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 == 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 == 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,
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 run_core_diversity_analyses(
biom_fp,
mapping_fp,
sampling_depth,
output_dir,
qiime_config,
command_handler=call_commands_serially,
tree_fp=None,
params=None,
categories=None,
arare_min_rare_depth=10,
arare_num_steps=10,
parallel=False,
suppress_taxa_summary=False,
suppress_beta_diversity=False,
suppress_alpha_diversity=False,
suppress_group_significance=False,
status_update_callback=print_to_stdout,
):
"""
"""
if categories is not None:
# Validate categories provided by the users
mapping_data, mapping_comments = parse_mapping_file_to_dict(open(mapping_fp, "U"))
metadata_map = MetadataMap(mapping_data, mapping_comments)
for c in categories:
if c not in metadata_map.CategoryNames:
raise ValueError(
"Category '%s' is not a column header "
"in your mapping file. "
"Categories are case and white space sensitive. Valid "
"choices are: (%s)" % (c, ", ".join(metadata_map.CategoryNames))
)
if metadata_map.hasSingleCategoryValue(c):
raise ValueError(
"Category '%s' contains only one value. "
"Categories analyzed here require at least two values." % c
)
else:
categories = []
comma_separated_categories = ",".join(categories)
# prep some variables
if params is None:
params = parse_qiime_parameters([])
create_dir(output_dir)
index_fp = "%s/index.html" % output_dir
index_links = []
commands = []
# begin logging
old_log_fps = glob(join(output_dir, "log_20*txt"))
log_fp = generate_log_fp(output_dir)
index_links.append(("Master run log", log_fp, _index_headers["run_summary"]))
for old_log_fp in old_log_fps:
index_links.append(("Previous run log", old_log_fp, _index_headers["run_summary"]))
logger = WorkflowLogger(log_fp, params=params, qiime_config=qiime_config)
input_fps = [biom_fp, mapping_fp]
if tree_fp is not None:
input_fps.append(tree_fp)
log_input_md5s(logger, input_fps)
# run 'biom summarize-table' on input BIOM table
try:
params_str = get_params_str(params["biom-summarize-table"])
except KeyError:
params_str = ""
biom_table_stats_output_fp = "%s/biom_table_summary.txt" % output_dir
if not exists(biom_table_stats_output_fp):
biom_table_summary_cmd = "biom summarize-table -i %s -o %s --suppress-md5 %s" % (
biom_fp,
biom_table_stats_output_fp,
params_str,
)
commands.append([("Generate BIOM table summary", biom_table_summary_cmd)])
else:
logger.write("Skipping 'biom summarize-table' as %s exists.\n\n" % biom_table_stats_output_fp)
index_links.append(("BIOM table statistics", biom_table_stats_output_fp, _index_headers["run_summary"]))
# filter samples with fewer observations than the requested sampling_depth.
# since these get filtered for some analyses (eg beta diversity after
# even sampling) it's useful to filter them here so they're filtered
# from all analyses.
filtered_biom_fp = "%s/table_mc%d.biom" % (output_dir, sampling_depth)
if not exists(filtered_biom_fp):
filter_samples_cmd = "filter_samples_from_otu_table.py -i %s -o %s -n %d" % (
biom_fp,
filtered_biom_fp,
sampling_depth,
)
commands.append(
[
(
"Filter low sequence count samples from table (minimum sequence count: %d)" % sampling_depth,
filter_samples_cmd,
)
]
)
else:
logger.write("Skipping filter_samples_from_otu_table.py as %s exists.\n\n" % filtered_biom_fp)
biom_fp = filtered_biom_fp
# rarify the BIOM table to sampling_depth
rarefied_biom_fp = "%s/table_even%d.biom" % (output_dir, sampling_depth)
if not exists(rarefied_biom_fp):
single_rarefaction_cmd = "single_rarefaction.py -i %s -o %s -d %d" % (biom_fp, rarefied_biom_fp, sampling_depth)
commands.append([("Rarify the OTU table to %d sequences/sample" % sampling_depth, single_rarefaction_cmd)])
else:
logger.write("Skipping single_rarefaction.py as %s exists.\n\n" % rarefied_biom_fp)
# run initial commands and reset the command list
if len(commands) > 0:
command_handler(commands, status_update_callback, logger, close_logger_on_success=False)
commands = []
if not suppress_beta_diversity:
bdiv_even_output_dir = "%s/bdiv_even%d/" % (output_dir, sampling_depth)
