_dirs, run_id,
timestamp, prot_db_name, project_id)
run_refs.append(ref_obj)
if os.path.exists(ref_pickles):
os.remove(ref_pickles)
pickle.dump(run_refs, open(ref_pickles, 'wb'))
step +=1
elif step is 2:
print "\n###", step, ". Prepare genomes ###\n"
for genome in genomes:
unpack_genomes(genome, separator, fixed_dirs, ctg_thresholds)
make_genome_DB(genome, fixed_dirs)
run_gs = add_refs_2g(genomes, references)
if os.path.exists(genome_pickles):
os.remove(genome_pickles)
pickle.dump(run_gs, open(genome_pickles, 'wb'))
step +=1
elif step is 3:
print "\n###", step, ". Blast reference segments against genomes ###\n"
for ref in run_refs:
timestamp = str(datetime.now())
run_blast = basic_batch_blast(run_gs, ref, blast_mode,
r_root_dir, run_dirs,
fixed_dirs, blast_prefs,
run_id, timestamp)
if os.path.exists(blast_pickles):
os.remove(blast_pickles)
pickle.dump(run_blast, open(blast_pickles, 'wb'))
step +=1
elif step is 4:
print "\n###", step, ". Collect Blast results ###\n"
for ref in run_refs:
timestamp = str(datetime.now())
ref_hits, ctl_scores = glompX_blast_out(run_gs, ref,
blast_mode,
r_root_dir, run_dirs,
run_id, fixed_dirs,
blast_dtypes,
references,
min_nt_match,
min_nt_score,
min_nt_idp,
min_aa_match,
min_aa_score,
min_aa_idp,
capture_span,
timestamp)
ref_matches = {'ref': ref, 'run': run_id, 'hits': ref_hits,
'ctl': ctl_scores}
run_matches.append(ref_matches)
if os.path.exists(match_pickles):
os.remove(match_pickles)
pickle.dump(run_matches, open(match_pickles, 'wb'))
step +=1
elif step is 5:
print "\n###", step, ". Make match results table & graphs ###\n"
for match_dict in run_matches:
timestamp = str(datetime.now())
matches_table(match_dict, r_root_dir, run_dirs, timestamp)
step +=1
### model evaluation and filtering goes here
elif step is 6:
print "\n###", step, ". Annotate matching contigs ###\n"
for ref in run_refs:
timestamp = str(datetime.now())
annot_genome_contigs(ref, prot_db_name, fixed_dirs, r_root_dir,
run_id, run_dirs, genomes, project_id,
timestamp, blast_prefs)
step +=1
elif step is 7:
print "\n###", step, ". Align contigs pairwise to reference ###\n"
for ref in run_refs:
timestamp = str(datetime.now())
align_ctg2ref(ref, run_id, timestamp, r_root_dir, run_dirs,
genomes, mauve_exec, max_size, chop_mode, mtype)
step +=1
elif step is 8:
print "\n###", step, ". Construct backbone-based scaffolds ###\n"
for ref in run_refs:
timestamp = str(datetime.now())
build_scaffolds(ref, r_root_dir, run_dirs, prox_D,
separator, genomes, run_id, timestamp,
mtype, ctg_subset)
step +=1
elif step is 9:
print "\n###", step, ". Align constructs pairwise to reference ###\n"
for ref in run_refs:
timestamp = str(datetime.now())
align_cstrct2ref(ref, run_id, timestamp, r_root_dir, run_dirs,
genomes, max_size, chop_mode, mtype, mauve_exec)
step +=1
elif step is 10:
print "\n###", step, ". Generate maps ###\n"
for ref in run_refs:
timestamp = str(datetime.now())
prep_maps(ref, run_id, timestamp, g_select, r_root_dir,
run_dirs, genomes, fixed_dirs, segtype, min_size,
fct_flags, fct_colors, idpt)
step +=1
elif step > 10:
break
stop_timestamp = str(datetime.now())
log_end_run(run_id, base_root, project_id, stop_timestamp)
print "\n### Nothing more to do! ###\n"
counter += 1
while True:
try:
fname = EFetcher(rec_id, data_dir)
except Exception:
print "Error retrieving record"
break
else:
if rec_id[0:2] == 'NZ': # disposition for WGS record sets
print "fetching WGS dataset",
# create a dedicated directory
seqdir = data_dir + rec_id + "/"
ensure_dir([seqdir])
