def tearDown(self):
remove_files(set(self.files_to_remove))
# remove directories last, so we don't get errors
# trying to remove files which may be in the directories
for d in self.dirs_to_remove:
if exists(d):
rmtree(d)
def tearDown(self):
remove_files(set(self.files_to_remove))
# remove directories last, so we don't get errors
# trying to remove files which may be in the directories
for d in self.dirs_to_remove:
if exists(d):
rmtree(d)
def test_plot_heatmap(self):
plot_heatmap(self.otu_table,
self.otu_table.ObservationIds,
self.otu_table.SampleIds,
filename=self.tmp_heatmap_fpath)
self.assertEqual(exists(self.tmp_heatmap_fpath), True)
remove_files(set([self.tmp_heatmap_fpath]))
def test_mothur_supported_version(self):
"""mothur is in path and version is supported """
acceptable_version = (1, 25, 0)
self.assertTrue(
which('mothur'),
"mothur not found. This may or may not be a problem depending on "
+ "which components of QIIME you plan to use.")
# mothur creates a log file in cwd, so create a tmp and cd there first
log_file = join(get_qiime_temp_dir(), 'mothur.log')
command = "mothur \"#set.logfile(name=%s)\" | grep '^mothur v'" % log_file
stdout, stderr, exit_Status = qiime_system_call(command)
# remove log file
remove_files([log_file], error_on_missing=False)
version_string = stdout.strip().split(' ')[1].strip('v.')
try:
version = tuple(map(int, version_string.split('.')))
pass_test = version == acceptable_version
except ValueError:
pass_test = False
version_string = stdout
self.assertTrue(
pass_test,
"Unsupported mothur version. %s is required, but running %s." %
('.'.join(map(str, acceptable_version)), version_string))
def tearDown(self):
if self._files_to_remove:
remove_files(self._files_to_remove)
if exists(self.output_dir):
rmtree(self.output_dir)
if exists(self.input_dir):
rmtree(self.input_dir)
def tearDown(self):
""" """
disable_timeout()
remove_files(self.files_to_remove, error_on_missing=False)
# remove directories last, so we don't get errors
# trying to remove files which may be in the directories
for d in self.dirs_to_remove:
if exists(d):
rmtree(d)
def tearDown(self):
"""Removes temporary directories and files."""
remove_files(self.files_to_remove)
# Remove directories last, so we don't get errors trying to remove
# files which may be in the directories.
for d in self.dirs_to_remove:
if exists(d):
rmtree(d)
def tearDown(self):
"""Clean up tmp files."""
remove_files(self.files_to_remove, False)
if self.tmpdir:
rmtree(self.tmpdir)
# clean up the file from init_flowgram_file
if (hasattr(self, "tmp_filename") and exists(self.tmp_filename)):
remove(self.tmp_filename)
def tearDown(self):
"""Removes temporary directories and files."""
remove_files(self.files_to_remove)
# Remove directories last, so we don't get errors trying to remove
# files which may be in the directories.
for d in self.dirs_to_remove:
if exists(d):
rmtree(d)
def tearDown(self):
""" """
disable_timeout()
remove_files(self.files_to_remove, error_on_missing=False)
# remove directories last, so we don't get errors
# trying to remove files which may be in the directories
for d in self.dirs_to_remove:
if exists(d):
rmtree(d)
def tearDown(self):
""" """
disable_timeout()
# reset sys.stderr
sys.stderr = self.saved_stderr
remove_files(self.files_to_remove)
# remove directories last, so we don't get errors
# trying to remove files which may be in the directories
for d in self.dirs_to_remove:
if exists(d):
rmtree(d)
def tearDown(self):
disable_timeout()
# reset sys.stderr
sys.stderr = self.saved_stderr
remove_files(self.files_to_remove)
# remove directories last, so we don't get errors
# trying to remove files which may be in the directories
for d in self.dirs_to_remove:
if exists(d):
rmtree(d)
def remove_intermediate_files(self):
"""Remove all intermediate files."""
# tmp files are written in the current dir,
# app controller always jumps into dir specified via exec_dir
# Note: blast intermediates are not removed
exec_dir = str(self.Parameters['--exec_dir'].Value)
inp_file_name = str(self.Parameters['--query_NAST'].Value)
exec_dir = exec_dir.rstrip('"')
exec_dir = exec_dir.lstrip('"')
inp_file_name = inp_file_name.rstrip('"')
inp_file_name = inp_file_name.lstrip('"')
tmp_suffixes = [".CPS", ".CPS.CPC", ".CPS_RENAST", ".CPS_RENAST.cidx",
".CPS.CPC.wTaxons", ".cidx"]
cs_tmp_files = [
exec_dir +
'/' +
inp_file_name +
x for x in tmp_suffixes]
remove_files(cs_tmp_files, error_on_missing=False)
db_param = self.Parameters['--db_NAST']
if db_param.isOn():
nast_db_name = str(db_param.Value)
nast_db_name = nast_db_name.rstrip('"')
nast_db_name = nast_db_name.lstrip('"')
# Better do not remove this file since other ChimeraSlayer
# instances running on the same ref set might use this file
# Should be rather deleted in the calling function
# remove_files([nast_db_name + ".cidx"],
# error_on_missing=False)
fasta_param = self.Parameters['--db_FASTA']
if fasta_param.isOn():
fasta_name = str(fasta_param.Value)
fasta_name = fasta_name.rstrip('"')
fasta_name = fasta_name.lstrip('"')
blast_db_files = [
fasta_name +
x for x in [
".nsq",
".nin",
".nhr",
".cidx"]]
remove_files(blast_db_files, error_on_missing=False)
def tearDown(self):
"""Clean up tmp files."""
# turn off the alarm
signal.alarm(0)
remove_files(self.files_to_remove, False)
if self.server_socket:
self.server_socket.close()
# give clients time to clean up
sleep(1)
if exists(self.tmp_dir):
try:
rmdir(self.tmp_dir)
except OSError:
# give clients some more time, fail if still error
sleep(5)
rmdir(self.tmp_dir)
def tearDown(self):
"""Clean up tmp files."""
# turn off the alarm
signal.alarm(0)
remove_files(self.files_to_remove, False)
if self.server_socket:
self.server_socket.close()
# give clients time to clean up
sleep(1)
if exists(self.tmp_dir):
try:
rmdir(self.tmp_dir)
except OSError:
# give clients some more time, fail if still error
sleep(5)
rmdir(self.tmp_dir)
def test_remove_files(self):
"""Remove files functions as expected """
# create list of temp file paths
test_fds = [NamedTemporaryFile(delete=False) for i in range(5)]
test_filepaths = [element.name for element in test_fds]
# should work just fine
remove_files(test_filepaths)
# check that an error is raised on trying to remove the files...
self.assertRaises(OSError, remove_files, test_filepaths)
# touch one of the filepaths so it exists
extra_file = NamedTemporaryFile(delete=False).name
test_filepaths.append(extra_file)
# no error is raised on trying to remove the files
# (although 5 don't exist)...
remove_files(test_filepaths, error_on_missing=False)
# ... and the existing file was removed
self.assertFalse(exists(extra_file))
# try to remove them with remove_files and verify that an IOError is
# raises
self.assertRaises(OSError, remove_files, test_filepaths)
# now get no error when error_on_missing=False
remove_files(test_filepaths, error_on_missing=False)
def test_remove_files(self):
"""Remove files functions as expected """
# create list of temp file paths
test_fds = [NamedTemporaryFile(delete=False) for i in range(5)]
test_filepaths = [element.name for element in test_fds]
# should work just fine
remove_files(test_filepaths)
# check that an error is raised on trying to remove the files...
self.assertRaises(OSError, remove_files, test_filepaths)
# touch one of the filepaths so it exists
extra_file = NamedTemporaryFile(delete=False).name
test_filepaths.append(extra_file)
# no error is raised on trying to remove the files
# (although 5 don't exist)...
remove_files(test_filepaths, error_on_missing=False)
# ... and the existing file was removed
self.assertFalse(exists(extra_file))
# try to remove them with remove_files and verify that an IOError is
# raises
self.assertRaises(OSError, remove_files, test_filepaths)
# now get no error when error_on_missing=False
remove_files(test_filepaths, error_on_missing=False)
def test_build_blast_db_from_fasta_path_aln(self):
"""build_blast_db_from_fasta_path works with alignment as input
"""
blast_db, db_files = build_blast_db_from_fasta_path(self.in_aln1_fp)
self.assertEqual(blast_db,self.in_aln1_fp)
expected_db_files = set([blast_db + ext\
for ext in ['.nhr','.nin','.nsq','.nsd','.nsi','.log']])
self.assertEqual(set(db_files),expected_db_files)
# result returned when blasting against new db
self.assertEqual(\
len(blastn(self.test_seq,blast_db=blast_db,e_value=0.0)),1)
# Make sure all db_files exist
for fp in db_files:
self.assertTrue(exists(fp))
# Remove all db_files exist
remove_files(db_files)
# Make sure nothing weird happened in the remove
for fp in db_files:
self.assertFalse(exists(fp))
def test_compute_seqs_per_file(self):
"""compute_seqs_per_file functions as expected
"""
fd, temp_fasta_fp = mkstemp(prefix='QiimeScriptUtilTests',
suffix='.fasta')
close(fd)
temp_fasta = ['>seq', 'AAACCCCAAATTGG'] * 25
open(temp_fasta_fp, 'w').write('\n'.join(temp_fasta))
actual_25 = self.pw._compute_seqs_per_file(temp_fasta_fp, 25)
actual_2 = self.pw._compute_seqs_per_file(temp_fasta_fp, 2)
actual_10 = self.pw._compute_seqs_per_file(temp_fasta_fp, 10)
actual_5 = self.pw._compute_seqs_per_file(temp_fasta_fp, 5)
actual_40 = self.pw._compute_seqs_per_file(temp_fasta_fp, 40)
remove_files([temp_fasta_fp])
self.assertEqual(actual_25, 1)
self.assertEqual(actual_2, 13)
self.assertEqual(actual_10, 3)
self.assertEqual(actual_5, 5)
self.assertEqual(actual_40, 1)
def test_compute_seqs_per_file(self):
"""compute_seqs_per_file functions as expected
"""
fd, temp_fasta_fp = mkstemp(prefix='QiimeScriptUtilTests',
suffix='.fasta')
close(fd)
temp_fasta = ['>seq', 'AAACCCCAAATTGG'] * 25
open(temp_fasta_fp, 'w').write('\n'.join(temp_fasta))
actual_25 = self.pw._compute_seqs_per_file(temp_fasta_fp, 25)
actual_2 = self.pw._compute_seqs_per_file(temp_fasta_fp, 2)
actual_10 = self.pw._compute_seqs_per_file(temp_fasta_fp, 10)
actual_5 = self.pw._compute_seqs_per_file(temp_fasta_fp, 5)
actual_40 = self.pw._compute_seqs_per_file(temp_fasta_fp, 40)
remove_files([temp_fasta_fp])
self.assertEqual(actual_25, 1)
self.assertEqual(actual_2, 13)
self.assertEqual(actual_10, 3)
self.assertEqual(actual_5, 5)
self.assertEqual(actual_40, 1)
def test_build_blast_db_from_seqs(self):
"""build_blast_db_from_seqs convenience function works as expected
"""
blast_db, db_files = build_blast_db_from_seqs(self.in_seqs1,output_dir='/tmp')
self.assertTrue(blast_db.startswith('/tmp/Blast_tmp_db'))
self.assertTrue(blast_db.endswith('.fasta'))
expected_db_files = set([blast_db + ext\
for ext in ['.nhr','.nin','.nsq','.nsd','.nsi','.log']])
self.assertEqual(set(db_files),expected_db_files)
# result returned when blasting against new db
self.assertEqual(\
len(blastn(self.test_seq,blast_db=blast_db)),1)
# Make sure all db_files exist
for fp in db_files:
self.assertTrue(exists(fp))
# Remove all db_files exist
remove_files(db_files)
# Make sure nothing weird happened in the remove
for fp in db_files:
self.assertFalse(exists(fp))
def test_build_blast_db_from_fasta_path(self):
"""build_blast_db_from_fasta_path convenience function works as expected
"""
blast_db, db_files = \
build_blast_db_from_fasta_path(self.in_seqs1_fp)
self.assertEqual(blast_db,self.in_seqs1_fp)
expected_db_files = set([self.in_seqs1_fp + ext\
for ext in ['.nhr','.nin','.nsq','.nsd','.nsi','.log']])
self.assertEqual(set(db_files),expected_db_files)
# result returned when blasting against new db
self.assertEqual(\
len(blastn(self.test_seq,blast_db=blast_db)),1)
# Make sure all db_files exist
for fp in db_files:
self.assertTrue(exists(fp))
# Remove all db_files exist
remove_files(db_files)
# Make sure nothing weird happened in the remove
for fp in db_files:
self.assertFalse(exists(fp))
def test_build_blast_db_from_fasta_file(self):
"""build_blast_db_from_fasta_file works with open files as input
"""
blast_db, db_files = \
build_blast_db_from_fasta_file(open(self.in_aln1_fp),output_dir='/tmp/')
self.assertTrue(blast_db.startswith('/tmp/BLAST_temp_db'))
self.assertTrue(blast_db.endswith('.fasta'))
expected_db_files = set([blast_db] + [blast_db + ext\
for ext in ['.nhr','.nin','.nsq','.nsd','.nsi','.log']])
self.assertEqual(set(db_files),expected_db_files)
# result returned when blasting against new db
self.assertEqual(\
len(blastn(self.test_seq,blast_db=blast_db,e_value=0.0)),1)
# Make sure all db_files exist
for fp in db_files:
self.assertTrue(exists(fp))
# Remove all db_files exist
remove_files(db_files)
# Make sure nothing weird happened in the remove
for fp in db_files:
self.assertFalse(exists(fp))
def test_mothur_supported_version(self):
"""mothur is in path and version is supported """
acceptable_version = (1, 25, 0)
self.assertTrue(which('mothur'),
"mothur not found. This may or may not be a problem depending on " +
"which components of QIIME you plan to use.")
