def main(input_filename, output_filename):
task_ids = [1]
if os.environ.has_key('SGE_TASK_ID'):
sge_task_id = int(os.environ['SGE_TASK_ID'])
if not os.environ.has_key('SGE_STEP_SIZE'):
sge_step_size = 1
else:
sge_step_size = int(os.environ['SGE_STEP_SIZE'])
sge_task_last = int(os.environ['SGE_TASK_LAST'])
task_ids = range(
sge_task_id,
min(sge_task_id + sge_step_size, sge_task_last + 1)
)
print task_ids, sge_task_id, sge_step_size, sge_task_last
for task_id in task_ids:
inp_filename = input_filename.format(id=task_id - 1)
out_filename = output_filename.format(id=task_id - 1)
aln = list(Fasta.load(
inp_filename,
'',
Alignment,
sequence_selectors=['sequence1', 'sequence2']))[0]
tmp_filename = get_temp_filename()
Fasta.save(zip(aln.names, aln.sequences), tmp_filename)
os.system("muscle -in {inp} -out {out} 2> /dev/null".format(
inp=tmp_filename,
out=out_filename,
))
os.system("cp {inp}.repeats {out}.repeats".format(
inp=inp_filename,
out=out_filename,
))
def create_fasta(fname, seq_basename, sequences, rename=False):
if rename:
names = [seq_basename + str(i + 1) for i in range(len(sequences))]
else:
names = [s["name"] for s in sequences]
assert len(names) == len(sequences)
seq = [s["sequence"].upper() for s in sequences]
Fasta.save(zip(names, seq), fname)
return names
def create_fasta(fname, seq_basename, sequences, rename=False):
if rename:
names = [seq_basename + str(i + 1) for i in range(len(sequences))]
else:
names = [s['name'] for s in sequences]
assert (len(names) == len(sequences))
seq = [s['sequence'].upper() for s in sequences]
Fasta.save(zip(names, seq), fname)
return names
def main():
parser = argparse.ArgumentParser(description='Sample alignments.')
parser.add_argument('output_file_template', type=str,
help="Template for output file. Have to contain " + \
"string '{id}' as placeholder for sequence number.")
parser.add_argument('--output_files', type=str, help="File where the " + \
'list of output files will be written.', default='-')
parser.add_argument('--model', type=str,
default='data/models/repeatHMM.js', help="Model file")
parser.add_argument('--bind_file', nargs='*', help='Replace filenames in '
+ 'the input_file model.', default=[])
parser.add_argument('--bind_constant', nargs='*', help='Replace constants'
+ ' in the input_file model.', default=[])
parser.add_argument('--bind_constant_file', nargs='*', help='Replace' +
' constants in the input_file model.', default=[])
parser.add_argument('n_samples', type=int, help='Number of samples.')
parser.add_argument('seq1_length',type=int,
help='Length of first sequence.')
parser.add_argument('seq2_length', type=int,
help='Length of second sequence.')