# open genome record stub to get the contig count
fname = data_dir + rec_id + ".gbk"
try:
stub = load_genbank(fname)
except IOError:
print "Error loading", fname
break
base_code = stub.annotations['wgs'][0][:10] # 7 if not NZ_
ctg_num = int(stub.annotations['wgs'][-1][10:]) # 7
records = []
# fetch contig records
ensure_dir, from_dir, read_array, blast_dtypes
data_dir = "data/"+argv[1]+"/"
dir_in = data_dir+argv[2]+"/"
infile = data_dir+argv[3] # must be a fasta file with query sequences
file_ext = argv[4]
blast_mode = argv[5]
if len(argv) > 5:
blast_mode = argv[5]
else:
blast_mode = 'n' # nucleotide blast by default
blast_out = data_dir+"blast_out/"
ensure_dir([blast_out])
queries = load_multifasta(infile)
filenames = from_dir(dir_in, re.compile(r'.*\.'+file_ext))
for filename in filenames:
rec_name = filename[:filename.find("."+file_ext)]
print rec_name,
genome_path = dir_in+filename
dbfile_path = "data/blast_db/"+rec_name
while True:
if not path.exists(dbfile_path+".nhr"):
counter += 1
while True:
try:
fname = EFetcher(rec_id, data_dir)
except Exception:
print "Error retrieving record"
break
else:
if rec_id[0:2] == 'NZ': # disposition for WGS record sets
print "fetching WGS dataset",
# create a dedicated directory
seqdir = data_dir+rec_id+"/"
ensure_dir([seqdir])
# open genome record stub to get the contig count
fname = data_dir+rec_id+".gbk"
try:
stub = load_genbank(fname)
except IOError:
print "Error loading", fname
break
base_code = stub.annotations['wgs'][0][:10] # 7 if not NZ_
ctg_num = int(stub.annotations['wgs'][-1][10:]) # 7
records = []
# fetch contig records
ensure_dir, from_dir, read_array, blast_dtypes
data_dir = "data/" + argv[1] + "/"
dir_in = data_dir + argv[2] + "/"
infile = data_dir + argv[3] # must be a fasta file with query sequences
file_ext = argv[4]
blast_mode = argv[5]
if len(argv) > 5:
blast_mode = argv[5]
else:
blast_mode = 'n' # nucleotide blast by default
blast_out = data_dir + "blast_out/"
ensure_dir([blast_out])
queries = load_multifasta(infile)
filenames = from_dir(dir_in, re.compile(r'.*\.' + file_ext))
for filename in filenames:
rec_name = filename[:filename.find("." + file_ext)]
print rec_name,
genome_path = dir_in + filename
dbfile_path = "data/blast_db/" + rec_name
while True:
if not path.exists(dbfile_path + ".nhr"):
from libs.common import load_genbank, write_fasta, make_blastDB, \
local_tblastn_2file, read_array, blast_dtypes, ensure_dir
from libs.tetris import segment_finder
from Bio.SeqRecord import SeqRecord
import cPickle as pickle
from sets.NheA_ctxt_set import test as genomes
data_dir = 'data/'+argv[1]+'/'
seq_dir = data_dir+argv[2]+'/'
out_dir = data_dir+argv[3]+'/'
feat_type = argv[4]
threshold = int(argv[5])
min_com = int(argv[6]) # min number of non-core feats in common within groups
ensure_dir([out_dir])
db_file = out_dir+'ref_DB.fas'
db_path = out_dir+'refs'
core_genome_pickle = out_dir+'core_genome.pik'
cluster_set_file = out_dir+'clusters.py'
new_DB = True
init_DB = False
symbolDB = {}
vectorDB = {}
segmentDB = {}
# vectorDB is a dict that contains genome-keyed dicts,
# script to capture sequences from the results of a batch blast
from sys import argv
from Bio.SeqRecord import SeqRecord
from libs.common import read_array, blast_dtypes, load_fasta, write_fasta, ensure_dir
data_dir = "data/" + argv[1] + "/"
main_in = data_dir + argv[2] + "_results.txt"
main_out = data_dir + argv[2] + "_ctxt.fas"
ctx_dir = data_dir + "context/"
capture_span = int(argv[3])
ensure_dir([ctx_dir])
records = []
rec_array = read_array(main_in, blast_dtypes)