# mothur creates a log file in cwd, so create a tmp and cd there first
log_file = join(get_qiime_temp_dir(), 'mothur.log')
command = "mothur \"#set.logfile(name=%s)\" | grep '^mothur v'" % log_file
stdout, stderr, exit_Status = qiime_system_call(command)
# remove log file
remove_files([log_file], error_on_missing=False)
version_string = stdout.strip().split(' ')[1].strip('v.')
try:
version = tuple(map(int, version_string.split('.')))
pass_test = version == acceptable_version
except ValueError:
pass_test = False
version_string = stdout
self.assertTrue(pass_test,
"Unsupported mothur version. %s is required, but running %s."
% ('.'.join(map(str, acceptable_version)), version_string))
def usearch61_chimera_check(input_seqs_fp,
output_dir,
reference_seqs_fp=None,
suppress_usearch61_intermediates=False,
suppress_usearch61_ref=False,
suppress_usearch61_denovo=False,
split_by_sampleid=False,
non_chimeras_retention="union",
usearch61_minh=0.28,
usearch61_xn=8.0,
usearch61_dn=1.4,
usearch61_mindiffs=3,
usearch61_mindiv=0.8,
usearch61_abundance_skew=2.0,
percent_id_usearch61=0.97,
minlen=64,
word_length=8,
max_accepts=1,
max_rejects=8,
verbose=False,
threads=1.0,
HALT_EXEC=False):
""" Main convenience function for usearch61 chimera checking
input_seqs_fp: filepath of input fasta file.
output_dir: output directory
reference_seqs_fp: fasta filepath for reference chimera detection.
suppress_usearch61_intermediates: Suppress retention of .uc and log files.
suppress_usearch61_ref: Suppress usearch61 reference chimera detection.
suppress_usearch61_denovo: Suppress usearch61 de novo chimera detection.
split_by_sampleid: Split by sample ID for de novo chimera detection.
non_chimeras_retention: Set to "union" or "intersection" to retain
non-chimeras between de novo and reference based results.
usearch61_minh: Minimum score (h) to be classified as chimera.
Increasing this value tends to the number of false positives (and also
sensitivity).
usearch61_xn: Weight of "no" vote. Increasing this value tends to the
number of false positives (and also sensitivity).
usearch61_dn: Pseudo-count prior for "no" votes. (n). Increasing this
value tends to the number of false positives (and also sensitivity).
usearch61_mindiffs: Minimum number of diffs in a segment. Increasing this
value tends to reduce the number of false positives while reducing
sensitivity to very low-divergence chimeras.
usearch61_mindiv: Minimum divergence, i.e. 100% - identity between the
query and closest reference database sequence. Expressed as a percentage,
so the default is 0.8%, which allows chimeras that are up to 99.2% similar
to a reference sequence.
usearch61_abundance_skew: abundance skew for de novo chimera comparisons.
percent_id_usearch61: identity to cluster sequences at
minlen: minimum sequence length for use with usearch61
word_length: length of nucleotide 'words' for usearch61
max_accepts: max number of accepts for hits with usearch61
max_rejects: max number of rejects for usearch61, increasing allows more
sensitivity at a cost of speed
threads: Specify number of threads used per core per CPU
HALT_EXEC=application controller option to halt execution and print command
"""
"""
Need to cluster sequences de novo first to get 1. abundance information
and 2 consensus sequence for each cluster. Using dereplication followed
by clustering does not appear to automatically update complete cluster
size, will directly cluster raw seqs with the small_mem clustering option.
This means without additional parsing steps to recalculate
actual cluster sizes, the sizeorder option can't be used for de novo
clustering and downstream chimera detection."""
files_to_remove = []
# Get absolute paths to avoid issues with calling usearch
input_seqs_fp = abspath(input_seqs_fp)
output_dir = abspath(output_dir)
if reference_seqs_fp:
reference_seqs_fp = abspath(reference_seqs_fp)
log_fp = join(output_dir, "identify_chimeric_seqs.log")
chimeras_fp = join(output_dir, "chimeras.txt")
non_chimeras_fp = join(output_dir, "non_chimeras.txt")
non_chimeras = []
chimeras = []
log_lines = {'denovo_chimeras': 0,
'denovo_non_chimeras': 0,
'ref_chimeras': 0,
'ref_non_chimeras': 0}
if split_by_sampleid:
if verbose:
print "Splitting fasta according to SampleID..."
full_seqs = open(input_seqs_fp, "U")
sep_fastas =\
split_fasta_on_sample_ids_to_files(parse_fasta(full_seqs),
output_dir)
full_seqs.close()
if suppress_usearch61_intermediates:
files_to_remove += sep_fastas
for curr_fasta in sep_fastas:
curr_chimeras, curr_non_chimeras, files_to_remove, log_lines =\
identify_chimeras_usearch61(curr_fasta, output_dir,
reference_seqs_fp, suppress_usearch61_intermediates,
suppress_usearch61_ref, suppress_usearch61_denovo,
non_chimeras_retention, usearch61_minh, usearch61_xn,
usearch61_dn, usearch61_mindiffs, usearch61_mindiv,
usearch61_abundance_skew, percent_id_usearch61, minlen,
word_length, max_accepts, max_rejects, files_to_remove, HALT_EXEC,
log_lines, verbose, threads)
chimeras += curr_chimeras
non_chimeras += curr_non_chimeras
else:
chimeras, non_chimeras, files_to_remove, log_lines =\
identify_chimeras_usearch61(input_seqs_fp, output_dir,
reference_seqs_fp, suppress_usearch61_intermediates,
suppress_usearch61_ref, suppress_usearch61_denovo,
non_chimeras_retention, usearch61_minh, usearch61_xn,
usearch61_dn, usearch61_mindiffs, usearch61_mindiv,
usearch61_abundance_skew, percent_id_usearch61, minlen,
word_length, max_accepts, max_rejects, files_to_remove, HALT_EXEC,
log_lines, verbose, threads)
# write log, non chimeras, chimeras.
write_usearch61_log(log_fp, input_seqs_fp, output_dir,
reference_seqs_fp, suppress_usearch61_intermediates,
suppress_usearch61_ref, suppress_usearch61_denovo,
split_by_sampleid, non_chimeras_retention, usearch61_minh,
usearch61_xn, usearch61_dn, usearch61_mindiffs, usearch61_mindiv,
usearch61_abundance_skew, percent_id_usearch61, minlen,
word_length, max_accepts, max_rejects, HALT_EXEC, log_lines)
chimeras_f = open(chimeras_fp, "w")
non_chimeras_f = open(non_chimeras_fp, "w")
for curr_chimera in chimeras:
chimeras_f.write("%s\n" % curr_chimera)
for curr_non_chimera in non_chimeras:
non_chimeras_f.write("%s\n" % curr_non_chimera)
chimeras_f.close()
non_chimeras_f.close()
remove_files(files_to_remove)
def tearDown(self):
remove_files(self.files_to_remove)
def tearDown(self):
for dir in self.dirs_to_remove:
if exists(dir):
rmdir(dir)
remove_files(self.files_to_remove)
def tearDown(self):
remove_files(self.files_to_remove)
for folder in self.folders_to_remove:
shutil.rmtree(folder)
def align_and_tree(repset_fasta_fp,
output_dir,
command_handler,
params,
qiime_config,
parallel=False,
logger=None,
status_update_callback=print_to_stdout):
input_dir, input_filename = split(repset_fasta_fp)
input_basename, input_ext = splitext(input_filename)
commands = []
if logger is None:
log_fp = generate_log_fp(output_dir)
logger = WorkflowLogger(log_fp,
params=params,
qiime_config=qiime_config)
close_logger_on_success = True
else:
close_logger_on_success = False
# Prep the pynast alignment command
alignment_method = 'pynast'
pynast_dir = '%s/%s_aligned_seqs' % (output_dir, alignment_method)
aln_fp = '%s/%s_aligned.fasta' % (pynast_dir, input_basename)
failures_fp = '%s/%s_failures.fasta' % (pynast_dir, input_basename)
if exists(pynast_dir):
rmtree(pynast_dir)
if parallel:
# Grab the parallel-specific parameters
try:
params_str = get_params_str(params['parallel'])
except KeyError:
params_str = ''
# Grab the OTU picker parameters
try:
# Want to find a cleaner strategy for this: the parallel script
# is method-specific, so doesn't take a --alignment_method
# option. This works for now though.
d = params['align_seqs'].copy()
if 'alignment_method' in d:
del d['alignment_method']
params_str += ' %s' % get_params_str(d)
except KeyError:
pass
# Build the parallel pynast alignment command
align_seqs_cmd = 'parallel_align_seqs_pynast.py -i %s -o %s -T %s' %\
(repset_fasta_fp, pynast_dir, params_str)
else:
try:
params_str = get_params_str(params['align_seqs'])
except KeyError:
params_str = ''
# Build the pynast alignment command
align_seqs_cmd = 'align_seqs.py -i %s -o %s %s' %\
(repset_fasta_fp, pynast_dir, params_str)
commands.append([('Align sequences', align_seqs_cmd)])
# Prep the alignment filtering command
filtered_aln_fp = '%s/%s_aligned_pfiltered.fasta' %\
(pynast_dir, input_basename)
try:
params_str = get_params_str(params['filter_alignment'])
except KeyError:
params_str = ''
# Build the alignment filtering command
filter_alignment_cmd = 'filter_alignment.py -o %s -i %s %s' %\
(pynast_dir, aln_fp, params_str)
commands.append([('Filter alignment', filter_alignment_cmd)])
# Prep the tree building command
tree_fp = '%s/rep_set.tre' % output_dir
try:
params_str = get_params_str(params['make_phylogeny'])
except KeyError:
params_str = ''
# Build the tree building command
make_phylogeny_cmd = 'make_phylogeny.py -i %s -o %s %s' %\
(filtered_aln_fp, tree_fp, params_str)
commands.append([('Build phylogenetic tree', make_phylogeny_cmd)])
if exists(tree_fp):
remove_files([tree_fp])
# Call the command handler on the list of commands
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=close_logger_on_success)
return failures_fp
def iterative_pick_subsampled_open_reference_otus(
input_fps,
refseqs_fp,
output_dir,
percent_subsample,
new_ref_set_id,
command_handler,
params,
qiime_config,
prefilter_refseqs_fp=None,
prefilter_percent_id=None,
min_otu_size=2,
run_assign_tax=True,
run_align_and_tree=True,
step1_otu_map_fp=None,
step1_failures_fasta_fp=None,
parallel=False,
suppress_step4=False,
logger=None,
suppress_md5=False,
denovo_otu_picking_method='uclust',
reference_otu_picking_method='uclust_ref',
status_update_callback=print_to_stdout):
""" Call the pick_subsampled_open_reference_otus workflow on multiple inputs
and handle processing of the results.