# Need to check for the existence of any distance matrices, since the user
# can select which will be generated.
existing_dm_fps = glob("%s/*_dm.txt" % bdiv_even_output_dir)
if len(existing_dm_fps) == 0:
even_dm_fps = run_beta_diversity_through_plots(
otu_table_fp=rarefied_biom_fp,
mapping_fp=mapping_fp,
output_dir=bdiv_even_output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
# Note: we pass sampling depth=None here as
# we rarify the BIOM table above and pass that
# in here.
sampling_depth=None,
tree_fp=tree_fp,
parallel=parallel,
logger=logger,
suppress_md5=True,
status_update_callback=status_update_callback,
)
else:
logger.write("Skipping beta_diversity_through_plots.py as %s exist(s).\n\n" % ", ".join(existing_dm_fps))
even_dm_fps = [(split(fp)[1].strip("_dm.txt"), fp) for fp in existing_dm_fps]
# Get make_distance_boxplots parameters
try:
params_str = get_params_str(params["make_distance_boxplots"])
except KeyError:
params_str = ""
for bdiv_metric, dm_fp in even_dm_fps:
for category in categories:
boxplots_output_dir = "%s/%s_boxplots/" % (bdiv_even_output_dir, bdiv_metric)
plot_output_fp = "%s/%s_Distances.pdf" % (boxplots_output_dir, category)
stats_output_fp = "%s/%s_Stats.txt" % (boxplots_output_dir, category)
if not exists(plot_output_fp):
boxplots_cmd = "make_distance_boxplots.py -d %s -f %s -o %s -m %s -n 999 %s" % (
dm_fp,
category,
boxplots_output_dir,
mapping_fp,
params_str,
)
commands.append([("Boxplots (%s)" % category, boxplots_cmd)])
else:
logger.write(
"Skipping make_distance_boxplots.py for %s as %s exists.\n\n" % (category, plot_output_fp)
)
index_links.append(
(
"Distance boxplots (%s)" % bdiv_metric,
plot_output_fp,
_index_headers["beta_diversity_even"] % sampling_depth,
)
)
index_links.append(
(
"Distance boxplots statistics (%s)" % bdiv_metric,
stats_output_fp,
_index_headers["beta_diversity_even"] % sampling_depth,
)
)
index_links.append(
(
"PCoA plot (%s)" % bdiv_metric,
"%s/%s_emperor_pcoa_plot/index.html" % (bdiv_even_output_dir, bdiv_metric),
_index_headers["beta_diversity_even"] % sampling_depth,
)
)
index_links.append(
(
"Distance matrix (%s)" % bdiv_metric,
"%s/%s_dm.txt" % (bdiv_even_output_dir, bdiv_metric),
_index_headers["beta_diversity_even"] % sampling_depth,
)
)
index_links.append(
(
"Principal coordinate matrix (%s)" % bdiv_metric,
"%s/%s_pc.txt" % (bdiv_even_output_dir, bdiv_metric),
_index_headers["beta_diversity_even"] % sampling_depth,
)
)
if not suppress_alpha_diversity:
# Alpha rarefaction workflow
arare_full_output_dir = "%s/arare_max%d/" % (output_dir, sampling_depth)
rarefaction_plots_output_fp = "%s/alpha_rarefaction_plots/rarefaction_plots.html" % arare_full_output_dir
if not exists(rarefaction_plots_output_fp):
run_alpha_rarefaction(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=arare_full_output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
tree_fp=tree_fp,
num_steps=arare_num_steps,
parallel=parallel,
logger=logger,
min_rare_depth=arare_min_rare_depth,
max_rare_depth=sampling_depth,
suppress_md5=True,
status_update_callback=status_update_callback,
retain_intermediate_files=False,
)
else:
logger.write("Skipping alpha_rarefaction.py as %s exists.\n\n" % rarefaction_plots_output_fp)
index_links.append(("Alpha rarefaction plots", rarefaction_plots_output_fp, _index_headers["alpha_diversity"]))
collated_alpha_diversity_fps = glob("%s/alpha_div_collated/*txt" % arare_full_output_dir)
try:
params_str = get_params_str(params["compare_alpha_diversity"])
except KeyError:
params_str = ""
if len(categories) > 0:
for collated_alpha_diversity_fp in collated_alpha_diversity_fps:
