def execute_file(input_filename_local,input_filename_local_2,file_name,chunk_number,myrank,ii,file_basecount_dict):
workflow_prov = []
err_chunk_file = wd.err_log_path + "/" + file_name + \
"_log_temp/" + file_name + "_" + str(ii).zfill(6)
stat_chunk_file = wd.stat_log_path + "/" + file_name + \
"_log_temp/" + file_name + "_" + str(ii).zfill(6)
str_out="*" * 50 + "ALIGNMENT STARTED" + "\t" + str(time.strftime("%Y/%m/%d %H:%M:%S", time.localtime())) + "*" * 50 + "\n"
yap_file_io.write_data(str_out,err_chunk_file)
yap_file_io.write_data(str_out,stat_chunk_file)
for filename_key in file_basecount_dict.iterkeys():
if filename_key == file_name:
for barcode in wd.barcode_dict.iterkeys():
barcode_value = yap_tools.rename_barcode(barcode)
barcode_dir_path = wd.workflow_output_path + "/" + file_name + "/" + barcode
aligner_dir_path = barcode_dir_path + "/" + "aligner_output"
if wd.alignment_sort_order != 'unsorted':
if barcode_value != '':
aligner_output_filename = aligner_dir_path + "/" + \
"aligner_" + file_name + \
"_" + barcode_value
else:
aligner_output_filename = aligner_dir_path + \
"/" + "aligner_" + file_name
else:
if barcode_value != '':
aligner_output_filename = aligner_dir_path + \
"/" + file_name + \
"_" + barcode_value
else:
aligner_output_filename = aligner_dir_path + \
"/" + file_name
if wd.run_preprocess_analysis == 'yes':
preprocessed_file_inp1 = ['pipe1']
preprocessed_file_inp2 = ['pipe2']
preprocess_dir_path = barcode_dir_path + \
"/" + "preprocess_output"
preprocessed_inp1 = preprocess_dir_path + \
"/" + "*preprocess_data*_1.txt"
preprocessed_inp2 = preprocess_dir_path + \
"/" + "*preprocess_data_*2.txt"
preprocessed_file_inp1 = glob.glob(
preprocessed_inp1)
if wd.paired_end_data == "yes":
preprocessed_file_inp2 = glob.glob(
preprocessed_inp2)
if (wd.paired_end_data== "yes" and preprocessed_file_inp1 and preprocessed_file_inp2) or (wd.paired_end_data != "yes" and preprocessed_file_inp1):
print "Entering Alignment section: Filename=", input_filename_local, "barcode=", barcode, "\n"
if wd.paired_end_data == 'yes':
workflow_prov.append(
"INPUT: " +
preprocessed_file_inp1[0] +
" and " +
preprocessed_file_inp2[0])
aligner_out_str, workflow_prov = yap_aligner.run_aligner(preprocessed_file_inp1[0], preprocessed_file_inp2[
0],aligner_output_filename, chunk_number,myrank,workflow_prov, err_chunk_file, stat_chunk_file)
else:
workflow_prov.append(
"INPUT: " +
preprocessed_file_inp1[0])
aligner_out_str, workflow_prov = yap_aligner.run_aligner(preprocessed_file_inp1[
0], '', aligner_output_filename, chunk_number,myrank, workflow_prov, err_chunk_file, stat_chunk_file)
if wd.write_preprocessed_data != 'yes':
prm1 = Popen(
"rm " +
preprocess_dir_path +
"/" +
"*preprocess_data*_1.txt",
shell='False').wait()
if paired_end_data == "yes":
if preprocessed_file_inp2:
prm2 = Popen(
"rm " +
preprocess_dir_path +
"/" +
"*preprocess_data*_2.txt",
shell='False').wait()
else:
print "Skipping Alignment for : Filename=", input_filename_local, "barcode=", barcode, "........", "No preprocessed data found"
else:
if wd.paired_end_data == 'yes':
workflow_prov.append(
"INPUT: " +
input_filename_local +
" and " +
input_filename_local_2)
aligner_out_str, workflow_prov = yap_aligner.run_aligner(