"""
create_dir(output_dir)
commands = []
if logger is None:
logger = WorkflowLogger(generate_log_fp(output_dir),
params=params,
qiime_config=qiime_config)
close_logger_on_success = True
else:
close_logger_on_success = False
# if the user has not passed a different reference collection for the pre-filter,
# used the input refseqs_fp for all iterations. we want to pre-filter all data against
# the input data as lower percent identity searches with uclust can be slow, so we
# want the reference collection to stay at a reasonable size.
if prefilter_refseqs_fp is None:
prefilter_refseqs_fp = refseqs_fp
otu_table_fps = []
repset_fasta_fps = []
for i, input_fp in enumerate(input_fps):
iteration_output_dir = '%s/%d/' % (output_dir, i)
if iteration_output_exists(iteration_output_dir, min_otu_size):
# if the output from an iteration already exists, skip that
# iteration (useful for continuing failed runs)
log_input_md5s(logger, [input_fp, refseqs_fp])
logger.write('Iteration %d (input file: %s) output data already exists. '
'Skipping and moving to next.\n\n' % (i, input_fp))
else:
pick_subsampled_open_reference_otus(input_fp=input_fp,
refseqs_fp=refseqs_fp,
output_dir=iteration_output_dir,
percent_subsample=percent_subsample,
new_ref_set_id='.'.join(
[new_ref_set_id, str(i)]),
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
run_assign_tax=False,
run_align_and_tree=False,
prefilter_refseqs_fp=prefilter_refseqs_fp,
prefilter_percent_id=prefilter_percent_id,
min_otu_size=min_otu_size,
step1_otu_map_fp=step1_otu_map_fp,
step1_failures_fasta_fp=step1_failures_fasta_fp,
parallel=parallel,
suppress_step4=suppress_step4,
logger=logger,
suppress_md5=suppress_md5,
suppress_index_page=True,
denovo_otu_picking_method=denovo_otu_picking_method,
reference_otu_picking_method=reference_otu_picking_method,
status_update_callback=status_update_callback)
# perform post-iteration file shuffling whether the previous iteration's
# data previously existed or was just computed.
# step1 otu map and failures can only be used for the first iteration
# as subsequent iterations need to use updated refseqs files
step1_otu_map_fp = step1_failures_fasta_fp = None
new_refseqs_fp = '%s/new_refseqs.fna' % iteration_output_dir
refseqs_fp = new_refseqs_fp
otu_table_fps.append(
'%s/otu_table_mc%d.biom' %
(iteration_output_dir, min_otu_size))
repset_fasta_fps.append('%s/rep_set.fna' % iteration_output_dir)
# Merge OTU tables - check for existence first as this step has historically
# been a frequent failure, so is sometimes run manually in failed runs.
otu_table_fp = '%s/otu_table_mc%d.biom' % (output_dir, min_otu_size)
if not (exists(otu_table_fp) and getsize(otu_table_fp) > 0):
merge_cmd = 'merge_otu_tables.py -i %s -o %s' %\
(','.join(otu_table_fps), otu_table_fp)
commands.append([("Merge OTU tables", merge_cmd)])
# Build master rep set
final_repset_fp = '%s/rep_set.fna' % output_dir
final_repset_from_iteration_repsets_fps(repset_fasta_fps, final_repset_fp)
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
# initialize output file names - these differ based on what combination of
# taxonomy assignment and alignment/tree building is happening.
if run_assign_tax and run_align_and_tree:
tax_input_otu_table_fp = otu_table_fp
otu_table_w_tax_fp = \
'%s/otu_table_mc%d_w_tax.biom' % (output_dir, min_otu_size)
align_and_tree_input_otu_table = otu_table_w_tax_fp
pynast_failure_filtered_otu_table_fp = \
'%s/otu_table_mc%d_w_tax_no_pynast_failures.biom' % (output_dir,
min_otu_size)
elif run_assign_tax:
tax_input_otu_table_fp = otu_table_fp
otu_table_w_tax_fp = \
'%s/otu_table_mc%d_w_tax.biom' % (output_dir, min_otu_size)
elif run_align_and_tree:
align_and_tree_input_otu_table = otu_table_fp
pynast_failure_filtered_otu_table_fp = \
'%s/otu_table_mc%d_no_pynast_failures.biom' % (output_dir,
min_otu_size)
if run_assign_tax:
if exists(otu_table_w_tax_fp) and getsize(otu_table_w_tax_fp) > 0:
logger.write(
"Final output file exists (%s). Will not rebuild." %
otu_table_w_tax_fp)
else:
# remove files from partially completed runs
remove_files([otu_table_w_tax_fp], error_on_missing=False)
taxonomy_fp = assign_tax(
repset_fasta_fp=final_repset_fp,
output_dir=output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
parallel=parallel,
logger=logger,
status_update_callback=status_update_callback)
# Add taxa to otu table
add_metadata_cmd = 'biom add-metadata -i %s --observation-metadata-fp %s -o %s --sc-separated taxonomy --observation-header OTUID,taxonomy' %\
(tax_input_otu_table_fp, taxonomy_fp, otu_table_w_tax_fp)
commands.append([("Add taxa to OTU table", add_metadata_cmd)])
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
if run_align_and_tree:
if exists(pynast_failure_filtered_otu_table_fp) and\
getsize(pynast_failure_filtered_otu_table_fp) > 0:
logger.write("Final output file exists (%s). Will not rebuild." %
pynast_failure_filtered_otu_table_fp)
else:
# remove files from partially completed runs
remove_files([pynast_failure_filtered_otu_table_fp],
error_on_missing=False)
pynast_failures_fp = align_and_tree(
repset_fasta_fp=final_repset_fp,
output_dir=output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
parallel=parallel,
logger=logger,
status_update_callback=status_update_callback)
# Build OTU table without PyNAST failures
with biom_open(align_and_tree_input_otu_table) as biom_file:
table = Table.from_hdf5(biom_file)
filtered_otu_table = filter_otus_from_otu_table(table,
get_seq_ids_from_fasta_file(open(pynast_failures_fp, 'U')),
0, inf, 0, inf, negate_ids_to_keep=True)
write_biom_table(filtered_otu_table,
pynast_failure_filtered_otu_table_fp)
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
logger.close()
def align_and_tree(repset_fasta_fp,
output_dir,
command_handler,
params,
qiime_config,
parallel=False,
logger=None,
status_update_callback=print_to_stdout):
input_dir, input_filename = split(repset_fasta_fp)
input_basename, input_ext = splitext(input_filename)
commands = []
if logger is None:
log_fp = generate_log_fp(output_dir)
logger = WorkflowLogger(log_fp,
params=params,
qiime_config=qiime_config)
close_logger_on_success = True
else:
close_logger_on_success = False
# Prep the pynast alignment command
alignment_method = 'pynast'
pynast_dir = '%s/%s_aligned_seqs' % (output_dir, alignment_method)
aln_fp = '%s/%s_aligned.fasta' % (pynast_dir, input_basename)
failures_fp = '%s/%s_failures.fasta' % (pynast_dir, input_basename)
if exists(pynast_dir):
rmtree(pynast_dir)
if parallel:
# Grab the parallel-specific parameters
try:
params_str = get_params_str(params['parallel'])
except KeyError:
params_str = ''
# Grab the OTU picker parameters
try:
# Want to find a cleaner strategy for this: the parallel script
# is method-specific, so doesn't take a --alignment_method
# option. This works for now though.
d = params['align_seqs'].copy()
if 'alignment_method' in d:
del d['alignment_method']
params_str += ' %s' % get_params_str(d)
except KeyError:
pass
# Build the parallel pynast alignment command
align_seqs_cmd = 'parallel_align_seqs_pynast.py -i %s -o %s -T %s' %\
(repset_fasta_fp, pynast_dir, params_str)
else:
try:
params_str = get_params_str(params['align_seqs'])
except KeyError:
params_str = ''
# Build the pynast alignment command
align_seqs_cmd = 'align_seqs.py -i %s -o %s %s' %\
(repset_fasta_fp, pynast_dir, params_str)
commands.append([('Align sequences', align_seqs_cmd)])
# Prep the alignment filtering command
filtered_aln_fp = '%s/%s_aligned_pfiltered.fasta' %\
(pynast_dir, input_basename)
try:
params_str = get_params_str(params['filter_alignment'])
except KeyError:
params_str = ''
# Build the alignment filtering command
filter_alignment_cmd = 'filter_alignment.py -o %s -i %s %s' %\
(pynast_dir, aln_fp, params_str)
commands.append([('Filter alignment', filter_alignment_cmd)])
# Prep the tree building command
tree_fp = '%s/rep_set.tre' % output_dir
try:
params_str = get_params_str(params['make_phylogeny'])
except KeyError:
params_str = ''
# Build the tree building command
make_phylogeny_cmd = 'make_phylogeny.py -i %s -o %s %s' %\
(filtered_aln_fp, tree_fp, params_str)
commands.append([('Build phylogenetic tree', make_phylogeny_cmd)])
if exists(tree_fp):
remove_files([tree_fp])
# Call the command handler on the list of commands
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=close_logger_on_success)
return failures_fp
def tearDown(self):
remove_files(self.files_to_remove)
if self._dirs_to_remove:
for i in self._dirs_to_remove:
rmtree(i)
def tearDown(self):
remove_files(set(self._paths_to_clean_up),error_on_missing=False)
def main():
option_parser, options, args = parse_command_line_parameters(**script_info)
DEBUG = options.verbose
check_options(option_parser, options)
start_time = time()
option_lines = format_options_as_lines(options)
if DEBUG:
print FORMAT_BAR
print "Running with options:"
for line in sorted(option_lines):
print line
print FORMAT_BAR
# because the blast app controller uses absolute paths, make sure subject
# db path is fully specified
subject_db = options.subjectdb
if not subject_db.startswith('/'):
subject_db = join(getcwd(), subject_db)
if not options.no_format_db:
# initialize object
inpath = FilePath(abspath(options.subjectdb))
subject_dir, subj_file = split(inpath)
fdb = FormatDb(WorkingDir=subject_dir)
# Currently we do not support protein blasts, but
# this would be easy to add in the future...
fdb.Parameters['-p'].on('F')
# Create indices for record lookup
fdb.Parameters['-o'].on('T')
# Set input database
fdb.Parameters['-i'].on(subject_db)
formatdb_cmd = fdb.BaseCommand
if DEBUG:
print "Formatting db with command: %s" % formatdb_cmd
app_result = fdb(subject_db)
formatdb_filepaths = []
for v in app_result.values():
try:
formatdb_filepaths.append(v.name)
except AttributeError:
# not a file object, so no path to return
pass
db_format_time = time() - start_time
if DEBUG:
print "Formatting subject db took: %2.f seconds" % db_format_time
print "formatdb log file written to: %s" % app_result['log']
print FORMAT_BAR
else:
db_format_time = time() - start_time
formatdb_cmd = "None (formatdb not called)"
# Check that User-Supplied subjectdb is valid
db_ext = [".nhr", ".nin", ".nsd", ".nsi", ".nsq"]
formatdb_filepaths = [subject_db + ext for ext in db_ext]
if DEBUG:
print "Checking that pre-existing formatdb files exist and can be read."