alpha_metric = splitext(split(collated_alpha_diversity_fp)[1])[0]
compare_alpha_output_dir = "%s/compare_%s" % (arare_full_output_dir, alpha_metric)
if not exists(compare_alpha_output_dir):
compare_alpha_cmd = "compare_alpha_diversity.py -i %s -m %s -c %s -o %s -n 999 %s" % (
collated_alpha_diversity_fp,
mapping_fp,
comma_separated_categories,
compare_alpha_output_dir,
params_str,
)
commands.append([("Compare alpha diversity (%s)" % alpha_metric, compare_alpha_cmd)])
for category in categories:
alpha_comparison_stat_fp = "%s/%s_stats.txt" % (compare_alpha_output_dir, category)
alpha_comparison_boxplot_fp = "%s/%s_boxplots.pdf" % (compare_alpha_output_dir, category)
index_links.append(
(
"Alpha diversity statistics (%s, %s)" % (category, alpha_metric),
alpha_comparison_stat_fp,
_index_headers["alpha_diversity"],
)
)
index_links.append(
(
"Alpha diversity boxplots (%s, %s)" % (category, alpha_metric),
alpha_comparison_boxplot_fp,
_index_headers["alpha_diversity"],
)
)
else:
logger.write(
"Skipping compare_alpha_diversity.py"
" for %s as %s exists.\n\n" % (alpha_metric, compare_alpha_output_dir)
)
else:
logger.write("Skipping compare_alpha_diversity.py as" " no categories were provided.\n\n")
if not suppress_taxa_summary:
taxa_plots_output_dir = "%s/taxa_plots/" % output_dir
# need to check for existence of any html files, since the user can
# select only certain ones to be generated
existing_taxa_plot_html_fps = glob(join(taxa_plots_output_dir, "taxa_summary_plots", "*.html"))
if len(existing_taxa_plot_html_fps) == 0:
run_summarize_taxa_through_plots(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=taxa_plots_output_dir,
mapping_cat=None,
sort=True,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
logger=logger,
suppress_md5=True,
status_update_callback=status_update_callback,
)
else:
logger.write(
"Skipping summarize_taxa_through_plots.py for as %s exist(s).\n\n"
% ", ".join(existing_taxa_plot_html_fps)
)
index_links.append(
(
"Taxa summary bar plots",
"%s/taxa_summary_plots/bar_charts.html" % taxa_plots_output_dir,
_index_headers["taxa_summary"],
)
)
index_links.append(
(
"Taxa summary area plots",
"%s/taxa_summary_plots/area_charts.html" % taxa_plots_output_dir,
_index_headers["taxa_summary"],
)
)
for category in categories:
taxa_plots_output_dir = "%s/taxa_plots_%s/" % (output_dir, category)
# need to check for existence of any html files, since the user can
# select only certain ones to be generated
existing_taxa_plot_html_fps = glob("%s/taxa_summary_plots/*.html" % taxa_plots_output_dir)
if len(existing_taxa_plot_html_fps) == 0:
run_summarize_taxa_through_plots(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=taxa_plots_output_dir,
mapping_cat=category,
sort=True,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
logger=logger,
suppress_md5=True,
status_update_callback=status_update_callback,
)
else:
logger.write(
"Skipping summarize_taxa_through_plots.py for %s as %s exist(s).\n\n"
% (category, ", ".join(existing_taxa_plot_html_fps))
)
index_links.append(
(
"Taxa summary bar plots",
"%s/taxa_summary_plots/bar_charts.html" % taxa_plots_output_dir,
_index_headers["taxa_summary_categorical"] % category,
)
)
index_links.append(
(
"Taxa summary area plots",
"%s/taxa_summary_plots/area_charts.html" % taxa_plots_output_dir,
_index_headers["taxa_summary_categorical"] % category,
)
)
if not suppress_group_significance:
params_str = get_params_str(params["group_significance"])
# group significance tests, aka category significance
for category in categories:
group_signifance_fp = "%s/group_significance_%s.txt" % (output_dir, category)
if not exists(group_signifance_fp):