input_filename_local, input_filename_local_2, aligner_output_filename, 0, workflow_prov, err_chunk_file, stat_chunk_file)
else:
workflow_prov.append("INPUT: " + input_filename_local)
aligner_out_str, workflow_prov = yap_aligner.run_aligner(
input_filename_local, '', aligner_cmd_arr, aligner_output_filename, 0, workflow_prov, err_chunk_file, stat_chunk_file)
#remove temporary files created by aligners
rm_cmd = "rm " + \
aligner_output_filename + "*.sai"
if len(glob.glob(aligner_output_filename + "*.sai")) > 0:
prm = Popen(
rm_cmd, shell='False').wait()
if barcode in file_basecount_dict[filename_key]:
pass
else:
file_basecount_dict[
filename_key][barcode] = []
#write to log
str_out="*" * 50 + "ALIGNMENT FINISHED" + "\t" + str(time.strftime("%Y/%m/%d %H:%M:%S", time.localtime())) + "*" * 50 + "\n"
yap_file_io.write_data(str_out,err_chunk_file)
yap_file_io.write_data(str_out,stat_chunk_file)
return workflow_prov, file_basecount_dict
def execute_chunk(
input_file_list_local,
inp1,
inp2,
chunk_number,
myrank,
workflow_prov,
eqp_dict):
'''
Executes preprocess commands for chunked data and passes to the alignment stage
Takes chunked input data, filename list, chunk number, rank of the processor
and provenance list to append log data.
'''
# variable declaration
input_filename_local = input_file_list_local[0]
input_filename_local_2 = input_file_list_local[1]
file_name = input_file_list_local[2]
err_chunk_file = wd.err_log_path + "/" + file_name + \
"_log_temp/" + file_name + "_" + str(chunk_number).zfill(6)
stat_chunk_file = wd.stat_log_path + "/" + file_name + \
"_log_temp/" + file_name + "_" + str(chunk_number).zfill(6)
myhost = os.getenv('HOSTNAME')
yap_file_io.write_data("HOSTNAME: " + str(myhost) + "\n", err_chunk_file)
yap_file_io.write_data("HOSTNAME: " + str(myhost) + "\n", stat_chunk_file)
yap_file_io.write_data("CHUNK NUMBER: " + str(chunk_number) + "\n", err_chunk_file)
yap_file_io.write_data("CHUNK NUMBER: " + str(chunk_number) + "\n", stat_chunk_file)
seqs_arr1 = []
seqs_arr2 = []
read_length = wd.max_read_length
barcode_seqstruct_dict1 = {}
barcode_seqstruct_dict2 = {}
barcode_output_dict = {}
aligner_out_str = ''
sort_order = ''
barcode_flag = 'False'
sort_order = wd.alignment_sort_order
# convert the input data based on format given in workflow configuration
if wd.input_file_format == "qseq" or wd.input_file_format != wd.preprocess_output_file_format:
inp1 = yap_tools.convert_format(inp1)
if wd.paired_end_data == 'yes':
inp2 = yap_tools.convert_format(inp2)
if wd.run_preprocess_analysis == 'yes':
str_out = "-"*20 + "PREPROCESS STARTED" +"\t" + str(time.strftime("%Y/%m/%d %H:%M:%S", time.localtime())) + "-"*20 + "\n"
yap_file_io.write_data(str_out,err_chunk_file)
yap_file_io.write_data(str_out,stat_chunk_file)
# Run barcode splitter as first preprocess step
for jj in range(0, len(wd.preprocess_cmd_arr)):
preprocess_cmd_name = wd.preprocess_cmd_arr[jj][2][0][0]
preprocess_cmd = wd.preprocess_cmd_arr[jj][2][0][1]
if re.search('fastx_barcode_splitter', preprocess_cmd_name) is not None:
barcode_flag = 'True'
print "Entering " + preprocess_cmd_name + " : Filename=", input_filename_local, " chunk number=", chunk_number, "\n"
str_out= "YAP_COMMAND: " + preprocess_cmd + "\n" + "INPUT FILE: " + input_filename_local
yap_file_io.write_data(str_out,err_chunk_file)
yap_file_io.write_data(str_out,stat_chunk_file)
