def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
sub_taxonomy_list=[]
taxonomic_rank_dictionary=create_taxonomic_rank_dictionary(opts.input_taxonomy_fps)
otu_dictionary=create_otu_dictionary(opts.input_fasta_fps)
if opts.taxonomy_level=="":
if path.isdir(opts.output_dir)==False:
mkdir(opts.output_dir,0755)
sub_taxonomy=split_taxonomy_list(opts.input_taxonomy_fps,7,opts.output_dir)
sub_taxonomy_list.append(sub_taxonomy)
assign_otuID_to_seqs(taxonomic_rank_dictionary,otu_dictionary,sub_taxonomy_list,opts.output_dir)
else:
sub_taxonomy=split_taxonomy_list(opts.input_taxonomy_fps,7,opts.output_dir)
sub_taxonomy_list.append(sub_taxonomy)
assign_otuID_to_seqs(taxonomic_rank_dictionary,otu_dictionary,sub_taxonomy_list,opts.output_dir)
else:
if path.isdir(opts.output_dir)==False:
mkdir(opts.output_dir,0755)
sub_taxonomy=split_taxonomy_list(opts.input_taxonomy_fps,opts.taxonomy_level,opts.output_dir)
sub_taxonomy_list.append(sub_taxonomy)
assign_otuID_to_seqs(taxonomic_rank_dictionary,otu_dictionary,sub_taxonomy_list,opts.output_dir)
else:
sub_taxonomy=split_taxonomy_list(opts.input_taxonomy_fps,opts.taxonomy_level,opts.output_dir)
sub_taxonomy_list.append(sub_taxonomy)
assign_otuID_to_seqs(taxonomic_rank_dictionary,otu_dictionary,sub_taxonomy_list,opts.output_dir)
build_cm_models(opts.output_dir)
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
sub_taxonomy_list = []
if opts.taxonomy_level == "":
mkdir(opts.output_dir, 0755)
sub_taxonomy = split_taxonomy_list(opts.input_taxonomy_fps, "7", opts.output_dir)
sub_taxonomy_list.append(sub_taxonomy)
assign_seqID_to_seqs(taxon_list, opts.input_fasta_fps, opts.output_dir)
else:
mkdir(opts.output_dir, 0755)
sub_taxonomy = split_taxonomy_list(opts.input_taxonomy_fps, opts.taxonomy_level, opts.output_dir)
sub_taxonomy_list.append(sub_taxonomy)
assign_seqID_to_seqs(sub_taxonomy_list, opts.input_fasta_fps, opts.output_dir)
def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
Query_collection=[]
rank_collection=[]
Query_dict=defaultdict(list)
temp_dir_name=tempfile.mkdtemp(prefix='root_')
level=0
for root,dirs,files in os.walk(opts.input_HMM_fp):
print root
#print dirs
#print files
#print ".................."
path_to_db=os.path.join(root,'db')
path_to_result=os.path.join(root,'result.out')
if level==0:
stdout,stderr,return_value = qcli_system_call('hmmscan '+path_to_db+' '+opts.input_query_fp+' > '+path_to_result)
if return_value != 0:
print 'Stdout:\n%s\nStderr:%s\n' % (stdout,stderr)
exit(1)
HMM_result=open(path_to_result,'U')
HMM_choice_list,HMM_Query_list,HMM_choice_list_with_ID=search_HMM(HMM_result)
create_temp_test_seq_file(temp_dir_name,HMM_choice_list_with_ID,open(opts.input_query_fp,'U'))
rank_collection.extend(HMM_choice_list)
Query_collection.extend(HMM_Query_list)
for Query_ID, rank in HMM_choice_list_with_ID:
Query_dict[Query_ID].append(rank)
else:
if os.path.basename(root) in rank_collection:
path_to_test_seq=os.path.join(temp_dir_name,os.path.basename(root)+'.fasta')
stdout,stderr,return_value = qcli_system_call('hmmscan '+path_to_db+' '+path_to_test_seq+' > '+path_to_result)
if return_value != 0:
print 'Stdout:\n%s\nStderr:%s\n' % (stdout,stderr)
exit(1)
HMM_result=open(path_to_result,'U')
HMM_choice_list,HMM_Query_list,HMM_choice_list_with_ID=search_HMM(HMM_result)
create_temp_test_seq_file(temp_dir_name,HMM_choice_list_with_ID,open(path_to_test_seq,'U'))
rank_collection.extend(HMM_choice_list)
for Query_ID, rank in HMM_choice_list_with_ID:
Query_dict[Query_ID].append(rank)
level+=1
shutil.rmtree(temp_dir_name)
taxonomy_assignment_to_query_seq(Query_dict,Query_collection)
def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
search_cmfile_to_cmpress(opts.input_dir)
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
barcode_type = opts.barcode_type
max_barcode_errors = opts.max_barcode_errors
mapping_fp = opts.mapping_fp
sequence_read_fps = opts.sequence_read_fps
min_consensus = opts.min_consensus
max_cluster_ratio = opts.max_cluster_ratio
output_dir = opts.output_dir
min_difference_in_bcs = opts.min_difference_in_bcs
fwd_length = opts.fwd_length
rev_length = opts.rev_length
min_reads_per_random_bc = opts.min_reads_per_random_bc
min_diff_in_clusters = opts.min_difference_in_clusters
barcode_column = opts.header_barcode_column
reverse_primer_column = opts.reverse_primer_column
create_dir(output_dir)
fwd_consensus_outfile = open(path.join(output_dir, "fwd.fna"), "w")
rev_consensus_outfile = open(path.join(output_dir, "rev.fna"), "w")
log_file = open(path.join(output_dir, "log.txt"), "w")
if barcode_type == 'golay_12':
barcode_correction_fn = decode_golay_12
barcode_len = 12
else:
barcode_correction_fn = None
try:
barcode_len = int(barcode_type)
except ValueError:
option_parser.error("Invalid barcode type '%s'. The barcode type "
"must be either golay_12 or a positive "
"integer indicating the barcode length." %
barcode_type)
if max_barcode_errors < 0:
option_parser.error("--max_barcode_errors must be greater than or "
"equal to zero. You provided %.4f." %
max_barcode_errors)
if barcode_len < 1:
option_parser.error("Invalid barcode length: %d. Must be greater "
"than zero." % barcode_len)
if len(sequence_read_fps) != 2:
option_parser.error("You must provide exactly two sequence read "
"filepaths, the first for forward reads and "
"second for reverse reads. You specified %d "
"filepaths." % len(sequence_read_fps))
fwd_read_f = open(sequence_read_fps[0], 'U')
rev_read_f = open(sequence_read_fps[1], 'U')
map_f = open(mapping_fp, 'U')
(consensus_seq_lookup,
log_out) = get_LEA_seq_consensus_seqs(fwd_read_f,
rev_read_f,
map_f,
output_dir,
barcode_type,
barcode_len,
barcode_correction_fn,
max_barcode_errors,
min_consensus,
max_cluster_ratio,
min_difference_in_bcs,
fwd_length,
rev_length,
min_reads_per_random_bc,
min_diff_in_clusters,
barcode_column,
reverse_primer_column)
for sample_id in consensus_seq_lookup:
for bc_index, rand_bc in enumerate(consensus_seq_lookup[sample_id]):
consensus_seq = consensus_seq_lookup[sample_id][rand_bc]
fwd_consensus, rev_consensus = consensus_seq.split('^')
fwd_consensus_outfile.write(">{}_{}\n{}\n".format(
sample_id, bc_index, fwd_consensus))
rev_consensus_outfile.write(">{}_{}\n{}\n".format(
sample_id, bc_index, rev_consensus))
log_file.write(log_out)
log_file.close()
fwd_read_f.close()
rev_read_f.close()
fwd_consensus_outfile.close()
rev_consensus_outfile.close()
map_f.close()
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
barcode_type = opts.barcode_type
max_barcode_errors = opts.max_barcode_errors
mapping_fp = opts.mapping_fp
sequence_read_fps = opts.sequence_read_fps
min_consensus = opts.min_consensus
max_cluster_ratio = opts.max_cluster_ratio
output_dir = opts.output_dir
min_difference_in_bcs = opts.min_difference_in_bcs
fwd_length = opts.fwd_length
rev_length = opts.rev_length
min_reads_per_random_bc = opts.min_reads_per_random_bc
min_diff_in_clusters = opts.min_difference_in_clusters
barcode_column = opts.header_barcode_column
reverse_primer_column = opts.reverse_primer_column
create_dir(output_dir)
fwd_consensus_outfile = open(path.join(output_dir, "fwd.fna"), "w")
rev_consensus_outfile = open(path.join(output_dir, "rev.fna"), "w")
log_file = open(path.join(output_dir, "log.txt"), "w")
if barcode_type == 'golay_12':
barcode_correction_fn = decode_golay_12
barcode_len = 12
else:
barcode_correction_fn = None
try:
barcode_len = int(barcode_type)
except ValueError:
option_parser.error("Invalid barcode type '%s'. The barcode type "
"must be either golay_12 or a positive "
"integer indicating the barcode length." %
barcode_type)
if max_barcode_errors < 0:
option_parser.error("--max_barcode_errors must be greater than or "
"equal to zero. You provided %.4f." %
max_barcode_errors)
if min_diff_in_clusters < 0 or min_diff_in_clusters > 1:
option_parser.error("--min_difference_in_clusters must be "
"between 0 to 1. You provided %.4f." %
min_diff_in_clusters)
if min_difference_in_bcs < 0 or min_difference_in_bcs > 1:
option_parser.error("--min_difference_in_bcs must be between 0 to 1."
" You provided %.4f." % min_difference_in_bcs)
if barcode_len < 1:
option_parser.error("Invalid barcode length: %d. Must be greater "
"than zero." % barcode_len)
if len(sequence_read_fps) != 2:
option_parser.error("You must provide exactly two sequence read "
"filepaths, the first for forward reads and "
"second for reverse reads. You specified %d "
"filepaths." % len(sequence_read_fps))
fwd_read_f = open(sequence_read_fps[0], 'U')
rev_read_f = open(sequence_read_fps[1], 'U')
map_f = open(mapping_fp, 'U')
(consensus_seq_lookup, log_out) = get_LEA_seq_consensus_seqs(
fwd_read_f, rev_read_f, map_f, output_dir, barcode_type, barcode_len,
barcode_correction_fn, max_barcode_errors, min_consensus,
max_cluster_ratio, min_difference_in_bcs, fwd_length, rev_length,
min_reads_per_random_bc, min_diff_in_clusters, barcode_column,
reverse_primer_column)
for sample_id in consensus_seq_lookup:
for bc_index, rand_bc in enumerate(consensus_seq_lookup[sample_id]):
consensus_seq = consensus_seq_lookup[sample_id][rand_bc]
fwd_consensus, rev_consensus = consensus_seq.split('^')
fwd_consensus_outfile.write(">{}_{}\n{}\n".format(
sample_id, bc_index, fwd_consensus))
rev_consensus_outfile.write(">{}_{}\n{}\n".format(
sample_id, bc_index, rev_consensus))
log_file.write(log_out)
log_file.close()
fwd_read_f.close()
rev_read_f.close()
fwd_consensus_outfile.close()
rev_consensus_outfile.close()
map_f.close()