# Build the OTU cateogry significance command
group_significance_cmd = "group_significance.py -i %s -m %s -c %s -o %s %s" % (
rarefied_biom_fp,
mapping_fp,
category,
group_signifance_fp,
params_str,
)
commands.append([("Group significance (%s)" % category, group_significance_cmd)])
else:
logger.write(
"Skipping group_significance.py for %s as %s exists.\n\n" % (category, group_signifance_fp)
)
index_links.append(
("Category significance (%s)" % category, group_signifance_fp, _index_headers["group_significance"])
)
filtered_biom_gzip_fp = "%s.gz" % filtered_biom_fp
if not exists(filtered_biom_gzip_fp):
commands.append([("Compress the filtered BIOM table", "gzip %s" % filtered_biom_fp)])
else:
logger.write("Skipping compressing of filtered BIOM table as %s exists.\n\n" % filtered_biom_gzip_fp)
index_links.append(
(
"Filtered BIOM table (minimum sequence count: %d)" % sampling_depth,
filtered_biom_gzip_fp,
_index_headers["run_summary"],
)
)
rarified_biom_gzip_fp = "%s.gz" % rarefied_biom_fp
if not exists(rarified_biom_gzip_fp):
commands.append([("Compress the rarified BIOM table", "gzip %s" % rarefied_biom_fp)])
else:
logger.write("Skipping compressing of rarified BIOM table as %s exists.\n\n" % rarified_biom_gzip_fp)
index_links.append(
(
"Rarified BIOM table (sampling depth: %d)" % sampling_depth,
rarified_biom_gzip_fp,
_index_headers["run_summary"],
)
)
if len(commands) > 0:
command_handler(commands, status_update_callback, logger)
else:
logger.close()
generate_index_page(index_links, index_fp)
def run_core_diversity_analyses(
biom_fp,
mapping_fp,
sampling_depth,
output_dir,
qiime_config,
command_handler=call_commands_serially,
tree_fp=None,
params=None,
categories=None,
arare_min_rare_depth=10,
arare_num_steps=10,
parallel=False,
suppress_taxa_summary=False,
suppress_beta_diversity=False,
suppress_alpha_diversity=False,
suppress_otu_category_significance=False,
status_update_callback=print_to_stdout,
):
"""
"""
if categories != None:
# Validate categories provided by the users
mapping_data, mapping_comments = parse_mapping_file_to_dict(open(mapping_fp, "U"))
metadata_map = MetadataMap(mapping_data, mapping_comments)
for c in categories:
if c not in metadata_map.CategoryNames:
raise ValueError, (
"Category '%s' is not a column header "
"in your mapping file. "
"Categories are case and white space sensitive. Valid "
"choices are: (%s)" % (c, ", ".join(metadata_map.CategoryNames))
)
if metadata_map.hasSingleCategoryValue(c):
raise ValueError, (
"Category '%s' contains only one value. "
"Categories analyzed here require at least two values." % c
)
else:
categories = []
# prep some variables
if params == None:
params = parse_qiime_parameters([])
create_dir(output_dir)
index_fp = "%s/index.html" % output_dir
index_links = []
commands = []
# begin logging
log_fp = generate_log_fp(output_dir)
index_links.append(("Master run log", log_fp, _index_headers["run_summary"]))
logger = WorkflowLogger(log_fp, params=params, qiime_config=qiime_config)
input_fps = [biom_fp, mapping_fp]
if tree_fp != None:
input_fps.append(tree_fp)
log_input_md5s(logger, input_fps)
# run print_biom_table_summary.py on input BIOM table
try:
params_str = get_params_str(params["print_biom_table_summary"])
except KeyError:
params_str = ""
biom_table_stats_output_fp = "%s/biom_table_summary.txt" % output_dir
print_biom_table_summary_cmd = "print_biom_table_summary.py -i %s -o %s --suppress_md5 %s" % (
biom_fp,
biom_table_stats_output_fp,
params_str,
)
index_links.append(("BIOM table statistics", biom_table_stats_output_fp, _index_headers["run_summary"]))
commands.append([("Generate BIOM table summary", print_biom_table_summary_cmd)])