barcode_seqstruct_dict1, workflow_prov = yap_preprocess.fastx_barcode_splitter(
inp1, wd.preprocess_output_file_format, preprocess_cmd, workflow_prov, err_chunk_file, stat_chunk_file)
yap_file_io.write_data("_" * 30 + "\n", err_chunk_file)
yap_file_io.write_data("_" * 30 + "\n", stat_chunk_file)
barcode_seqstruct_dict1["no_barcode_specified"] = ''
print "Exiting " + preprocess_cmd_name + " : Filename=", input_filename_local, " chunk number=", chunk_number, "\n"
if wd.paired_end_data == 'yes':
print "Entering " + preprocess_cmd_name + " : Filename=", input_filename_local_2, " chunk number=", chunk_number, "\n"
str_out= "YAP_COMMAND: " + preprocess_cmd + "\n" + "INPUT FILE: " + input_filename_local_2
yap_file_io.write_data(str_out,err_chunk_file)
yap_file_io.write_data(str_out,stat_chunk_file)
barcode_seqstruct_dict2, workflow_prov = yap_preprocess.fastx_barcode_splitter(
inp2,wd.preprocess_output_file_format , preprocess_cmd, workflow_prov, err_chunk_file, stat_chunk_file)
yap_file_io.write_data("_" * 30 + "\n", err_chunk_file)
yap_file_io.write_data("_" * 30 + "\n", stat_chunk_file)
barcode_seqstruct_dict2["no_barcode_specified"] = ''
print "Exiting " + preprocess_cmd_name + " : Filename=", input_filename_local, " chunk number=", chunk_number, "\n"
break
if barcode_flag == 'False':
#if no barcode command; then create dictionary with one barcode tag
barcode_seqstruct_dict1["no_barcode_specified"] = inp1
barcode_seqstruct_dict2["no_barcode_specified"] = inp2
else:
#if no preprocess stage specified; then create dictionary with one barcode tag
barcode_seqstruct_dict1["no_barcode_specified"] = inp1
barcode_seqstruct_dict2["no_barcode_specified"] = inp2
#iterate over the barcode dictionary
for barcode, inp1 in barcode_seqstruct_dict1.iteritems():
run_unique_reads = 'False'
barcode_value = yap_tools.rename_barcode(barcode)
if wd.paired_end_data == "yes":
inp2 = barcode_seqstruct_dict2[barcode]
preprocessed_data_dict = {}
#intialize matrix for basecount analysis
aligner_output_str_local = ''
basecount_matrix_local1 = numpy.zeros(
(int(read_length), 5), dtype=numpy.int)
basecount_matrix_local2 = numpy.zeros(
(int(read_length), 5), dtype=numpy.int)
barcode_output_dict.setdefault(barcode, [basecount_matrix_local1, basecount_matrix_local2])
#set output file paths
barcode_dir_path = wd.workflow_output_path + "/" + file_name + "/" + barcode
preprocess_dir_path = barcode_dir_path + "/" + "preprocess_output"
if wd.data_distribution_method != "file_based":
if barcode_value != '':
preprocess_out_filename1 = preprocess_dir_path + "/" + barcode_value + "_" + file_name + \
"_" + str(chunk_number).zfill(6) + "_" + \
str(myrank) + "_preprocessed_data_1.txt"
preprocess_out_filename2 = preprocess_dir_path + "/" + barcode_value + "_" + file_name + \
"_" + str(chunk_number).zfill(6) + "_" + \
str(myrank) + "_preprocessed_data_2.txt"
else:
preprocess_out_filename1 = preprocess_dir_path + "/" + file_name + "_" + \
str(chunk_number).zfill(6) + "_" + \
str(myrank) + "_preprocessed_data_1.txt"
preprocess_out_filename2 = preprocess_dir_path + "/" + file_name + "_" + \
str(chunk_number).zfill(6) + "_" + \
str(myrank) + "_preprocessed_data_2.txt"
else:
if barcode_value != '':
preprocess_out_filename1 = preprocess_dir_path + "/" + \