print "Files to be checked:"
for fp in formatdb_filepaths:
print fp
print FORMAT_BAR
try:
formatdb_files = [open(db_f, "U") for db_f in formatdb_filepaths]
[f.close() for f in formatdb_files]
except IOError:
if DEBUG:
print "Cannot open user-supplied database file:", db_f
option_parser.error(
"""Problem with -d and --no_format_db option combination: Cannot open the following user-supplied database file: %s. Consider running without --no_format_db to let formatdb generate these required files"""
% db_f)
if DEBUG:
print "OK: BLAST Database files exist and can be read."
print FORMAT_BAR
# Perform BLAST search
blast_results, hit_ids, removed_hit_ids = find_homologs(
options.querydb,
subject_db,
options.e_value,
options.max_hits,
options.working_dir,
options.blastmatroot,
options.wordsize,
options.percent_aligned,
DEBUG=DEBUG)
blast_time = (time() - start_time) - db_format_time
if DEBUG:
print "BLAST search took: %2.f minute(s)" % (blast_time / 60.0)
print FORMAT_BAR
# Create output folder
outputdir = options.outputdir
try:
makedirs(outputdir)
except OSError:
pass
# Record raw blast results
raw_blast_results_path = join(outputdir, "raw_blast_results.txt")
f = open(raw_blast_results_path, 'w')
f.writelines(blast_results)
f.close()
# Record excluded seqs
excluded_seqs_path = join(outputdir, "matching.fna")
ids_to_seq_file(hit_ids, options.querydb, excluded_seqs_path, "")
# Record included (screened) seqs
included_seqs_path = join(outputdir, "non-matching.fna")
all_ids = ids_from_fasta_lines(open(options.querydb))
included_ids = set(all_ids) - hit_ids
ids_to_seq_file(included_ids, options.querydb, included_seqs_path, "")
log_lines = compose_logfile_lines(start_time, db_format_time, blast_time,
option_lines, formatdb_cmd,
blast_results, options, all_ids, hit_ids,
removed_hit_ids, included_ids, DEBUG)
log_path = join(outputdir, "sequence_exclusion.log")
if DEBUG:
print "Writing summary to: %s" % log_path
f = open(log_path, 'w')
f.writelines(log_lines)
f.close()
if not options.no_clean:
if DEBUG:
print FORMAT_BAR
print "| Cleanup |"
print FORMAT_BAR
if not options.no_format_db:
if options.verbose:
print "Cleaning up formatdb files:", formatdb_filepaths
remove_files(formatdb_filepaths)
else:
if options.verbose:
print "Formatdb not run...nothing to clean"
def iterative_pick_subsampled_open_reference_otus(
input_fps,
refseqs_fp,
output_dir,
percent_subsample,
new_ref_set_id,
command_handler,
params,
qiime_config,
prefilter_refseqs_fp=None,
prefilter_percent_id=0.60,
min_otu_size=2,
run_assign_tax=True,
run_align_and_tree=True,
step1_otu_map_fp=None,
step1_failures_fasta_fp=None,
parallel=False,
suppress_step4=False,
logger=None,
suppress_md5=False,
denovo_otu_picking_method='uclust',
reference_otu_picking_method='uclust_ref',
status_update_callback=print_to_stdout):
""" Call the pick_subsampled_open_reference_otus workflow on multiple inputs
and handle processing of the results.
"""
create_dir(output_dir)
commands = []
if logger is None:
logger = WorkflowLogger(generate_log_fp(output_dir),
params=params,
qiime_config=qiime_config)
close_logger_on_success = True
else:
close_logger_on_success = False
# if the user has not passed a different reference collection for the pre-filter,
# used the input refseqs_fp for all iterations. we want to pre-filter all data against
# the input data as lower percent identity searches with uclust can be slow, so we
# want the reference collection to stay at a reasonable size.
if prefilter_refseqs_fp is None:
prefilter_refseqs_fp = refseqs_fp
otu_table_fps = []
repset_fasta_fps = []
for i, input_fp in enumerate(input_fps):
iteration_output_dir = '%s/%d/' % (output_dir, i)
if iteration_output_exists(iteration_output_dir, min_otu_size):
# if the output from an iteration already exists, skip that
# iteration (useful for continuing failed runs)
log_input_md5s(logger, [input_fp, refseqs_fp])
logger.write(
'Iteration %d (input file: %s) output data already exists. '
'Skipping and moving to next.\n\n' % (i, input_fp))
else:
pick_subsampled_open_reference_otus(
input_fp=input_fp,
refseqs_fp=refseqs_fp,
output_dir=iteration_output_dir,
percent_subsample=percent_subsample,
new_ref_set_id='.'.join([new_ref_set_id,
str(i)]),
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
run_assign_tax=False,
run_align_and_tree=False,
prefilter_refseqs_fp=prefilter_refseqs_fp,
prefilter_percent_id=prefilter_percent_id,
min_otu_size=min_otu_size,
step1_otu_map_fp=step1_otu_map_fp,
step1_failures_fasta_fp=step1_failures_fasta_fp,
parallel=parallel,
suppress_step4=suppress_step4,
logger=logger,
suppress_md5=suppress_md5,
suppress_index_page=True,
denovo_otu_picking_method=denovo_otu_picking_method,
reference_otu_picking_method=reference_otu_picking_method,
status_update_callback=status_update_callback)
# perform post-iteration file shuffling whether the previous iteration's
# data previously existed or was just computed.
# step1 otu map and failures can only be used for the first iteration
# as subsequent iterations need to use updated refseqs files
step1_otu_map_fp = step1_failures_fasta_fp = None
new_refseqs_fp = '%s/new_refseqs.fna' % iteration_output_dir
refseqs_fp = new_refseqs_fp
otu_table_fps.append('%s/otu_table_mc%d.biom' %
(iteration_output_dir, min_otu_size))
repset_fasta_fps.append('%s/rep_set.fna' % iteration_output_dir)
# Merge OTU tables - check for existence first as this step has historically
# been a frequent failure, so is sometimes run manually in failed runs.
otu_table_fp = '%s/otu_table_mc%d.biom' % (output_dir, min_otu_size)
if not (exists(otu_table_fp) and getsize(otu_table_fp) > 0):
merge_cmd = 'merge_otu_tables.py -i %s -o %s' %\
(','.join(otu_table_fps), otu_table_fp)
commands.append([("Merge OTU tables", merge_cmd)])
# Build master rep set
final_repset_fp = '%s/rep_set.fna' % output_dir
final_repset_from_iteration_repsets_fps(repset_fasta_fps, final_repset_fp)
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
# initialize output file names - these differ based on what combination of
# taxonomy assignment and alignment/tree building is happening.
if run_assign_tax and run_align_and_tree:
tax_input_otu_table_fp = otu_table_fp
otu_table_w_tax_fp = \
'%s/otu_table_mc%d_w_tax.biom' % (output_dir, min_otu_size)
align_and_tree_input_otu_table = otu_table_w_tax_fp
pynast_failure_filtered_otu_table_fp = \
'%s/otu_table_mc%d_w_tax_no_pynast_failures.biom' % (output_dir,
min_otu_size)
elif run_assign_tax:
tax_input_otu_table_fp = otu_table_fp
otu_table_w_tax_fp = \
'%s/otu_table_mc%d_w_tax.biom' % (output_dir, min_otu_size)
elif run_align_and_tree:
align_and_tree_input_otu_table = otu_table_fp
pynast_failure_filtered_otu_table_fp = \
'%s/otu_table_mc%d_no_pynast_failures.biom' % (output_dir,
min_otu_size)
if run_assign_tax:
if exists(otu_table_w_tax_fp) and getsize(otu_table_w_tax_fp) > 0:
logger.write("Final output file exists (%s). Will not rebuild." %
otu_table_w_tax_fp)
else:
# remove files from partially completed runs
remove_files([otu_table_w_tax_fp], error_on_missing=False)
taxonomy_fp = assign_tax(
repset_fasta_fp=final_repset_fp,
output_dir=output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
parallel=parallel,
logger=logger,
status_update_callback=status_update_callback)
# Add taxa to otu table
add_metadata_cmd = 'biom add-metadata -i %s --observation-metadata-fp %s -o %s --sc-separated taxonomy --observation-header OTUID,taxonomy' %\
(tax_input_otu_table_fp, taxonomy_fp, otu_table_w_tax_fp)
commands.append([("Add taxa to OTU table", add_metadata_cmd)])
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
if run_align_and_tree:
if exists(pynast_failure_filtered_otu_table_fp) and\
getsize(pynast_failure_filtered_otu_table_fp) > 0:
logger.write("Final output file exists (%s). Will not rebuild." %
pynast_failure_filtered_otu_table_fp)
else:
# remove files from partially completed runs
remove_files([pynast_failure_filtered_otu_table_fp],
error_on_missing=False)
pynast_failures_fp = align_and_tree(
repset_fasta_fp=final_repset_fp,
output_dir=output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
parallel=parallel,
logger=logger,
status_update_callback=status_update_callback)
# Build OTU table without PyNAST failures
filtered_otu_table = filter_otus_from_otu_table(
parse_biom_table(open(align_and_tree_input_otu_table, 'U')),
get_seq_ids_from_fasta_file(open(pynast_failures_fp, 'U')),
0,
inf,
0,
inf,
negate_ids_to_keep=True)
otu_table_f = open(pynast_failure_filtered_otu_table_fp, 'w')
otu_table_f.write(format_biom_table(filtered_otu_table))
otu_table_f.close()
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
logger.close()
def tearDown(self):
for dir in self.dirs_to_remove:
if exists(dir):
rmdir(dir)
remove_files(self.files_to_remove)
def pick_subsampled_open_reference_otus(input_fp,
refseqs_fp,
output_dir,
percent_subsample,
new_ref_set_id,
command_handler,
params,
qiime_config,
prefilter_refseqs_fp=None,
run_assign_tax=True,
run_align_and_tree=True,
prefilter_percent_id=None,
min_otu_size=2,
step1_otu_map_fp=None,
step1_failures_fasta_fp=None,
parallel=False,
suppress_step4=False,
logger=None,
suppress_md5=False,
suppress_index_page=False,
denovo_otu_picking_method='uclust',
reference_otu_picking_method='uclust_ref',
status_update_callback=print_to_stdout):
""" Run the data preparation steps of Qiime
The steps performed by this function are:
- Pick reference OTUs against refseqs_fp
- Subsample the failures to n sequences.
- Pick OTUs de novo on the n failures.
- Pick representative sequences for the resulting OTUs.
- Pick reference OTUs on all failures using the
representative set from step 4 as the reference set.