# filter samples with fewer observations than the requested sampling_depth.
# since these get filtered for some analyses (eg beta diversity after
# even sampling) it's useful to filter them here so they're filtered
# from all analyses.
filtered_biom_fp = "%s/table_mc%d.biom" % (output_dir, sampling_depth)
filter_samples_cmd = "filter_samples_from_otu_table.py -i %s -o %s -n %d" % (
biom_fp,
filtered_biom_fp,
sampling_depth,
)
commands.append(
[
(
"Filter low sequence count samples from table (minimum sequence count: %d)" % sampling_depth,
filter_samples_cmd,
)
]
)
biom_fp = filtered_biom_fp
# run initial commands and reset the command list
command_handler(commands, status_update_callback, logger, close_logger_on_success=False)
commands = []
if not suppress_beta_diversity:
bdiv_even_output_dir = "%s/bdiv_even%d/" % (output_dir, sampling_depth)
even_dm_fps = run_beta_diversity_through_plots(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=bdiv_even_output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
sampling_depth=sampling_depth,
# force suppression of distance histograms - boxplots work better
# in this context, and are created below.
histogram_categories=[],
tree_fp=tree_fp,
parallel=parallel,
logger=logger,
suppress_md5=True,
status_update_callback=status_update_callback,
)
for bdiv_metric, dm_fp in even_dm_fps:
for category in categories:
boxplots_output_dir = "%s/%s_boxplots/" % (bdiv_even_output_dir, bdiv_metric)
try:
params_str = get_params_str(params["make_distance_boxplots"])
except KeyError:
params_str = ""
boxplots_cmd = "make_distance_boxplots.py -d %s -f %s -o %s -m %s -n 999 %s" % (
dm_fp,
category,
boxplots_output_dir,
mapping_fp,
params_str,
)
commands.append([("Boxplots (%s)" % category, boxplots_cmd)])
index_links.append(
(
"Distance boxplots (%s)" % bdiv_metric,
"%s/%s_Distances.pdf" % (boxplots_output_dir, category),
_index_headers["beta_diversity_even"] % sampling_depth,
)
)
index_links.append(
(
"Distance boxplots statistics (%s)" % bdiv_metric,
"%s/%s_Stats.txt" % (boxplots_output_dir, category),
_index_headers["beta_diversity_even"] % sampling_depth,
)
)
index_links.append(
(
"3D plot (%s, continuous coloring)" % bdiv_metric,
"%s/%s_3d_continuous/%s_pc_3D_PCoA_plots.html" % (bdiv_even_output_dir, bdiv_metric, bdiv_metric),
_index_headers["beta_diversity_even"] % sampling_depth,
)
)
index_links.append(
(
"3D plot (%s, discrete coloring)" % bdiv_metric,
"%s/%s_3d_discrete/%s_pc_3D_PCoA_plots.html" % (bdiv_even_output_dir, bdiv_metric, bdiv_metric),
_index_headers["beta_diversity_even"] % sampling_depth,
)
)
index_links.append(
(
"2D plot (%s, continuous coloring)" % bdiv_metric,
"%s/%s_2d_continuous/%s_pc_2D_PCoA_plots.html" % (bdiv_even_output_dir, bdiv_metric, bdiv_metric),
_index_headers["beta_diversity_even"] % sampling_depth,
)
)
index_links.append(
(
"2D plot (%s, discrete coloring)" % bdiv_metric,
"%s/%s_2d_discrete/%s_pc_2D_PCoA_plots.html" % (bdiv_even_output_dir, bdiv_metric, bdiv_metric),
_index_headers["beta_diversity_even"] % sampling_depth,
)
)
index_links.append(
(
"Distance matrix (%s)" % bdiv_metric,
"%s/%s_dm.txt" % (bdiv_even_output_dir, bdiv_metric),
_index_headers["beta_diversity_even"] % sampling_depth,
)
)
index_links.append(
(
"Principal coordinate matrix (%s)" % bdiv_metric,