"preprocess_data" + "_" + file_name + \
"_" + barcode_value + "_1.txt"
preprocess_out_filename2 = preprocess_dir_path + "/" + \
"preprocess_data" + "_" + file_name + \
"_" + barcode_value + "_2.txt"
else:
preprocess_out_filename1 = preprocess_dir_path + "/" + \
"preprocess_data" + "_" + file_name + "_1.txt"
preprocess_out_filename2 = preprocess_dir_path + "/" + \
"preprocess_data" + "_" + file_name + "_2.txt"
aligner_dir_path = barcode_dir_path + "/" + "aligner_output"
if barcode_value != '':
aligner_output_filename = aligner_dir_path + "/" + "aligner_" + \
file_name + "_" + barcode_value + \
"_" + str(chunk_number).zfill(6)
else:
aligner_output_filename = aligner_dir_path + "/" + \
"aligner_" + file_name + "_" + str(chunk_number).zfill(6)
for jj in range(0, len(wd.preprocess_cmd_arr)):
preprocess_cmd_name = wd.preprocess_cmd_arr[jj][2][0][1]
preprocess_cmd = wd.preprocess_cmd_arr[jj][2][0][1]
# skip fastqc and fastq screen and barcode splitter as they are
# already executed
if (re.search('fastqc', preprocess_cmd_name) is not None) or (re.search('fastq_screen', preprocess_cmd_name) is not None)or(re.search('fastx_barcode_splitter',
preprocess_cmd_name) is not None):
pass
else:
if re.search('calculate_basecount_metrics', preprocess_cmd_name) is not None:
#excecute basecount calculation
basecount_matrix_local1, workflow_prov = yap_tools.qc_basecount(
inp1, workflow_prov)
basecount_matrix_local2, workflow_prov = yap_tools.qc_basecount(
inp2, workflow_prov)
elif re.search('fastx_clipper', preprocess_cmd_name) is not None:
"""
Check for fastx clipper as special case and execute.
This is because fastx clipper execution has been optimized by providing contaminants for every file,
instead of just applying contaminants universally.
"""
run_unique_reads = 'True'
if input_filename_local in wd.contaminant_dict.keys():
contaminants_arr1 = wd.contaminant_dict[
input_filename_local]
print "Entering " + preprocess_cmd_name + " : Filename=", input_filename_local, " chunk number=", chunk_number, "\n"
index = 0
for index in range(0, len(contaminants_arr1)):
#iterate over all the contaminants for this file
fastx_clipper_cmd = preprocess_cmd
contaminant1 = contaminants_arr1[index].strip("\n")
if inp1 != '':
cont_replace = " -a " + contaminant1
fastx_clipper_cmd = fastx_clipper_cmd.replace(
'pipe1', " - ") + " -a " + contaminant1
inp1 = yap_tools.multiproc_function(
fastx_clipper_cmd, inp1, int(
wd.format_specific_lines), '', err_chunk_file, stat_chunk_file)
yap_log.merge_multiproc_files(
fastx_clipper_cmd,
input_filename_local,
barcode,
err_chunk_file,
stat_chunk_file)
if inp1 == '':
break
print "Exiting " + preprocess_cmd_name + " : Filename=", input_filename_local, " chunk number=", chunk_number, "\n"
if wd.paired_end_data == 'yes':
if input_filename_local_2 in wd.contaminant_dict.keys():
#repeat fastx clipper for the paired end
contaminants_arr2 = wd.contaminant_dict[
input_filename_local_2]
print "Entering " + preprocess_cmd_name + " : Filename=", input_filename_local_2, " chunk number=", chunk_number, "\n"
index = 0
for index in range(0, len(contaminants_arr2)):
fastx_clipper_cmd = preprocess_cmd
contaminant2 = contaminants_arr2[
index].strip("\n")
if inp2 != '':
cont_replace = " -a " + contaminant2
fastx_clipper_cmd = fastx_clipper_cmd.replace(
'pipe1',