parsed_arg = parser.parse_args()
# ====== Validate input parameters =========================================
if parsed_arg.output_file_template.count("{id}") < 1:
sys.stderr.write('ERROR: If sampling, output_file filename has to ' +\
'contain at least one "%d".\n')
return 1
if len(parsed_arg.bind_file) % 2 != 0:
sys.stderr.write('ERROR: If binding files, the number of arguments has'
+ 'to be divisible by 2\n')
return 1
if len(parsed_arg.bind_constant_file) % 2 != 0:
sys.stderr.write('ERROR: If binding constants (as files), the number of'
+ ' arguments has to be divisible by 2\n')
return 1
if len(parsed_arg.bind_constant) % 2 != 0:
sys.stderr.write('ERROR: If binding constants, the number of'
+ ' arguments has to be divisible by 2\n')
return 1
# ====== Parse parameters ==================================================
output_filename = parsed_arg.output_file_template
output_files_filename = parsed_arg.output_files
output_files = list()
# ====== Load model ========================================================
loader = HMMLoader()
for i in range(0, len(parsed_arg.bind_constant), 2):
loader.addFile(parsed_arg.bind_file[i], parsed_arg.bind_file[i + 1])
for i in range(0, len(parsed_arg.bind_constant_file), 2):
loader.addConstant(
parsed_arg.bind_constant_file[i],
loader.load(parsed_arg.bind_constant_file[i + 1])
)
for i in range(0, len(parsed_arg.bind_constant), 2):
loader.addConstant(
parsed_arg.bind_constant[i],
loader.loads(parsed_arg.bind_constant[i + 1]),
)
model_filename = parsed_arg.model
PHMM = loader.load(model_filename)["model"]
# ====== Sample ============================================================
PHMM.buildSampleTransitions()
n_samples = parsed_arg.n_samples
X_len = parsed_arg.seq1_length
Y_len = parsed_arg.seq2_length
dirname = os.path.dirname(output_filename)
if not os.path.exists(dirname):
os.makedirs(dirname)
for i in range(n_samples):
done = False
while not done:
tandemRepeats = {'sequence1': [], 'sequence2': []}
seq = PHMM.generateSequence((X_len, Y_len))
X = ""
Y = ""
A = ""
for (seq, state) in seq:
ann_data = None
if len(seq) == 2:
x, y = seq
else:
x, y, ann_data = seq
dx, dy = len(x), len(y)
if ann_data != None:
xlen = len(X.replace('-', ''))
ylen = len(Y.replace('-', ''))
if dx > 0:
tandemRepeats['sequence1'].append((
xlen, xlen + dx, dx / ann_data[1], ann_data[0], x
))
done = True
if dy > 0:
tandemRepeats['sequence2'].append((
ylen, ylen + dy, dy / ann_data[2], ann_data[0], y
))
done = True
A += PHMM.states[state].getChar() * max(dx, dy)
X += x + ('-' * (dy - dx))
Y += y + ('-' * (dx - dy))
#if len(X) - X.count('-') > 2 * X_len:
# done = False
#if len(Y) - Y.count('-') > 2 * Y_len:
# done = False
aln = [("sequence1", X), ("alignment", A), ("sequence2", Y)]
json.dump(tandemRepeats, Open(output_filename.format(id=i) + '.repeats',
'w'), indent=4)
Fasta.save(aln, output_filename.format(id=i))
output_files.append(output_filename.format(id=i))
with Open(output_files_filename, 'w') as output_file_object:
json.dump(output_files, output_file_object, indent=4)
return 0
def select_sequences(inp_filename, out_filename, sequences):
aln = list(Fasta.load(inp_filename,
'',
Alignment,
sequence_selectors=sequences))[0]
Fasta.save(zip(aln.names, aln.sequences), out_filename)
def main(n, datadir='data/train_sequences/', fname='simulated_alignment'):
s1name = "sequence1"
s2name = "sequence2"
s3name = "sequence3"
annotation_name = 'gene'
alignment_extension = ".fa"
annotations_extension = ".bed"