descript = "Context of " + argv[2] + " (" + argv[3] + " bp either side)"
for line in rec_array:
query = line[0]
subject = line[1]
print subject
rev_flag = False
if line[8] < line[9]:
q_start, q_stop = line[8] - 1, line[9]
rev_flag = False
# script to capture features based on annotation tags
import re
from sys import argv
from libs.common import load_genbank, write_fasta, ensure_dir, from_dir
data_dir = "data/" + argv[1] + "/"
dir_in = "data/" + argv[2] + "/"
feat_type = argv[3]
feat_tag = argv[4]
feat_name = argv[5]
main_out = data_dir + feat_name + "_seqs.fas"
records = []
ensure_dir([data_dir])
filenames = from_dir(dir_in, re.compile(r'.*\.gbk'))
for filename in filenames:
rec_name = filename[:filename.find(".gbk")]
print '.',
# load data
record = load_genbank(dir_in + "/" + filename)
# scan annotations
for feat in record.features:
if feat.type == feat_type:
try:
if feat_name in feat.qualifiers[feat_tag]:
import re
from os import path
from sys import argv
from libs.common import from_dir, ensure_dir, fas2gbk, gbk2fas, write_genbank, \
load_genbank, train_prodigal, run_prodigal, load_multifasta
from Bio.SeqFeature import SeqFeature, FeatureLocation
from Bio.Alphabet import generic_dna
origin_dir = "data/"+argv[1]+"/"
file_ext = argv[2]
annot_gbk_dir = origin_dir+"annot_gbk/"
annot_aa_dir = origin_dir+"annot_aa/"
trn_file = origin_dir+"prodigal.trn"
ensure_dir([annot_gbk_dir, annot_aa_dir])
filenames = from_dir(origin_dir, re.compile(r'.*\.'+file_ext+'.*'))
for filename in filenames:
rec_name = filename[:filename.find("."+file_ext)]
print rec_name, "...",
# load data
if file_ext == 'fas':
fas_file = origin_dir+"/"+filename
gbk_file = fas2gbk(fas_file)
record = load_genbank(gbk_file)
else:
gbk_file = origin_dir+"/"+filename
# script to capture features based on annotation tags
import re
from sys import argv
from libs.common import load_genbank, write_fasta, ensure_dir, from_dir
data_dir = "data/"+argv[1]+"/"
dir_in = "data/"+argv[2]+"/"
feat_type = argv[3]
feat_tag = argv[4]
feat_name = argv[5]
main_out = data_dir+feat_name+"_seqs.fas"
records = []
ensure_dir([data_dir])
filenames = from_dir(dir_in, re.compile(r'.*\.gbk'))
for filename in filenames:
rec_name = filename[:filename.find(".gbk")]
print '.',
# load data
record = load_genbank(dir_in+"/"+filename)
# scan annotations
for feat in record.features:
if feat.type == feat_type:
try:
if feat_name in feat.qualifiers[feat_tag]:
# script to capture sequences from the results of a batch blast
from sys import argv
from Bio.SeqRecord import SeqRecord
from libs.common import read_array, blast_dtypes, load_fasta, write_fasta, ensure_dir
data_dir = "data/"+argv[1]+"/"
main_in = data_dir+argv[2]+"_results.txt"
main_out = data_dir+argv[2]+"_ctxt.fas"
ctx_dir = data_dir+"context/"
capture_span = int(argv[3])
ensure_dir([ctx_dir])
records = []
rec_array = read_array(main_in, blast_dtypes)
descript = "Context of "+argv[2]+" ("+argv[3]+" bp either side)"
for line in rec_array:
query = line[0]
subject = line[1]
print subject
rev_flag = False
if line[8] < line[9]:
q_start, q_stop = line[8]-1, line[9]
rev_flag = False
if len(argv) > 1 and argv[1] == '-h':
print "Basic usage: \n", \
"$ python main_script.py [step#]\n"
exit()
if len(argv) < 2:
step = 0
else:
step = int(argv[1])
if step is 0:
### STEP 0: Ensure that all base directories exist ###
print "\n###", step, ". Setting up the work environment ###"
for dir_name in directories.keys():
ensure_dir(directories[dir_name])
step +=1
if step is 1:
### STEP 1: Trim & bin reads (based on results of FastQC) ###
print "\n###", step, ". Trim & bin, then split for batching ###"
for dataset in datasets:
dataset['trim_files'] = []
bin_counts = trim_illumina(dataset)
print bin_counts # TO LOG
dataset['mft_files'] = []
for trim_file in dataset['trim_files']:
mft_count = simple_q2a(dataset, trim_file)
#print mft_count # TO LOG
for mft_file in dataset['mft_files']:
print mft_file
from libs.common import load_genbank, write_fasta, make_blastDB, \
local_tblastn_2file, read_array, blast_dtypes, ensure_dir
from libs.tetris import segment_finder
from Bio.SeqRecord import SeqRecord
import cPickle as pickle
from sets.NheA_ctxt_set import test as genomes
data_dir = 'data/' + argv[1] + '/'
seq_dir = data_dir + argv[2] + '/'
out_dir = data_dir + argv[3] + '/'
feat_type = argv[4]
threshold = int(argv[5])
min_com = int(argv[6]) # min number of non-core feats in common within groups
ensure_dir([out_dir])
db_file = out_dir + 'ref_DB.fas'
db_path = out_dir + 'refs'
core_genome_pickle = out_dir + 'core_genome.pik'
cluster_set_file = out_dir + 'clusters.py'
new_DB = True
init_DB = False
symbolDB = {}
vectorDB = {}
segmentDB = {}
# vectorDB is a dict that contains genome-keyed dicts,
file_ext = argv[3]
if len(argv) < 5:
trim_ids = ''
else:
trim_ids = argv[4]
blast_dir = origin_dir+"blast/"
hits_dir = origin_dir+"hits/"
remote_prot_db = "nr"
annot_gbk_dir = origin_dir+"annot_gbk/"
annot_aa_dir = origin_dir+"annot_aa/"
trn_file = origin_dir+"prodigal.trn"
ensure_dir([annot_gbk_dir, annot_aa_dir, blast_dir, hits_dir])
filenames = from_dir(seq_dir, re.compile(r'.*\.'+file_ext+'.*'))
for filename in filenames:
rec_name = filename[:filename.find(trim_ids+"."+file_ext)]
print rec_name, "..."
# load data
if file_ext == 'fas':
fas_file = seq_dir+"/"+filename
gbk_file = fas2gbk(fas_file)
record = load_genbank(gbk_file)
else:
gbk_file = seq_dir+"/"+filename
## script to strip trailing tails from genome file names
import re
from sys import argv
from libs.common import from_dir, ensure_dir
from shutil import copyfile
origin_dir = "data/"+argv[1]+"/"
destin_dir = origin_dir+argv[2]+"/"
file_ext = argv[3]
tail = argv[4]
ensure_dir([destin_dir])
filenames = from_dir(origin_dir, re.compile(r'.*\.'+file_ext))
counter = 0
for filename in filenames:
# identify strain name
pattern = re.compile(r'^(.*)'+tail+'\.'+file_ext+'$')
capture = re.match(pattern, filename)
# substitute new name
if capture:
counter +=1
new_filename = capture.group(1)+".fas"
# copy file
copyfile(origin_dir+filename, destin_dir+new_filename)
print capture.group(1)
def main(args=None):
info()
if args is None:
args = sys.argv[1:]
if "-h" in args or "--help" in args:
usage()
sys.exit(2)
if "-id" in args:
run_id = get_arg(args, "-id")
else:
run_id = str(int(
time.time())) # use timestamp as unique run identifier
if "-bm" in args:
blast_mode = get_arg(args, "-bm")
else:
blast_mode = 'n' # nucleotide blast by default
if "-resume" in args:
step = int(get_arg(args, "-resume"))
resume = True
else:
step = 0
resume = False
if "-short" in args:
limit = 6
else:
limit = 100 # unnecessarily high cap
if "-filter" in args:
threshold = int(get_arg(args, "-filter"))
resume = True
step = 6
limit = 7
else:
threshold = 5 # reduce for small references
limit = 100 # unnecessarily high cap
if "-ctg" in args:
ctg_subset = get_arg(args, "-ctg")
else:
ctg_subset = 'exclude'
if "-g" in args:
g_select = get_arg(args, "-g")
else:
g_select = None
start_timestamp = str(datetime.now())
# ensure existence of all directories
ensure_dir(fixed_dirs.values())
run_dirs_go = [