" - ") + " -a " + contaminant2
inp2 = yap_tools.multiproc_function(
fastx_clipper_cmd, inp2, int(
wd.format_specific_lines), '', err_chunk_file, stat_chunk_file)
yap_log.merge_multiproc_files(
fastx_clipper_cmd,
input_filename_local_2,
barcode,
err_chunk_file,
stat_chunk_file)
if inp2 == '':
break
print "Exiting " + preprocess_cmd_name + " : Filename=", input_filename_local_2, " chunk number=", chunk_number, "\n"
elif re.search('eqp_rename_reads',preprocess_cmd_name) != None:
# this section renames reads according to specific format, applies to in-house use, neglect otherwise
inp1_arr = inp1.splitlines(1)
inp1=''
inp2_arr = inp2.splitlines(1)
inp2=''
read_count=1
if wd.data_distribution_method == "file_based":
if eqp_dict.has_key("eqp_read_counter"):
if len(eqp_dict["eqp_read_counter"]) > 0:
file_name, read_count = eqp_dict["eqp_read_counter"]
if file_name != input_filename_local:
read_count = 1
format_lines = int(wd.format_specific_lines)
for i in range(0,len(inp1_arr),format_lines):
if wd.paired_end_data == 'yes':
if (len(inp1_arr[i+1].strip("\n").replace('A','')) >= 5) and (len(inp2_arr[i+1].strip("\n").replace('A','')) >= 5) and (len(inp1_arr[i+1].strip("\n").replace('T','')) >= 5) and (len(inp2_arr[i+1].strip("\n").replace('T','')) >= 5) :
inp1 += '@F'+str(read_count).zfill(9)+'/1'+'\n'
inp2 += '@F'+str(read_count).zfill(9)+'/2'+'\n'
for jj in range (1,format_lines):
inp1 += inp1_arr[i+jj]
inp2 += inp2_arr[i+jj]
read_count += 1
else:
if (len(inp1_arr[i+1].strip("\n").replace('A','')) >= 5) and (len(inp1_arr[i+1].strip("\n").replace('T','')) >= 5):
inp1_arr[i] = '@F'+str(read_count).zfill(9)+'/1'+'\n'
for jj in range (1,format_lines):
inp1 += inp1_arr[i+jj]
read_count += 1
eqp_dict["eqp_read_counter"] = [ input_filename_local, read_count]
inp1_arr = []
inp2_arr = []
else:
#set the flag to remove umatched pair after preprocesing
run_unique_reads = 'True'
print "Entering " + preprocess_cmd_name + " : Filename=", input_filename_local, " chunk number=", chunk_number, "\n"
#for all other preprocess commands execute this section
if inp1 != '':
preprocess_cmd = preprocess_cmd.replace('pipe1', ' - ')
inp1 = yap_tools.multiproc_function(
preprocess_cmd, inp1, int(
wd.format_specific_lines), '', err_chunk_file, stat_chunk_file)
yap_log.merge_multiproc_files(
preprocess_cmd,
input_filename_local,
barcode,
err_chunk_file,
stat_chunk_file)
print "Exiting " + preprocess_cmd_name + " : Filename=", input_filename_local, " chunk number=", chunk_number, "\n"
if wd.paired_end_data == 'yes':
preprocess_cmd = preprocess_cmd.replace('pipe1', ' - ')
print "Entering " + preprocess_cmd_name + " : Filename=", input_filename_local_2, " chunk number=", chunk_number, "\n"
if inp2 != '':
inp2 = yap_tools.multiproc_function(
preprocess_cmd, inp2, int(
wd.format_specific_lines), '', err_chunk_file, stat_chunk_file)
yap_log.merge_multiproc_files(
preprocess_cmd,
input_filename_local_2,
barcode,
err_chunk_file,
stat_chunk_file)
print "Exiting " + preprocess_cmd_name + " : Filename=", input_filename_local_2, " chunk number=", chunk_number, "\n"
if wd.paired_end_data == 'yes':
if run_unique_reads == 'True':
#remove all the umatched pairs from two chunks belonging to the same sample
#this is because each chunk goes through command separately, not as a pair.