config_extension = ".js"
if len(sys.argv) > 1:
n = int(sys.argv[1])
if len(sys.argv) > 2:
fname = sys.argv[2]
master_gene_sequence = MarkovChain(P_START_GENE, P_STOP_GENE)
human_delete_sequence = MarkovChain(P_START_DELETE, P_STOP_DELETE)
mouse_delete_sequence = MarkovChain(P_START_DELETE, P_STOP_DELETE)
horse_delete_sequence = MarkovChain(P_START_DELETE, P_STOP_DELETE)
mutator_coin = BiasedCoin(P_NOT_MUTATE_GENE)
master_gene = list()
human_gene = list()
mouse_gene = list()
horse_gene = list()
human_dna = list()
mouse_dna = list()
horse_dna = list()
for i in range(n):
# create master_gene item
g = g2 = g3 = g4 = master_gene_sequence.get_state()
# mutate master_gene item
if g:
g2 = mutator_coin.flip()
g3 = mutator_coin.flip()
g4 = mutator_coin.flip()
dna_mutation_coin = create_dna_mutation_coin(g2 + g3)
dna_mutation_coin2 = create_dna_mutation_coin(g2 + g4)
# create DNA item
c = c2 = c3 = random.randint(0, 3)
c2 = mutate(c2, g2 + g3)
c, c2, c3 = [DNA_CHARS[i] for i in (c, c2, c3)]
if not dna_mutation_coin.flip():
char_index = random.randint(0, 2)
if DNA_CHARS[char_index] == c2:
char_index = 3
c2 = DNA_CHARS[char_index]
if not dna_mutation_coin2.flip():
char_index = random.randint(0, 2)
if DNA_CHARS[char_index] == c3:
char_index = 3
c3 = DNA_CHARS[char_index]
# delete DNA item
if human_delete_sequence.get_state():
c = '-'
if mouse_delete_sequence.get_state():
c2 = '-'
if horse_delete_sequence.get_state():
c3 = '-'
# add items to sequence
master_gene.append(g)
human_gene.append(g2)
mouse_gene.append(g3)
horse_gene.append(g4)
human_dna.append(c)
mouse_dna.append(c2)
horse_dna.append(c3)
# output
s1fname = os.path.join(
datadir,
fname + '_' + s1name + '_' + annotation_name + annotations_extension)
if os.path.isfile(s1fname):
os.remove(s1fname)
s2fname = os.path.join(
datadir,
fname + '_' + s2name + '_' + annotation_name + annotations_extension)
if os.path.isfile(s2fname):
os.remove(s2fname)
s3fname = os.path.join(
datadir,
fname + '_' + s3name + '_' + annotation_name + annotations_extension)
if os.path.isfile(s3fname):
os.remove(s3fname)
intervals1 = sequence_to_intervals(get_sequence(human_gene, human_dna),
annotation_name)
intervals2 = sequence_to_intervals(get_sequence(mouse_gene, mouse_dna),
annotation_name)
intervals3 = sequence_to_intervals(get_sequence(horse_gene, horse_dna),
annotation_name)
annotations = Annotations()
annotations.setAnnotations([annotation_name])
annotations.addSequences([s1name, s2name, s3name])
annotations.addAnnotationFile(s1name, annotation_name, s1fname)
annotations.addAnnotationFile(s2name, annotation_name, s2fname)
# annotations.addAnnotationFile(s3name, annotation_name, s3fname)
Fasta.save(
[
(s1name, ''.join(human_dna)),
(s2name, ''.join(mouse_dna)),
# (s3name, ''.join(horse_dna))
],
os.path.join(datadir, fname + alignment_extension))
with track.new(s1fname, 'bed') as t:
t.fields = ['start', 'end', 'name']
t.write("chr1", intervals1)
with track.new(s2fname, 'bed') as t:
t.fields = ['start', 'end', 'name']
t.write("chr1", intervals2)
# with track.new(s3fname, 'bed') as t:
# t.fields = ['start', 'end', 'name']
# t.write("chr1", intervals3)
with Open(os.path.join(datadir, fname + config_extension), "w") as f:
json.dump(annotations.toJSON(), f)
def simulate(
n,
datadir='data/sequences/train_sequences/',
fname='simulated_alignment',
):
s1name = "sequence1"
s2name = "sequence2"
s3name = "sequence3"
annotation_name = 'gene'
alignment_extension = ".fa"
annotations_extension = ".bed"
config_extension = ".js"