"".join([r_root_dir, run_id, "/", rdir]) for rdir in run_dirs.values()
]
ensure_dir(run_dirs_go)
# define pickle paths
pickle_root = r_root_dir + run_id + "/" + run_dirs['pickles'] + run_id
ref_pickles = pickle_root + "_refs.p"
genome_pickles = pickle_root + "_genomes.p"
blast_pickles = pickle_root + "_blast.p"
match_pickles = pickle_root + "_matches.p"
norm_pickles = pickle_root + "_norm.p"
# check for pickles
run_refs = []
run_gs = []
run_blast = False
run_matches = []
run_norm_matches = {}
if resume:
# normalized matches
if step > 5:
try:
run_norm_matches = pickle.load(open(norm_pickles, 'rb'))
except IOError:
print "WARNING: Could not load norm pickle"
run_norm_matches = {}
step = 5
# matches
if step > 4:
try:
run_matches = pickle.load(open(match_pickles, 'rb'))
except IOError:
print "WARNING: Could not load matches pickle"
run_matches = []
step = 4
# blast
if step > 3:
try:
run_blast = pickle.load(open(blast_pickles, 'rb'))
except IOError:
print "WARNING: Could not load blast pickle"
run_blast = False
step = 3
# genomes
if step > 2:
try:
run_gs = pickle.load(open(genome_pickles, 'rb'))
except IOError:
print "WARNING: Could not load genomes pickle"
run_gs = []
step = 2
# references
if step > 1:
try:
run_refs = pickle.load(open(ref_pickles, 'rb'))
except IOError:
print "WARNING: Could not load refs pickle"
run_refs = []
step = 1
else:
step = 0
## pipeline
print "starting pipeline"
print step, limit
if resume:
log_resume_run(run_id, base_root, project_id, start_timestamp, step)
else:
print "\n###", step, ". Set up logging & reporting ###\n"
log_start_run(run_id, base_root, project_id, run_dirs, start_timestamp)
save_datasumm(run_id, blast_mode, r_root_dir, run_dirs, genomes,
references, project_id, project_date, start_timestamp)
init_reports(run_id, fixed_dirs, ctg_thresholds, start_timestamp)
step += 1
while step < limit:
if step is 1:
print "\n###", step, ". Prepare references ###\n"
for ref in references:
timestamp = str(datetime.now())
ref_obj = process_ref(ref, ref_annot_flag, r_root_dir,
fixed_dirs, run_dirs, run_id, timestamp,
prot_db_name, project_id)
run_refs.append(ref_obj)
if os.path.exists(ref_pickles):
os.remove(ref_pickles)
pickle.dump(run_refs, open(ref_pickles, 'wb'))
step += 1
elif step is 2:
print "\n###", step, ". Prepare genomes ###\n"
for genome in genomes:
unpack_genomes(genome, separator, fixed_dirs, ctg_thresholds)
make_genome_DB(genome, fixed_dirs)
run_gs = add_refs_2g(genomes, references)
if os.path.exists(genome_pickles):
os.remove(genome_pickles)
pickle.dump(run_gs, open(genome_pickles, 'wb'))
step += 1
elif step is 3:
print "\n###", step, ". Blast reference segments against genomes ###\n"
for ref in run_refs:
timestamp = str(datetime.now())
run_blast = basic_batch_blast(run_gs, ref, blast_mode,
r_root_dir, run_dirs, fixed_dirs,
blast_prefs, run_id, timestamp)
if os.path.exists(blast_pickles):
os.remove(blast_pickles)
pickle.dump(run_blast, open(blast_pickles, 'wb'))
step += 1
elif step is 4:
print "\n###", step, ". Collect Blast results ###\n"
for ref in run_refs:
timestamp = str(datetime.now())
ref_hits, ctl_scores = glompX_blast_out(
run_gs, ref, blast_mode, r_root_dir, run_dirs, run_id,
fixed_dirs, blast_dtypes, references, min_nt_match,
min_nt_score, min_nt_idp, min_aa_match, min_aa_score,
min_aa_idp, capture_span, timestamp)
ref_matches = {
'ref': ref,
'run': run_id,
'hits': ref_hits,
'ctl': ctl_scores
}
run_matches.append(ref_matches)
if os.path.exists(match_pickles):
os.remove(match_pickles)
pickle.dump(run_matches, open(match_pickles, 'wb'))
step += 1