"%s/%s_pc.txt" % (bdiv_even_output_dir, bdiv_metric),
_index_headers["beta_diversity_even"] % sampling_depth,
)
)
if not suppress_alpha_diversity:
## Alpha rarefaction workflow
arare_full_output_dir = "%s/arare_max%d/" % (output_dir, sampling_depth)
run_alpha_rarefaction(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=arare_full_output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
tree_fp=tree_fp,
num_steps=arare_num_steps,
parallel=parallel,
logger=logger,
min_rare_depth=arare_min_rare_depth,
max_rare_depth=sampling_depth,
suppress_md5=True,
status_update_callback=status_update_callback,
)
index_links.append(
(
"Alpha rarefaction plots",
"%s/alpha_rarefaction_plots/rarefaction_plots.html" % arare_full_output_dir,
_index_headers["alpha_diversity"],
)
)
collated_alpha_diversity_fps = glob("%s/alpha_div_collated/*txt" % arare_full_output_dir)
try:
params_str = get_params_str(params["compare_alpha_diversity"])
except KeyError:
params_str = ""
for category in categories:
for collated_alpha_diversity_fp in collated_alpha_diversity_fps:
alpha_metric = splitext(split(collated_alpha_diversity_fp)[1])[0]
alpha_comparison_output_fp = "%s/%s_%s.txt" % (arare_full_output_dir, category, alpha_metric)
compare_alpha_cmd = "compare_alpha_diversity.py -i %s -m %s -c %s -o %s -n 999 %s" % (
collated_alpha_diversity_fp,
mapping_fp,
category,
alpha_comparison_output_fp,
params_str,
)
commands.append([("Compare alpha diversity (%s, %s)" % (category, alpha_metric), compare_alpha_cmd)])
index_links.append(
(
"Alpha diversity statistics (%s, %s)" % (category, alpha_metric),
alpha_comparison_output_fp,
_index_headers["alpha_diversity"],
)
)
if not suppress_taxa_summary:
taxa_plots_output_dir = "%s/taxa_plots/" % output_dir
run_summarize_taxa_through_plots(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=taxa_plots_output_dir,
mapping_cat=None,
sort=True,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
logger=logger,
suppress_md5=True,
status_update_callback=status_update_callback,
)
index_links.append(
(
"Taxa summary bar plots",
"%s/taxa_summary_plots/bar_charts.html" % taxa_plots_output_dir,
_index_headers["taxa_summary"],
)
)
index_links.append(
(
"Taxa summary area plots",
"%s/taxa_summary_plots/area_charts.html" % taxa_plots_output_dir,
_index_headers["taxa_summary"],
)
)
for category in categories:
taxa_plots_output_dir = "%s/taxa_plots_%s/" % (output_dir, category)
run_summarize_taxa_through_plots(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=taxa_plots_output_dir,
mapping_cat=category,
sort=True,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
logger=logger,
suppress_md5=True,
status_update_callback=status_update_callback,
)
index_links.append(
(
"Taxa summary bar plots",
"%s/taxa_summary_plots/bar_charts.html" % taxa_plots_output_dir,
_index_headers["taxa_summary_categorical"] % category,
)
)
index_links.append(
(
"Taxa summary area plots",
"%s/taxa_summary_plots/area_charts.html" % taxa_plots_output_dir,
_index_headers["taxa_summary_categorical"] % category,
)
)
if not suppress_otu_category_significance:
# OTU category significance
for category in categories:
category_signifance_fp = "%s/category_significance_%s.txt" % (output_dir, category)
try:
params_str = get_params_str(params["otu_category_significance"])
except KeyError:
params_str = ""