"""
# for now only allowing uclust for otu picking
allowed_denovo_otu_picking_methods = ['uclust', 'usearch61']
allowed_reference_otu_picking_methods = ['uclust_ref', 'usearch61_ref']
assert denovo_otu_picking_method in allowed_denovo_otu_picking_methods,\
"Unknown de novo OTU picking method: %s. Known methods are: %s"\
% (denovo_otu_picking_method,
','.join(allowed_denovo_otu_picking_methods))
assert reference_otu_picking_method in allowed_reference_otu_picking_methods,\
"Unknown reference OTU picking method: %s. Known methods are: %s"\
% (reference_otu_picking_method,
','.join(allowed_reference_otu_picking_methods))
# Prepare some variables for the later steps
index_links = []
input_dir, input_filename = split(input_fp)
input_basename, input_ext = splitext(input_filename)
create_dir(output_dir)
commands = []
if logger is None:
log_fp = generate_log_fp(output_dir)
logger = WorkflowLogger(log_fp,
params=params,
qiime_config=qiime_config)
close_logger_on_success = True
index_links.append(
('Run summary data',
log_fp,
_index_headers['run_summary']))
else:
close_logger_on_success = False
if not suppress_md5:
log_input_md5s(logger, [input_fp,
refseqs_fp,
step1_otu_map_fp,
step1_failures_fasta_fp])
# if the user has not passed a different reference collection for the pre-filter,
# used the main refseqs_fp. this is useful if the user wants to provide a smaller
# reference collection, or to use the input reference collection when running in
# iterative mode (rather than an iteration's new refseqs)
if prefilter_refseqs_fp is None:
prefilter_refseqs_fp = refseqs_fp
# Step 1: Closed-reference OTU picking on the input file (if not already
# complete)
if step1_otu_map_fp and step1_failures_fasta_fp:
step1_dir = '%s/step1_otus' % output_dir
create_dir(step1_dir)
logger.write("Using pre-existing reference otu map and failures.\n\n")
else:
if prefilter_percent_id is not None:
prefilter_dir = '%s/prefilter_otus/' % output_dir
prefilter_failures_list_fp = '%s/%s_failures.txt' % \
(prefilter_dir, input_basename)
prefilter_pick_otu_cmd = pick_reference_otus(
input_fp, prefilter_dir, reference_otu_picking_method,
prefilter_refseqs_fp, parallel, params, logger, prefilter_percent_id)
commands.append(
[('Pick Reference OTUs (prefilter)', prefilter_pick_otu_cmd)])
prefiltered_input_fp = '%s/prefiltered_%s%s' %\
(prefilter_dir, input_basename, input_ext)
filter_fasta_cmd = 'filter_fasta.py -f %s -o %s -s %s -n' %\
(input_fp, prefiltered_input_fp, prefilter_failures_list_fp)
commands.append(
[('Filter prefilter failures from input', filter_fasta_cmd)])
index_links.append(
('Pre-filtered sequence identifiers '
'(failed to hit reference at %1.1f%% identity)' % (float(prefilter_percent_id)*100),
prefilter_failures_list_fp,
_index_headers['sequences']))
# Call the command handler on the list of commands
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
input_fp = prefiltered_input_fp
input_dir, input_filename = split(input_fp)
input_basename, input_ext = splitext(input_filename)
if getsize(prefiltered_input_fp) == 0:
raise ValueError(
"All sequences were discarded by the prefilter. "
"Are the input sequences in the same orientation "
"in your input file and reference file (you can "
"add 'pick_otus:enable_rev_strand_match True' to "
"your parameters file if not)? Are you using the "
"correct reference file?")
# Build the OTU picking command
step1_dir = \
'%s/step1_otus' % output_dir
step1_otu_map_fp = \
'%s/%s_otus.txt' % (step1_dir, input_basename)
step1_pick_otu_cmd = pick_reference_otus(
input_fp, step1_dir, reference_otu_picking_method,
refseqs_fp, parallel, params, logger)
commands.append([('Pick Reference OTUs', step1_pick_otu_cmd)])
# Build the failures fasta file
step1_failures_list_fp = '%s/%s_failures.txt' % \
(step1_dir, input_basename)
step1_failures_fasta_fp = \
'%s/failures.fasta' % step1_dir
step1_filter_fasta_cmd = 'filter_fasta.py -f %s -s %s -o %s' %\
(input_fp, step1_failures_list_fp, step1_failures_fasta_fp)
commands.append([('Generate full failures fasta file',
step1_filter_fasta_cmd)])
# Call the command handler on the list of commands
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
step1_repset_fasta_fp = \
'%s/step1_rep_set.fna' % step1_dir
step1_pick_rep_set_cmd = 'pick_rep_set.py -i %s -o %s -f %s' %\
(step1_otu_map_fp, step1_repset_fasta_fp, input_fp)
commands.append([('Pick rep set', step1_pick_rep_set_cmd)])
# Call the command handler on the list of commands
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
# Subsample the failures fasta file to retain (roughly) the
# percent_subsample
step2_input_fasta_fp = \
'%s/subsampled_failures.fasta' % step1_dir
subsample_fasta(step1_failures_fasta_fp,
step2_input_fasta_fp,
percent_subsample)
logger.write('# Subsample the failures fasta file using API \n' +
'python -c "import qiime; qiime.util.subsample_fasta' +
'(\'%s\', \'%s\', \'%f\')\n\n"' % (abspath(step1_failures_fasta_fp),
abspath(
step2_input_fasta_fp),
percent_subsample))
# Prep the OTU picking command for the subsampled failures
step2_dir = '%s/step2_otus/' % output_dir
step2_cmd = pick_denovo_otus(step2_input_fasta_fp,
step2_dir,
new_ref_set_id,
denovo_otu_picking_method,
params,
logger)
step2_otu_map_fp = '%s/subsampled_failures_otus.txt' % step2_dir
commands.append([('Pick de novo OTUs for new clusters', step2_cmd)])
# Prep the rep set picking command for the subsampled failures
step2_repset_fasta_fp = '%s/step2_rep_set.fna' % step2_dir
step2_rep_set_cmd = 'pick_rep_set.py -i %s -o %s -f %s' %\
(step2_otu_map_fp, step2_repset_fasta_fp, step2_input_fasta_fp)
commands.append(
[('Pick representative set for subsampled failures', step2_rep_set_cmd)])
step3_dir = '%s/step3_otus/' % output_dir
step3_otu_map_fp = '%s/failures_otus.txt' % step3_dir
step3_failures_list_fp = '%s/failures_failures.txt' % step3_dir
step3_cmd = pick_reference_otus(
step1_failures_fasta_fp,
step3_dir,
reference_otu_picking_method,
step2_repset_fasta_fp,
parallel,
params,
logger)
commands.append([
('Pick reference OTUs using de novo rep set', step3_cmd)])
# name the final otu map
merged_otu_map_fp = '%s/final_otu_map.txt' % output_dir
index_links.append(
('Final map of OTU identifier to sequence identifers (i.e., "OTU map")',
merged_otu_map_fp,
_index_headers['otu_maps']))
if not suppress_step4:
step3_failures_fasta_fp = '%s/failures_failures.fasta' % step3_dir
step3_filter_fasta_cmd = 'filter_fasta.py -f %s -s %s -o %s' %\
(step1_failures_fasta_fp,
step3_failures_list_fp, step3_failures_fasta_fp)
commands.append([('Create fasta file of step3 failures',
step3_filter_fasta_cmd)])
step4_dir = '%s/step4_otus/' % output_dir
step4_cmd = pick_denovo_otus(step3_failures_fasta_fp,
step4_dir,
'.'.join([new_ref_set_id, 'CleanUp']),
denovo_otu_picking_method,
params,
logger)
step4_otu_map_fp = '%s/failures_failures_otus.txt' % step4_dir
commands.append([('Pick de novo OTUs on step3 failures', step4_cmd)])
# Merge the otu maps, note that we are explicitly using the '>' operator
# otherwise passing the --force flag on the script interface would
# append the newly created maps to the map that was previously created
cat_otu_tables_cmd = 'cat %s %s %s > %s' %\
(step1_otu_map_fp, step3_otu_map_fp,
step4_otu_map_fp, merged_otu_map_fp)
commands.append([('Merge OTU maps', cat_otu_tables_cmd)])
step4_repset_fasta_fp = '%s/step4_rep_set.fna' % step4_dir
step4_rep_set_cmd = 'pick_rep_set.py -i %s -o %s -f %s' %\
(step4_otu_map_fp, step4_repset_fasta_fp, step3_failures_fasta_fp)
commands.append(
[('Pick representative set for subsampled failures', step4_rep_set_cmd)])
else:
# Merge the otu maps, note that we are explicitly using the '>' operator
# otherwise passing the --force flag on the script interface would
# append the newly created maps to the map that was previously created
cat_otu_tables_cmd = 'cat %s %s > %s' %\
(step1_otu_map_fp, step3_otu_map_fp, merged_otu_map_fp)
commands.append([('Merge OTU maps', cat_otu_tables_cmd)])
# Move the step 3 failures file to the top-level directory
commands.append([('Move final failures file to top-level directory',
'mv %s %s/final_failures.txt' % (step3_failures_list_fp, output_dir))])
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
otu_fp = merged_otu_map_fp
# Filter singletons from the otu map
otu_no_singletons_fp = '%s/final_otu_map_mc%d.txt' % (output_dir,
min_otu_size)
otus_to_keep = filter_otus_from_otu_map(
otu_fp,
otu_no_singletons_fp,
min_otu_size)
index_links.append(('Final map of OTU identifier to sequence identifers excluding '
'OTUs with fewer than %d sequences' % min_otu_size,
otu_no_singletons_fp,
_index_headers['otu_maps']))
logger.write('# Filter singletons from the otu map using API \n' +
'python -c "import qiime; qiime.filter.filter_otus_from_otu_map' +
'(\'%s\', \'%s\', \'%d\')"\n\n' % (abspath(otu_fp),
abspath(
otu_no_singletons_fp),
min_otu_size))