elif step is 5:
print "\n###", step, ". Make match results table & graphs ###\n"
for ref_matches in run_matches:
timestamp = str(datetime.now())
ref_norm_matches = matches_table(ref_matches, r_root_dir,
run_dirs, timestamp)
run_norm_matches[ref_matches['ref'].name] = ref_norm_matches
if os.path.exists(norm_pickles):
os.remove(norm_pickles)
pickle.dump(run_norm_matches, open(norm_pickles, 'wb'))
step += 1
elif step is 6:
print "\n###", step, ". Filter matching contigs ###\n"
for ref in run_refs:
timestamp = str(datetime.now())
filter_contigs(ref, run_id, genomes,
run_norm_matches[ref.name], chop_size,
threshold, r_root_dir, run_dirs, fixed_dirs,
timestamp)
step += 1
elif step is 7:
print "\n###", step, ". Annotate matching contigs ###\n"
for ref in run_refs:
timestamp = str(datetime.now())
annot_genome_contigs(ref, prot_db_name, fixed_dirs, r_root_dir,
run_id, run_dirs, genomes, project_id,
timestamp, blast_prefs)
step += 1
elif step is 8:
print "\n###", step, ". Align contigs pairwise to reference ###\n"
for ref in run_refs:
timestamp = str(datetime.now())
align_ctg2ref(ref, run_id, timestamp, r_root_dir, run_dirs,
genomes, mauve_exec, max_size, chop_mode, mtype)
step += 1
elif step is 9:
print "\n###", step, ". Construct backbone-based scaffolds ###\n"
for ref in run_refs:
timestamp = str(datetime.now())
build_scaffolds(ref, r_root_dir, run_dirs, prox_D, separator,
genomes, run_id, timestamp, mtype, ctg_subset)
step += 1
elif step is 10:
print "\n###", step, ". Align constructs pairwise to reference ###\n"
for ref in run_refs:
timestamp = str(datetime.now())
align_cstrct2ref(ref, run_id, timestamp, r_root_dir, run_dirs,
genomes, max_size, chop_mode, mtype,
mauve_exec)
step += 1
elif step is 11:
print "\n###", step, ". Generate maps ###\n"
for ref in run_refs:
timestamp = str(datetime.now())
prep_maps(ref, run_id, timestamp, g_select, r_root_dir,
run_dirs, genomes, fixed_dirs, segtype, min_size,
fct_flags, fct_colors, idpt)
step += 1
elif step > 12:
break
stop_timestamp = str(datetime.now())
log_end_run(run_id, base_root, project_id, stop_timestamp)
print "\n### Nothing more to do! ###\n"
## script to strip trailing tails from genome file names
import re
from sys import argv
from libs.common import from_dir, ensure_dir
from shutil import copyfile
origin_dir = "data/" + argv[1] + "/"
destin_dir = origin_dir + argv[2] + "/"
file_ext = argv[3]
tail = argv[4]
ensure_dir([destin_dir])
filenames = from_dir(origin_dir, re.compile(r'.*\.' + file_ext))
counter = 0
for filename in filenames:
# identify strain name
pattern = re.compile(r'^(.*)' + tail + '\.' + file_ext + '$')
capture = re.match(pattern, filename)
# substitute new name
if capture:
counter += 1
new_filename = capture.group(1) + ".fas"
# copy file
copyfile(origin_dir + filename, destin_dir + new_filename)
print capture.group(1)
file_ext = argv[3]
if len(argv) < 5:
trim_ids = ''
else:
trim_ids = argv[4]
blast_dir = origin_dir + "blast/"
hits_dir = origin_dir + "hits/"
remote_prot_db = "nr"
annot_gbk_dir = origin_dir + "annot_gbk/"
annot_aa_dir = origin_dir + "annot_aa/"
trn_file = origin_dir + "prodigal.trn"
ensure_dir([annot_gbk_dir, annot_aa_dir, blast_dir, hits_dir])
filenames = from_dir(seq_dir, re.compile(r'.*\.' + file_ext + '.*'))
for filename in filenames:
rec_name = filename[:filename.find(trim_ids + "." + file_ext)]
print rec_name, "..."
# load data
if file_ext == 'fas':
fas_file = seq_dir + "/" + filename
gbk_file = fas2gbk(fas_file)
record = load_genbank(gbk_file)
else:
gbk_file = seq_dir + "/" + filename