# if len(sys.argv) > 1:
# n = int(sys.argv[1])
# if len(sys.argv) > 2:
# fname = sys.argv[2]
master_gene_sequence = MarkovChain(P_START_GENE, P_STOP_GENE)
human_delete_sequence = MarkovChain(P_START_DELETE, P_STOP_DELETE)
mouse_delete_sequence = MarkovChain(P_START_DELETE, P_STOP_DELETE)
horse_delete_sequence = MarkovChain(P_START_DELETE, P_STOP_DELETE)
mutator_coin = BiasedCoin(P_NOT_MUTATE_GENE)
master_gene = list()
human_gene = list()
mouse_gene = list()
horse_gene = list()
human_dna = list()
mouse_dna = list()
horse_dna = list()
for i in range(n):
# create master_gene item
g = g2 = g3 = g4 = master_gene_sequence.get_state()
# mutate master_gene item
if g:
g2 = mutator_coin.flip()
g3 = mutator_coin.flip()
g4 = mutator_coin.flip()
dna_mutation_coin = create_dna_mutation_coin(g2 + g3)
dna_mutation_coin2 = create_dna_mutation_coin(g2 + g4)
# create DNA item
c = c1 = c2 = c3 = DNA_CHARS[random.randint(0, 3)]
if not dna_mutation_coin.flip():
char_index = random.randint(0, 2)
if DNA_CHARS[char_index] == c1:
char_index = 3
c1 = DNA_CHARS[char_index]
if not dna_mutation_coin.flip():
char_index = random.randint(0, 2)
if DNA_CHARS[char_index] == c2:
char_index = 3
c2 = DNA_CHARS[char_index]
if not dna_mutation_coin2.flip():
char_index = random.randint(0, 2)
if DNA_CHARS[char_index] == c3:
char_index = 3
c3 = DNA_CHARS[char_index]
# delete DNA item
if human_delete_sequence.get_state():
c1 = '-'
if mouse_delete_sequence.get_state():
c2 = '-'
if horse_delete_sequence.get_state():
c3 = '-'
# add items to sequence
master_gene.append(g)
human_gene.append(g2)
mouse_gene.append(g3)
horse_gene.append(g4)
human_dna.append(c1)
mouse_dna.append(c2)
horse_dna.append(c3)
# output
s1fname = os.path.join(
datadir, fname+'_'+s1name+'_'+annotation_name+annotations_extension
)
if os.path.isfile(s1fname):
os.remove(s1fname)
s2fname = os.path.join(
datadir, fname+'_'+s2name+'_'+annotation_name+annotations_extension
)
if os.path.isfile(s2fname):
os.remove(s2fname)
s3fname = os.path.join(
datadir, fname+'_'+s3name+'_'+annotation_name+annotations_extension
)
if os.path.isfile(s3fname):
os.remove(s3fname)
intervals1 = sequence_to_intervals(
get_sequence(human_gene, human_dna), annotation_name
)
intervals2 = sequence_to_intervals(
get_sequence(mouse_gene, mouse_dna), annotation_name
)
intervals3 = sequence_to_intervals(
get_sequence(horse_gene, horse_dna), annotation_name
)
annotations = Annotations()
annotations.setAnnotations([annotation_name])
annotations.addSequences([s1name, s2name, s3name])
annotations.addAnnotationFile(s1name, annotation_name, s1fname)
annotations.addAnnotationFile(s2name, annotation_name, s2fname)
annotations.addAnnotationFile(s3name, annotation_name, s3fname)
Fasta.save(
[
(s1name, ''.join(human_dna)),
(s2name, ''.join(mouse_dna)),
(s3name, ''.join(horse_dna))
],
os.path.join(datadir, fname+alignment_extension)
)
with track.new(s1fname, 'bed') as t:
t.fields = ['start', 'end', 'name']
t.write("chr1", intervals1)
with track.new(s2fname, 'bed') as t:
t.fields = ['start', 'end', 'name']
t.write("chr1", intervals2)
with track.new(s3fname, 'bed') as t:
t.fields = ['start', 'end', 'name']
t.write("chr1", intervals3)
with Open(os.path.join(datadir, fname+config_extension), "w") as f:
json.dump(annotations.toJSON(), f)
def select_sequences(inp_filename, out_filename, sequences):
aln = list(
Fasta.load(inp_filename, '', Alignment,
sequence_selectors=sequences))[0]
Fasta.save(zip(aln.names, aln.sequences), out_filename)