# make the final representative seqs file and a new refseqs file that
# could be used in subsequent otu picking runs.
# this is clunky. first, we need to do this without singletons to match
# the otu map without singletons. next, there is a difference in what
# we need the reference set to be and what we need the repseqs to be.
# the reference set needs to be a superset of the input reference set
# to this set. the repset needs to be only the sequences that were observed
# in this data set, and we want reps for the step1 reference otus to be
# reads from this run so we don't hit issues building a tree using
# sequences of very different lengths. so...
final_repset_fp = '%s/rep_set.fna' % output_dir
index_links.append(
('OTU representative sequences',
final_repset_fp,
_index_headers['sequences']))
final_repset_f = open(final_repset_fp, 'w')
new_refseqs_fp = '%s/new_refseqs.fna' % output_dir
index_links.append(
('New reference sequences (i.e., OTU representative sequences plus input '
'reference sequences)',
new_refseqs_fp,
_index_headers['sequences']))
# write non-singleton otus representative sequences from step1 to the
# final rep set file
for otu_id, seq in parse_fasta(open(step1_repset_fasta_fp, 'U')):
if otu_id.split()[0] in otus_to_keep:
final_repset_f.write('>%s\n%s\n' % (otu_id, seq))
logger.write('# Write non-singleton otus representative sequences ' +
'from step1 to the final rep set file: %s\n\n' % final_repset_fp)
# copy the full input refseqs file to the new refseqs_fp
copy(refseqs_fp, new_refseqs_fp)
new_refseqs_f = open(new_refseqs_fp, 'a')
new_refseqs_f.write('\n')
logger.write('# Copy the full input refseqs file to the new refseq file\n' +
'cp %s %s\n\n' % (refseqs_fp, new_refseqs_fp))
# iterate over all representative sequences from step2 and step4 and write
# those corresponding to non-singleton otus to the final representative set
# file and the new reference sequences file.
for otu_id, seq in parse_fasta(open(step2_repset_fasta_fp, 'U')):
if otu_id.split()[0] in otus_to_keep:
new_refseqs_f.write('>%s\n%s\n' % (otu_id, seq))
final_repset_f.write('>%s\n%s\n' % (otu_id, seq))
if not suppress_step4:
for otu_id, seq in parse_fasta(open(step4_repset_fasta_fp, 'U')):
if otu_id.split()[0] in otus_to_keep:
new_refseqs_f.write('>%s\n%s\n' % (otu_id, seq))
final_repset_f.write('>%s\n%s\n' % (otu_id, seq))
new_refseqs_f.close()
final_repset_f.close()
logger.write('# Write non-singleton otus representative sequences from ' +
'step 2 and step 4 to the final representative set and the new reference' +
' set (%s and %s respectively)\n\n' % (final_repset_fp, new_refseqs_fp))
# Prep the make_otu_table.py command
otu_table_fp = '%s/otu_table_mc%d.biom' % (output_dir, min_otu_size)
make_otu_table_cmd = 'make_otu_table.py -i %s -o %s' %\
(otu_no_singletons_fp, otu_table_fp)
commands.append([("Make the otu table", make_otu_table_cmd)])
index_links.append(
('OTU table exluding OTUs with fewer than %d sequences' % min_otu_size,
otu_table_fp,
_index_headers['otu_tables']))
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
# initialize output file names - these differ based on what combination of
# taxonomy assignment and alignment/tree building is happening.
if run_assign_tax and run_align_and_tree:
tax_input_otu_table_fp = otu_table_fp
otu_table_w_tax_fp = \
'%s/otu_table_mc%d_w_tax.biom' % (output_dir, min_otu_size)
align_and_tree_input_otu_table = otu_table_w_tax_fp
index_links.append(
('OTU table exluding OTUs with fewer than %d sequences and including OTU '
'taxonomy assignments' % min_otu_size,
otu_table_w_tax_fp,
_index_headers['otu_tables']))
pynast_failure_filtered_otu_table_fp = \
'%s/otu_table_mc%d_w_tax_no_pynast_failures.biom' % (output_dir, min_otu_size)
index_links.append(
('OTU table exluding OTUs with fewer than %d sequences and sequences that '
'fail to align with PyNAST and including OTU taxonomy assignments' % min_otu_size,
pynast_failure_filtered_otu_table_fp,
_index_headers['otu_tables']))
elif run_assign_tax:
tax_input_otu_table_fp = otu_table_fp
otu_table_w_tax_fp = \
'%s/otu_table_mc%d_w_tax.biom' % (output_dir, min_otu_size)
index_links.append(
('OTU table exluding OTUs with fewer than %d sequences and including OTU '
'taxonomy assignments' % min_otu_size,
otu_table_w_tax_fp,
_index_headers['otu_tables']))
elif run_align_and_tree:
align_and_tree_input_otu_table = otu_table_fp
pynast_failure_filtered_otu_table_fp = \
'%s/otu_table_mc%d_no_pynast_failures.biom' % (output_dir,
min_otu_size)
index_links.append(
('OTU table exluding OTUs with fewer than %d sequences and sequences that '
'fail to align with PyNAST' % min_otu_size,
pynast_failure_filtered_otu_table_fp,
_index_headers['otu_tables']))
if run_assign_tax:
if exists(otu_table_w_tax_fp) and getsize(otu_table_w_tax_fp) > 0:
logger.write(
"Final output file exists (%s). Will not rebuild." %
otu_table_w_tax_fp)
else:
# remove files from partially completed runs
remove_files([otu_table_w_tax_fp], error_on_missing=False)
taxonomy_fp = assign_tax(
repset_fasta_fp=final_repset_fp,
output_dir=output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
parallel=parallel,
logger=logger,
status_update_callback=status_update_callback)
# Add taxa to otu table
add_metadata_cmd = 'biom add-metadata -i %s --observation-metadata-fp %s -o %s --sc-separated taxonomy --observation-header OTUID,taxonomy' %\
(tax_input_otu_table_fp, taxonomy_fp, otu_table_w_tax_fp)
commands.append([("Add taxa to OTU table", add_metadata_cmd)])
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
if run_align_and_tree:
rep_set_tree_fp = join(output_dir, 'rep_set.tre')
index_links.append(
('OTU phylogenetic tree',
rep_set_tree_fp,
_index_headers['trees']))
if exists(pynast_failure_filtered_otu_table_fp) and\
getsize(pynast_failure_filtered_otu_table_fp) > 0:
logger.write("Final output file exists (%s). Will not rebuild." %
pynast_failure_filtered_otu_table_fp)
else:
# remove files from partially completed runs
remove_files([pynast_failure_filtered_otu_table_fp],
error_on_missing=False)
pynast_failures_fp = align_and_tree(
repset_fasta_fp=final_repset_fp,
output_dir=output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
parallel=parallel,
logger=logger,
status_update_callback=status_update_callback)
# Build OTU table without PyNAST failures
with biom_open(align_and_tree_input_otu_table) as biom_file:
table = Table.from_hdf5(biom_file)
filtered_otu_table = filter_otus_from_otu_table(table,
get_seq_ids_from_fasta_file(open(pynast_failures_fp, 'U')),
0, inf, 0, inf, negate_ids_to_keep=True)
write_biom_table(filtered_otu_table,
pynast_failure_filtered_otu_table_fp)
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
if close_logger_on_success:
logger.close()
if not suppress_index_page:
index_fp = '%s/index.html' % output_dir
generate_index_page(index_links, index_fp)
def get_clusters_from_fasta_filepath(
fasta_filepath,
original_fasta_path,
percent_ID=0.97,
max_accepts=1,
max_rejects=8,
stepwords=8,
word_length=8,
optimal=False,
exact=False,
suppress_sort=False,
output_dir=None,
enable_rev_strand_matching=False,
subject_fasta_filepath=None,
suppress_new_clusters=False,
return_cluster_maps=False,
stable_sort=False,
save_uc_files=True,
HALT_EXEC=False):
""" Main convenience wrapper for using uclust to generate cluster files
A source fasta file is required for the fasta_filepath. This will be
sorted to be in order of longest to shortest length sequences. Following
this, the sorted fasta file is used to generate a cluster file in the
uclust (.uc) format. Next the .uc file is converted to cd-hit format
(.clstr). Finally this file is parsed and returned as a list of lists,
where each sublist a cluster of sequences. If an output_dir is
specified, the intermediate files will be preserved, otherwise all
files created are temporary and will be deleted at the end of this
function
The percent_ID parameter specifies the percent identity for a clusters,
i.e., if 99% were the parameter, all sequences that were 99% identical
would be grouped as a cluster.
"""
# Create readable intermediate filenames if they are to be kept
fasta_output_filepath = None
uc_output_filepath = None
cd_hit_filepath = None
if output_dir and not output_dir.endswith('/'):
output_dir += '/'
if save_uc_files:
uc_save_filepath = get_output_filepaths(
output_dir,
original_fasta_path)
else:
uc_save_filepath = None
sorted_fasta_filepath = ""
uc_filepath = ""
clstr_filepath = ""
# Error check in case any app controller fails
files_to_remove = []
try:
if not suppress_sort:
# Sort fasta input file from largest to smallest sequence
sort_fasta = uclust_fasta_sort_from_filepath(fasta_filepath,
output_filepath=fasta_output_filepath)
# Get sorted fasta name from application wrapper
sorted_fasta_filepath = sort_fasta['Output'].name
files_to_remove.append(sorted_fasta_filepath)
else:
sort_fasta = None
sorted_fasta_filepath = fasta_filepath
# Generate uclust cluster file (.uc format)
uclust_cluster = uclust_cluster_from_sorted_fasta_filepath(
sorted_fasta_filepath,
uc_save_filepath,
percent_ID=percent_ID,
max_accepts=max_accepts,
max_rejects=max_rejects,
stepwords=stepwords,
word_length=word_length,
optimal=optimal,
exact=exact,
suppress_sort=suppress_sort,
enable_rev_strand_matching=enable_rev_strand_matching,
subject_fasta_filepath=subject_fasta_filepath,
suppress_new_clusters=suppress_new_clusters,
stable_sort=stable_sort,
HALT_EXEC=HALT_EXEC)
# Get cluster file name from application wrapper
remove_files(files_to_remove)
except ApplicationError:
remove_files(files_to_remove)
raise ApplicationError('Error running uclust. Possible causes are '
'unsupported version (current supported version is v1.2.22) is installed or '
'improperly formatted input file was provided')
except ApplicationNotFoundError:
remove_files(files_to_remove)
raise ApplicationNotFoundError('uclust not found, is it properly ' +
'installed?')
# Get list of lists for each cluster
clusters, failures, seeds = \
clusters_from_uc_file(uclust_cluster['ClusterFile'])
# Remove temp files unless user specifies output filepath
if not save_uc_files:
uclust_cluster.cleanUp()
if return_cluster_maps:
return clusters, failures, seeds
else:
return clusters.values(), failures, seeds
def test_plot_heatmap(self):
plot_heatmap(
self.otu_table, self.otu_table.observation_ids, self.otu_table.sample_ids, filename=self.tmp_heatmap_fpath
)
self.assertEqual(exists(self.tmp_heatmap_fpath), True)
remove_files(set([self.tmp_heatmap_fpath]))
def main():
option_parser, options, args = parse_command_line_parameters(**script_info)
DEBUG = options.verbose
check_options(option_parser, options)
start_time = time()
option_lines = format_options_as_lines(options)
if DEBUG:
print FORMAT_BAR
print "Running with options:"
for line in sorted(option_lines):
print line
print FORMAT_BAR
# because the blast app controller uses absolute paths, make sure subject
# db path is fully specified
subject_db = options.subjectdb
if not subject_db.startswith('/'):
subject_db = join(getcwd(), subject_db)
if not options.no_format_db:
# initialize object
inpath = FilePath(abspath(options.subjectdb))
subject_dir, subj_file = split(inpath)
fdb = FormatDb(WorkingDir=subject_dir)
# Currently we do not support protein blasts, but
# this would be easy to add in the future...
fdb.Parameters['-p'].on('F')
# Create indices for record lookup
fdb.Parameters['-o'].on('T')
# Set input database
fdb.Parameters['-i'].on(subject_db)
formatdb_cmd = fdb.BaseCommand
if DEBUG:
print "Formatting db with command: %s" % formatdb_cmd
app_result = fdb(subject_db)
formatdb_filepaths = []
for v in app_result.values():
try:
formatdb_filepaths.append(v.name)
except AttributeError:
# not a file object, so no path to return
pass
db_format_time = time() - start_time
if DEBUG:
print "Formatting subject db took: %2.f seconds" % db_format_time
print "formatdb log file written to: %s" % app_result['log']
print FORMAT_BAR
else:
db_format_time = time() - start_time
formatdb_cmd = "None (formatdb not called)"
# Check that User-Supplied subjectdb is valid
db_ext = [".nhr", ".nin", ".nsd", ".nsi", ".nsq"]
formatdb_filepaths = [subject_db + ext for ext in db_ext]
if DEBUG:
print "Checking that pre-existing formatdb files exist and can be read."
print "Files to be checked:"
for fp in formatdb_filepaths:
print fp
print FORMAT_BAR
try:
formatdb_files = [open(db_f, "U") for db_f in formatdb_filepaths]
[f.close() for f in formatdb_files]
except IOError:
if DEBUG:
print "Cannot open user-supplied database file:", db_f
option_parser.error(
"""Problem with -d and --no_format_db option combination: Cannot open the following user-supplied database file: %s. Consider running without --no_format_db to let formatdb generate these required files""" %
db_f)
if DEBUG:
print "OK: BLAST Database files exist and can be read."