if inp1 != '' and inp2 != '':
inp1, inp2 = yap_tools.find_unique_set(
inp1.splitlines(1), inp2.splitlines(1))
if wd.run_preprocess_analysis == 'yes':
#write log data
str_out="-"*20 + "PREPROCESS FINISHED" + "\t" + str(time.strftime("%Y/%m/%d %H:%M:%S", time.localtime())) + "-"*20 + "\n"
yap_file_io.write_data(str_out, err_chunk_file)
yap_file_io.write_data(str_out, stat_chunk_file)
if wd.data_distribution_method != "file_based":
#if the workflow is not filebased; then pass the chunks for alignment.
if wd.run_reference_alignment == 'yes':
str_out="-"*20 + "ALIGNMENT STARTED" + "\t" + str(time.strftime("%Y/%m/%d %H:%M:%S", time.localtime())) + "-"*20 + "\n"
yap_file_io.write_data(str_out, err_chunk_file)
yap_file_io.write_data(str_out, stat_chunk_file)
if (wd.paired_end_data == 'yes' and inp1 != '' and inp2 != '') or (wd.paired_end_data != 'yes' and inp1 != ''):
print "Entering Alignment: Filename=", input_filename_local, "barcode=", barcode, " chunk number=", chunk_number, "\n"
if wd.paired_end_data == 'yes':
workflow_prov.append(
"INPUT: " +
input_filename_local +
" and " +
input_filename_local_2 +
" chunk number= " +
str(chunk_number))
aligner_out_str, workflow_prov = yap_aligner.run_aligner(
inp1, inp2,aligner_output_filename, chunk_number, myrank,workflow_prov, err_chunk_file, stat_chunk_file)
else:
workflow_prov.append(
"INPUT: " +
input_filename_local +
" chunk number= " +
str(chunk_number))
aligner_out_str, workflow_prov = yap_aligner.run_aligner(
inp1, '', aligner_output_filename, chunk_number,myrank,workflow_prov, err_chunk_file, stat_chunk_file)
rm_cmd = "rm " + aligner_output_filename + "*.sai"
if len(glob.glob(aligner_output_filename + "*.sai")) > 0:
prm = Popen(rm_cmd, shell='False').wait()
if len(glob.glob(aligner_output_filename + "*.head")) > 0:
prm = Popen(rm_cmd, shell='False').wait()
else:
print "Exiting Alignment: Filename=", input_filename_local, "barcode=", barcode, " chunk number=", chunk_number, "\n"
str_out="-"*20 + "ALIGNMENT FINISHED" + "\t" + str(time.strftime("%Y/%m/%d %H:%M:%S", time.localtime())) + "-"*20+ "\n"
yap_file_io.write_data(str_out, err_chunk_file)
yap_file_io.write_data(str_out, stat_chunk_file)
if wd.run_preprocess_analysis == 'yes':
if wd.write_preprocessed_data == 'yes':
#write preprocessed data to file
yap_file_io.write_data(inp1, preprocess_out_filename1)
if wd.paired_end_data == "yes":
yap_file_io.write_data(inp2, preprocess_out_filename2)
else:
#else empty input data chunk
inp1 = ''
inp2 = ''
else:
#else empty input data chunk
inp1 = ''
inp2 = ''
else:
#if workflow is filebased; then write preprocessed data to file
if wd.run_preprocess_analysis == "yes":
if wd.write_preprocessed_data == 'yes' or wd.run_reference_alignment == "yes":
yap_file_io.write_data(inp1, preprocess_out_filename1)
if wd.paired_end_data == "yes":
yap_file_io.write_data(inp2, preprocess_out_filename2)
barcode_output_dict[barcode][0] = basecount_matrix_local1
barcode_output_dict[barcode][1] = basecount_matrix_local2
return barcode_output_dict, workflow_prov
