def test_get_header_info(self):
"""get_header_info should return a sff file common header as a dict"""
header = get_header_info(self.rec)
self.assertEqual(len(header), 11)
self.assertEqual(header['Key Length'], '4')
self.assertEqual(header['Key Sequence'], 'TCAG')
def submit_sff_and_split_lib(data_access,fasta_files,metadata_study_id):
"""
SFF Loading: This function takes the fasta filenames and using that path,
determines the location of the split-library and picked-otu files. Once
file locations have been determined, it moves the files to the DB machine
and load the files into the DB.
"""
# get database connection and cursor
con = data_access.getSFFDatabaseConnection()
cur = con.cursor()
### this may help in speeding up loading but shouldn't be necessary
#print 'Rebuilding PK_SPLIT_LIBRARY_READ_MAP...'
#cur.execute('alter index "SFF"."PK_SPLIT_LIBRARY_READ_MAP" rebuild ')
#cur = con.cursor()
# check if study exists
study_id_exists=data_access.checkIfStudyIdExists(metadata_study_id)
print "Study ID exists: " + str(study_id_exists)
# create a temp filename
alphabet = "ABCDEFGHIJKLMNOPQRSTUZWXYZ"
alphabet += alphabet.lower()
alphabet += "01234567890"
random_fname=''.join([choice(alphabet) for i in range(10)])
tmp_filename ='_'+random_fname+'_'+strftime("%Y_%m_%d_%H_%M_%S")
# get a list of filenames
fasta_filenames=fasta_files.split(',')
seq_run_id=0
analysis_id=0
split_lib_input_checksums=[]
fasta_qual_files=[]
### by disabling constraints you can speed up loading as well, but shouldn't
### be necessary
#valid = data_access.disableTableConstraints()
#print "Disabled table constraints"
#split the fasta filenames and determine filepaths
for fasta_fname in fasta_filenames:
input_fname, input_ext = splitext(split(fasta_fname)[-1])
input_basename, input_ext = splitext(fasta_fname)
input_dir = split(input_basename)[:-1][0]
# get the sff basename
if re.search('0\d$', input_fname)==None or re.search('0\d$',
input_fname).group()==None:
sff_basename=input_fname
else:
sff_basename=input_fname[:-2]
if re.search('0\d_FLX$', sff_basename)==None or re.search('0\d_FLX$',
sff_basename).group()==None:
sff_basename=sff_basename
else:
sff_basename=sff_basename[:-6]
print 'sff_basename: %s' % sff_basename
# get analysis notes
analysis_notes=split(input_basename)[0]
# using the fasta basename, define qual and flow files
qual_fname=join(input_basename+'.qual')
flow_fname=join(input_basename+'.txt')
fasta_qual_files.append(fasta_fname)
fasta_qual_files.append(qual_fname)
# Run the Oracle process_sff_files load package
## Get the location and name of the SFF file, get it's MD5. .SFF is one
# directory up from the other files
rev = dirname(fasta_fname)[::-1]
# check for sffs in the processed folder...only occurs for Ti processing
sffs_in_processed_folder = glob(join(input_dir, '*_FLX.sff'))
if len(sffs_in_processed_folder) == 0:
sff_file_dir = split(input_dir)[0]
else:
sff_file_dir=input_dir
# get SFF file
sff_file = join(sff_file_dir, input_fname + '.sff')
# get md5 of SFF
sff_md5 = safe_md5(open(sff_file)).hexdigest()
print 'MD5 is: %s' % str(sff_md5)
# create an analysis
if analysis_id==0:
analysis_id=data_access.createAnalysis(metadata_study_id)
# check if SFF info was already loaded into DB
sff_exists=data_access.checkIfSFFExists(sff_md5)
print 'sff in database? %s' % str(sff_exists)
#if True:
if not sff_exists:
print 'flow_fname: %s' % flow_fname
sff_header=get_header_info(open(flow_fname))
# get instrument info
if sff_header['# of Flows']=='400':
instrument_code='GS FLX'
elif sff_header['# of Flows']=='168':
instrument_code='GS2-'
elif sff_header['# of Flows']=='800':
instrument_code='Titanium'
else:
instrument_code='UNKNOWN'
print 'Instrument Code: %s' % instrument_code
# load SFF info
if seq_run_id==0:
seq_run_id=data_access.createSequencingRun(True,instrument_code,
sff_header['Version'],seq_run_id)
valid=data_access.addSFFFileInfo(True,sff_basename,
sff_header['# of Reads'],
sff_header['Header Length'],
sff_header['Key Length'],
sff_header['# of Flows'],
sff_header['Flowgram Code'],
sff_header['Flow Chars'],
sff_header['Key Sequence'],
sff_md5,seq_run_id)
else:
valid=data_access.addSFFFileInfo(True,sff_basename,
sff_header['# of Reads'],
sff_header['Header Length'],
sff_header['Key Length'],
sff_header['# of Flows'],
sff_header['Flowgram Code'],
sff_header['Flow Chars'],
sff_header['Key Sequence'],
sff_md5,seq_run_id)
else:
seq_run_id=data_access.getSeqRunIDUsingMD5(sff_md5)
print 'sequence_run_id is: %s' % str(seq_run_id)
# get md5 of fna/qual files
print fasta_qual_files
split_lib_input_md5sum=safe_md5(MD5Wrap(fasta_qual_files)).hexdigest()
print split_lib_input_md5sum
print 'Finished loading the processed SFF data!'
print 'Run ID: %s' % seq_run_id
print 'Analysis ID: %s' % analysis_id
# add seq_run_id to Analysis table
valid=data_access.updateAnalysisWithSeqRunID(True,analysis_id,seq_run_id)
if not valid:
raise ValueError, 'Error: Unable to append SEQ_RUN_ID into ANALYSIS table!'
return analysis_id,input_dir,seq_run_id,split_lib_input_md5sum
def test_get_header_info(self):
"""get_header_info should return a sff file common header as a dict"""
header = get_header_info(self.rec)
self.assertEqual(len(header), 11)
self.assertEqual(header['Key Length'], '4')
self.assertEqual(header['Key Sequence'], 'TCAG')