print FORMAT_BAR
# Perform BLAST search
blast_results, hit_ids, removed_hit_ids = find_homologs(options.querydb,
subject_db, options.e_value, options.max_hits,
options.working_dir, options.blastmatroot, options.wordsize,
options.percent_aligned, DEBUG=DEBUG)
blast_time = (time() - start_time) - db_format_time
if DEBUG:
print "BLAST search took: %2.f minute(s)" % (blast_time / 60.0)
print FORMAT_BAR
# Create output folder
outputdir = options.outputdir
try:
makedirs(outputdir)
except OSError:
pass
# Record raw blast results
raw_blast_results_path = join(outputdir, "raw_blast_results.txt")
f = open(raw_blast_results_path, 'w')
f.writelines(blast_results)
f.close()
# Record excluded seqs
excluded_seqs_path = join(outputdir, "matching.fna")
ids_to_seq_file(hit_ids, options.querydb, excluded_seqs_path, "")
# Record included (screened) seqs
included_seqs_path = join(outputdir, "non-matching.fna")
all_ids = ids_from_fasta_lines(open(options.querydb))
included_ids = set(all_ids) - hit_ids
ids_to_seq_file(included_ids, options.querydb, included_seqs_path, "")
log_lines = compose_logfile_lines(start_time, db_format_time, blast_time,
option_lines, formatdb_cmd,
blast_results, options, all_ids,
hit_ids, removed_hit_ids,
included_ids, DEBUG)
log_path = join(outputdir, "sequence_exclusion.log")
if DEBUG:
print "Writing summary to: %s" % log_path
f = open(log_path, 'w')
f.writelines(log_lines)
f.close()
if not options.no_clean:
if DEBUG:
print FORMAT_BAR
print "| Cleanup |"
print FORMAT_BAR
if not options.no_format_db:
if options.verbose:
print "Cleaning up formatdb files:", formatdb_filepaths
remove_files(formatdb_filepaths)
else:
if options.verbose:
print "Formatdb not run...nothing to clean"
def tearDown(self):
remove_files(self.files_to_remove)
for folder in self.folders_to_remove:
shutil.rmtree(folder)
def tearDown(self):
if self._files_to_remove:
remove_files(self._files_to_remove)
if isdir('/tmp/truncate_fasta_qual_test/'):
rmtree('/tmp/truncate_fasta_qual_test/')
def cleanUp(self):
""" Remove temporary blast database files, if applicable
"""
remove_files(self._db_files_to_remove, error_on_missing=False)
def tearDown(self):
""" """
remove_files(self.files_to_remove)
def pick_subsampled_open_reference_otus(
input_fp,
refseqs_fp,
output_dir,
percent_subsample,
new_ref_set_id,
command_handler,
params,
qiime_config,
prefilter_refseqs_fp=None,
run_assign_tax=True,
run_align_and_tree=True,
prefilter_percent_id=0.60,
min_otu_size=2,
step1_otu_map_fp=None,
step1_failures_fasta_fp=None,
parallel=False,
suppress_step4=False,
logger=None,
suppress_md5=False,
suppress_index_page=False,
denovo_otu_picking_method='uclust',
reference_otu_picking_method='uclust_ref',
status_update_callback=print_to_stdout):
""" Run the data preparation steps of Qiime
The steps performed by this function are:
- Pick reference OTUs against refseqs_fp
- Subsample the failures to n sequences.
- Pick OTUs de novo on the n failures.
- Pick representative sequences for the resulting OTUs.
- Pick reference OTUs on all failures using the
representative set from step 4 as the reference set.
"""
# for now only allowing uclust for otu picking
allowed_denovo_otu_picking_methods = ['uclust', 'usearch61']
allowed_reference_otu_picking_methods = ['uclust_ref', 'usearch61_ref']
assert denovo_otu_picking_method in allowed_denovo_otu_picking_methods,\
"Unknown de novo OTU picking method: %s. Known methods are: %s"\
% (denovo_otu_picking_method,
','.join(allowed_denovo_otu_picking_methods))
assert reference_otu_picking_method in allowed_reference_otu_picking_methods,\
"Unknown reference OTU picking method: %s. Known methods are: %s"\
% (reference_otu_picking_method,
','.join(allowed_reference_otu_picking_methods))
# Prepare some variables for the later steps
index_links = []
input_dir, input_filename = split(input_fp)
input_basename, input_ext = splitext(input_filename)
create_dir(output_dir)
commands = []
if logger is None:
log_fp = generate_log_fp(output_dir)
logger = WorkflowLogger(log_fp,
params=params,
qiime_config=qiime_config)
close_logger_on_success = True
index_links.append(
('Run summary data', log_fp, _index_headers['run_summary']))
else:
close_logger_on_success = False
if not suppress_md5:
log_input_md5s(
logger,
[input_fp, refseqs_fp, step1_otu_map_fp, step1_failures_fasta_fp])
# if the user has not passed a different reference collection for the pre-filter,
# used the main refseqs_fp. this is useful if the user wants to provide a smaller
# reference collection, or to use the input reference collection when running in
# iterative mode (rather than an iteration's new refseqs)
if prefilter_refseqs_fp is None:
prefilter_refseqs_fp = refseqs_fp
# Step 1: Closed-reference OTU picking on the input file (if not already
# complete)
if step1_otu_map_fp and step1_failures_fasta_fp:
step1_dir = '%s/step1_otus' % output_dir
create_dir(step1_dir)
logger.write("Using pre-existing reference otu map and failures.\n\n")
else:
if prefilter_percent_id is not None:
prefilter_dir = '%s/prefilter_otus/' % output_dir
prefilter_failures_list_fp = '%s/%s_failures.txt' % \
(prefilter_dir, input_basename)
prefilter_pick_otu_cmd = pick_reference_otus(
input_fp, prefilter_dir, reference_otu_picking_method,
prefilter_refseqs_fp, parallel, params, logger,
prefilter_percent_id)
commands.append([('Pick Reference OTUs (prefilter)',
prefilter_pick_otu_cmd)])
prefiltered_input_fp = '%s/prefiltered_%s%s' %\
(prefilter_dir, input_basename, input_ext)
filter_fasta_cmd = 'filter_fasta.py -f %s -o %s -s %s -n' %\
(input_fp, prefiltered_input_fp, prefilter_failures_list_fp)
commands.append([('Filter prefilter failures from input',
filter_fasta_cmd)])
index_links.append(
('Pre-filtered sequence identifiers '
'(failed to hit reference at %1.1f%% identity)' %
(float(prefilter_percent_id) * 100),
prefilter_failures_list_fp, _index_headers['sequences']))
# Call the command handler on the list of commands
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
input_fp = prefiltered_input_fp
input_dir, input_filename = split(input_fp)
input_basename, input_ext = splitext(input_filename)
if getsize(prefiltered_input_fp) == 0:
raise ValueError(
"All sequences were discarded by the prefilter. "
"Are the input sequences in the same orientation "
"in your input file and reference file (you can "
"add 'pick_otus:enable_rev_strand_match True' to "
"your parameters file if not)? Are you using the "
"correct reference file?")
# Build the OTU picking command
step1_dir = \
'%s/step1_otus' % output_dir
step1_otu_map_fp = \
'%s/%s_otus.txt' % (step1_dir, input_basename)
step1_pick_otu_cmd = pick_reference_otus(input_fp, step1_dir,
reference_otu_picking_method,
refseqs_fp, parallel, params,
logger)
commands.append([('Pick Reference OTUs', step1_pick_otu_cmd)])
# Build the failures fasta file
step1_failures_list_fp = '%s/%s_failures.txt' % \
(step1_dir, input_basename)
step1_failures_fasta_fp = \
'%s/failures.fasta' % step1_dir
step1_filter_fasta_cmd = 'filter_fasta.py -f %s -s %s -o %s' %\
(input_fp, step1_failures_list_fp, step1_failures_fasta_fp)
commands.append([('Generate full failures fasta file',
step1_filter_fasta_cmd)])
# Call the command handler on the list of commands
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
step1_repset_fasta_fp = \
'%s/step1_rep_set.fna' % step1_dir
step1_pick_rep_set_cmd = 'pick_rep_set.py -i %s -o %s -f %s' %\
(step1_otu_map_fp, step1_repset_fasta_fp, input_fp)
commands.append([('Pick rep set', step1_pick_rep_set_cmd)])
# Call the command handler on the list of commands
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
# Subsample the failures fasta file to retain (roughly) the
# percent_subsample
step2_input_fasta_fp = \
'%s/subsampled_failures.fasta' % step1_dir
subsample_fasta(step1_failures_fasta_fp, step2_input_fasta_fp,
percent_subsample)
logger.write('# Subsample the failures fasta file using API \n' +
'python -c "import qiime; qiime.util.subsample_fasta' +
'(\'%s\', \'%s\', \'%f\')\n\n"' %
(abspath(step1_failures_fasta_fp),
abspath(step2_input_fasta_fp), percent_subsample))
# Prep the OTU picking command for the subsampled failures
step2_dir = '%s/step2_otus/' % output_dir
step2_cmd = pick_denovo_otus(step2_input_fasta_fp, step2_dir,
new_ref_set_id, denovo_otu_picking_method,
params, logger)
step2_otu_map_fp = '%s/subsampled_failures_otus.txt' % step2_dir
commands.append([('Pick de novo OTUs for new clusters', step2_cmd)])
# Prep the rep set picking command for the subsampled failures
step2_repset_fasta_fp = '%s/step2_rep_set.fna' % step2_dir
step2_rep_set_cmd = 'pick_rep_set.py -i %s -o %s -f %s' %\
(step2_otu_map_fp, step2_repset_fasta_fp, step2_input_fasta_fp)
commands.append([('Pick representative set for subsampled failures',
step2_rep_set_cmd)])
step3_dir = '%s/step3_otus/' % output_dir
step3_otu_map_fp = '%s/failures_otus.txt' % step3_dir
step3_failures_list_fp = '%s/failures_failures.txt' % step3_dir
step3_cmd = pick_reference_otus(step1_failures_fasta_fp, step3_dir,
reference_otu_picking_method,
step2_repset_fasta_fp, parallel, params,
logger)
commands.append([('Pick reference OTUs using de novo rep set', step3_cmd)])
# name the final otu map
merged_otu_map_fp = '%s/final_otu_map.txt' % output_dir
index_links.append((
'Final map of OTU identifier to sequence identifers (i.e., "OTU map")',
merged_otu_map_fp, _index_headers['otu_maps']))
if not suppress_step4:
step3_failures_fasta_fp = '%s/failures_failures.fasta' % step3_dir
step3_filter_fasta_cmd = 'filter_fasta.py -f %s -s %s -o %s' %\
(step1_failures_fasta_fp,
step3_failures_list_fp, step3_failures_fasta_fp)
commands.append([('Create fasta file of step3 failures',
step3_filter_fasta_cmd)])
step4_dir = '%s/step4_otus/' % output_dir
step4_cmd = pick_denovo_otus(step3_failures_fasta_fp, step4_dir,
'.'.join([new_ref_set_id, 'CleanUp']),
denovo_otu_picking_method, params, logger)
step4_otu_map_fp = '%s/failures_failures_otus.txt' % step4_dir
commands.append([('Pick de novo OTUs on step3 failures', step4_cmd)])
# Merge the otu maps, note that we are explicitly using the '>' operator
# otherwise passing the --force flag on the script interface would
# append the newly created maps to the map that was previously created
cat_otu_tables_cmd = 'cat %s %s %s > %s' %\
(step1_otu_map_fp, step3_otu_map_fp,
step4_otu_map_fp, merged_otu_map_fp)
commands.append([('Merge OTU maps', cat_otu_tables_cmd)])
step4_repset_fasta_fp = '%s/step4_rep_set.fna' % step4_dir
step4_rep_set_cmd = 'pick_rep_set.py -i %s -o %s -f %s' %\
(step4_otu_map_fp, step4_repset_fasta_fp, step3_failures_fasta_fp)
commands.append([('Pick representative set for subsampled failures',
step4_rep_set_cmd)])
else:
# Merge the otu maps, note that we are explicitly using the '>' operator
# otherwise passing the --force flag on the script interface would
# append the newly created maps to the map that was previously created
cat_otu_tables_cmd = 'cat %s %s > %s' %\
(step1_otu_map_fp, step3_otu_map_fp, merged_otu_map_fp)
commands.append([('Merge OTU maps', cat_otu_tables_cmd)])
# Move the step 3 failures file to the top-level directory
commands.append([('Move final failures file to top-level directory',
'mv %s %s/final_failures.txt' %
(step3_failures_list_fp, output_dir))])
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
otu_fp = merged_otu_map_fp
# Filter singletons from the otu map
otu_no_singletons_fp = '%s/final_otu_map_mc%d.txt' % (output_dir,
min_otu_size)
otus_to_keep = filter_otus_from_otu_map(otu_fp, otu_no_singletons_fp,
min_otu_size)
index_links.append(
('Final map of OTU identifier to sequence identifers excluding '
'OTUs with fewer than %d sequences' % min_otu_size,
otu_no_singletons_fp, _index_headers['otu_maps']))
logger.write(
'# Filter singletons from the otu map using API \n' +
'python -c "import qiime; qiime.filter.filter_otus_from_otu_map' +
'(\'%s\', \'%s\', \'%d\')"\n\n' %
(abspath(otu_fp), abspath(otu_no_singletons_fp), min_otu_size))
# make the final representative seqs file and a new refseqs file that
# could be used in subsequent otu picking runs.
# this is clunky. first, we need to do this without singletons to match
# the otu map without singletons. next, there is a difference in what
# we need the reference set to be and what we need the repseqs to be.
# the reference set needs to be a superset of the input reference set
# to this set. the repset needs to be only the sequences that were observed
# in this data set, and we want reps for the step1 reference otus to be
# reads from this run so we don't hit issues building a tree using
# sequences of very different lengths. so...
final_repset_fp = '%s/rep_set.fna' % output_dir
index_links.append(('OTU representative sequences', final_repset_fp,
_index_headers['sequences']))
final_repset_f = open(final_repset_fp, 'w')
new_refseqs_fp = '%s/new_refseqs.fna' % output_dir
index_links.append((
'New reference sequences (i.e., OTU representative sequences plus input '
'reference sequences)', new_refseqs_fp, _index_headers['sequences']))
# write non-singleton otus representative sequences from step1 to the
# final rep set file
for otu_id, seq in parse_fasta(open(step1_repset_fasta_fp, 'U')):
if otu_id.split()[0] in otus_to_keep:
final_repset_f.write('>%s\n%s\n' % (otu_id, seq))
logger.write('# Write non-singleton otus representative sequences ' +
'from step1 to the final rep set file: %s\n\n' %
final_repset_fp)
# copy the full input refseqs file to the new refseqs_fp
copy(refseqs_fp, new_refseqs_fp)
new_refseqs_f = open(new_refseqs_fp, 'a')
new_refseqs_f.write('\n')
logger.write(
'# Copy the full input refseqs file to the new refseq file\n' +
'cp %s %s\n\n' % (refseqs_fp, new_refseqs_fp))
# iterate over all representative sequences from step2 and step4 and write
# those corresponding to non-singleton otus to the final representative set
# file and the new reference sequences file.
for otu_id, seq in parse_fasta(open(step2_repset_fasta_fp, 'U')):
if otu_id.split()[0] in otus_to_keep:
new_refseqs_f.write('>%s\n%s\n' % (otu_id, seq))
final_repset_f.write('>%s\n%s\n' % (otu_id, seq))
if not suppress_step4:
for otu_id, seq in parse_fasta(open(step4_repset_fasta_fp, 'U')):
if otu_id.split()[0] in otus_to_keep:
new_refseqs_f.write('>%s\n%s\n' % (otu_id, seq))
final_repset_f.write('>%s\n%s\n' % (otu_id, seq))
new_refseqs_f.close()
final_repset_f.close()
logger.write(
'# Write non-singleton otus representative sequences from ' +
'step 2 and step 4 to the final representative set and the new reference'
+ ' set (%s and %s respectively)\n\n' %
(final_repset_fp, new_refseqs_fp))
# Prep the make_otu_table.py command
otu_table_fp = '%s/otu_table_mc%d.biom' % (output_dir, min_otu_size)
make_otu_table_cmd = 'make_otu_table.py -i %s -o %s' %\
(otu_no_singletons_fp, otu_table_fp)
commands.append([("Make the otu table", make_otu_table_cmd)])
index_links.append(
('OTU table exluding OTUs with fewer than %d sequences' % min_otu_size,
otu_table_fp, _index_headers['otu_tables']))
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
# initialize output file names - these differ based on what combination of
# taxonomy assignment and alignment/tree building is happening.
if run_assign_tax and run_align_and_tree:
tax_input_otu_table_fp = otu_table_fp
otu_table_w_tax_fp = \
'%s/otu_table_mc%d_w_tax.biom' % (output_dir, min_otu_size)
align_and_tree_input_otu_table = otu_table_w_tax_fp
index_links.append((
'OTU table exluding OTUs with fewer than %d sequences and including OTU '
'taxonomy assignments' % min_otu_size, otu_table_w_tax_fp,
_index_headers['otu_tables']))
pynast_failure_filtered_otu_table_fp = \
'%s/otu_table_mc%d_w_tax_no_pynast_failures.biom' % (output_dir, min_otu_size)
index_links.append((
'OTU table exluding OTUs with fewer than %d sequences and sequences that '
'fail to align with PyNAST and including OTU taxonomy assignments'
% min_otu_size, pynast_failure_filtered_otu_table_fp,
_index_headers['otu_tables']))
elif run_assign_tax:
tax_input_otu_table_fp = otu_table_fp
otu_table_w_tax_fp = \
'%s/otu_table_mc%d_w_tax.biom' % (output_dir, min_otu_size)
index_links.append((
'OTU table exluding OTUs with fewer than %d sequences and including OTU '
'taxonomy assignments' % min_otu_size, otu_table_w_tax_fp,
_index_headers['otu_tables']))
elif run_align_and_tree:
align_and_tree_input_otu_table = otu_table_fp
pynast_failure_filtered_otu_table_fp = \
'%s/otu_table_mc%d_no_pynast_failures.biom' % (output_dir,
min_otu_size)
index_links.append((
'OTU table exluding OTUs with fewer than %d sequences and sequences that '
'fail to align with PyNAST' % min_otu_size,
pynast_failure_filtered_otu_table_fp,
_index_headers['otu_tables']))
if run_assign_tax:
if exists(otu_table_w_tax_fp) and getsize(otu_table_w_tax_fp) > 0:
logger.write("Final output file exists (%s). Will not rebuild." %
otu_table_w_tax_fp)
else:
# remove files from partially completed runs
remove_files([otu_table_w_tax_fp], error_on_missing=False)
taxonomy_fp = assign_tax(
repset_fasta_fp=final_repset_fp,
output_dir=output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
parallel=parallel,
logger=logger,
status_update_callback=status_update_callback)
# Add taxa to otu table
add_metadata_cmd = 'biom add-metadata -i %s --observation-metadata-fp %s -o %s --sc-separated taxonomy --observation-header OTUID,taxonomy' %\
(tax_input_otu_table_fp, taxonomy_fp, otu_table_w_tax_fp)
commands.append([("Add taxa to OTU table", add_metadata_cmd)])
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
if run_align_and_tree:
rep_set_tree_fp = join(output_dir, 'rep_set.tre')
index_links.append(('OTU phylogenetic tree', rep_set_tree_fp,
_index_headers['trees']))
if exists(pynast_failure_filtered_otu_table_fp) and\
getsize(pynast_failure_filtered_otu_table_fp) > 0:
logger.write("Final output file exists (%s). Will not rebuild." %
pynast_failure_filtered_otu_table_fp)
else:
# remove files from partially completed runs
remove_files([pynast_failure_filtered_otu_table_fp],
error_on_missing=False)
pynast_failures_fp = align_and_tree(
repset_fasta_fp=final_repset_fp,
output_dir=output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
parallel=parallel,
logger=logger,
status_update_callback=status_update_callback)
# Build OTU table without PyNAST failures
filtered_otu_table = filter_otus_from_otu_table(
parse_biom_table(open(align_and_tree_input_otu_table, 'U')),
get_seq_ids_from_fasta_file(open(pynast_failures_fp, 'U')),
0,
inf,
0,
inf,
negate_ids_to_keep=True)
otu_table_f = open(pynast_failure_filtered_otu_table_fp, 'w')
otu_table_f.write(format_biom_table(filtered_otu_table))
otu_table_f.close()
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
if close_logger_on_success:
logger.close()
if not suppress_index_page:
index_fp = '%s/index.html' % output_dir
generate_index_page(index_links, index_fp)
def test_plot_heatmap(self):
plot_heatmap(
self.otu_table, self.otu_table.ids(axis='observation'),
self.otu_table.ids(), filename=self.tmp_heatmap_fpath)
self.assertEqual(exists(self.tmp_heatmap_fpath), True)
remove_files(set([self.tmp_heatmap_fpath]))
def get_chimeras_from_Nast_aligned(seqs_fp, ref_db_aligned_fp=None,
ref_db_fasta_fp=None,
HALT_EXEC=False, min_div_ratio=None,
keep_intermediates=False):
"""remove chimeras from seqs_fp using chimeraSlayer.
seqs_fp: a filepath with the seqs to check in the file
ref_db_aligned_fp: fp to (pynast) aligned reference sequences
ref_db_fasta_fp: same seqs as above, just unaligned. Will be computed on the fly if not provided,
HALT_EXEC: stop execution if true
min_div_ratio: passed to ChimeraSlayer App
"""
files_to_remove = []
# might come in as FilePath object with quotes
seqs_fp = str(seqs_fp)
seqs_fp = seqs_fp.rstrip('"')
seqs_fp = seqs_fp.lstrip('"')
seqs_dir, new_seqs_fp = split(seqs_fp)
# if fp is in current dir, we fake a dir change
if seqs_dir == "":
seqs_dir = "./"
# Chimera Slayer puts some temp files in current dir and some in dir of input file
# use exe_dir to change to dir of input file, so to have all tmp files in
# one place
params = {'--query_NAST': new_seqs_fp,
'--exec_dir': seqs_dir}
if ref_db_aligned_fp is None and ref_db_fasta_fp is None:
# use default db, whose relative position to the
# ChimeraSlayer binary is hardcoded
pass
else:
if not ref_db_fasta_fp:
# make degapped reference file
ref_db_fasta_fp = write_degapped_fasta_to_file(parse_fasta(
open(ref_db_aligned_fp)))
files_to_remove.append(ref_db_fasta_fp)
# use user db
params.update({'--db_NAST': abspath(ref_db_aligned_fp),
'--db_FASTA': abspath(ref_db_fasta_fp)})
if min_div_ratio is not None:
params.update({'-R': min_div_ratio})
app = ChimeraSlayer(params=params, HALT_EXEC=HALT_EXEC)
app_results = app()
# this is a FilePath object in case of success.
# How can we test for failure here?
# if not exists(app_results['CPS']):
# raise ApplicationError, "ChimeraSlayer failed. No output file."
chimeras = parse_CPS_file((app_results['CPS']))
if not keep_intermediates:
app.remove_intermediate_files()
remove_files(files_to_remove)
return chimeras