# Build the OTU cateogry significance command
category_significance_cmd = "otu_category_significance.py -i %s -m %s -c %s -o %s %s" % (
biom_fp,
mapping_fp,
category,
category_signifance_fp,
params_str,
)
commands.append([("OTU category significance (%s)" % category, category_significance_cmd)])
index_links.append(
("Category significance (%s)" % category, category_signifance_fp, _index_headers["otu_category_sig"])
)
commands.append([("Compress the filtered BIOM table", "gzip %s" % filtered_biom_fp)])
index_links.append(
(
"Filtered BIOM table (minimum sequence count: %d)" % sampling_depth,
"%s.gz" % filtered_biom_fp,
_index_headers["run_summary"],
)
)
command_handler(commands, status_update_callback, logger)
generate_index_page(index_links, index_fp)
def run_pick_closed_reference_otus(
input_fp,
refseqs_fp,
output_dir,
taxonomy_fp,
command_handler,
params,
qiime_config,
parallel=False,
logger=None,
suppress_md5=False,
status_update_callback=print_to_stdout):
""" Run the data preparation steps of Qiime
The steps performed by this function are:
1) Pick OTUs;
2) Build an OTU table with optional pre-defined taxonmy.
"""
# confirm that a valid otu picking method was supplied before doing
# any work
reference_otu_picking_methods = ['blast','uclust_ref','usearch61_ref']
try:
otu_picking_method = params['pick_otus']['otu_picking_method']
except KeyError:
otu_picking_method = 'uclust_ref'
assert otu_picking_method in reference_otu_picking_methods,\
"Invalid OTU picking method supplied: %s. Valid choices are: %s"\
% (otu_picking_method,' '.join(reference_otu_picking_methods))
# Prepare some variables for the later steps
input_dir, input_filename = split(input_fp)
input_basename, input_ext = splitext(input_filename)
create_dir(output_dir)
commands = []
python_exe_fp = qiime_config['python_exe_fp']
script_dir = get_qiime_scripts_dir()
if logger == 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 not suppress_md5:
log_input_md5s(logger,[input_fp,refseqs_fp,taxonomy_fp])
# Prep the OTU picking command
pick_otu_dir = '%s/%s_picked_otus' % (output_dir, otu_picking_method)
otu_fp = '%s/%s_otus.txt' % (pick_otu_dir,input_basename)
if parallel and (otu_picking_method == 'blast' or
otu_picking_method == 'uclust_ref' or
otu_picking_method == 'usearch61_ref'):
# 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['pick_otus'].copy()
if 'otu_picking_method' in d:
del d['otu_picking_method']
params_str += ' %s' % get_params_str(d)
except KeyError:
pass
otu_picking_script = 'parallel_pick_otus_%s.py' % otu_picking_method
# Build the OTU picking command
pick_otus_cmd = '%s %s/%s -i %s -o %s -r %s -T %s' %\
(python_exe_fp,
script_dir,
otu_picking_script,
input_fp,
pick_otu_dir,
refseqs_fp,
params_str)
else:
try:
params_str = get_params_str(params['pick_otus'])
except KeyError:
params_str = ''
# Since this is reference-based OTU picking we always want to
# suppress new clusters -- force it here.
params_str+= ' --suppress_new_clusters'
logger.write("Forcing --suppress_new_clusters as this is closed-reference OTU picking.\n\n")
# Build the OTU picking command
pick_otus_cmd = '%s %s/pick_otus.py -i %s -o %s -r %s -m %s %s' %\
(python_exe_fp,
script_dir,
input_fp,
pick_otu_dir,
refseqs_fp,
otu_picking_method,
params_str)
commands.append([('Pick OTUs', pick_otus_cmd)])
# Prep the OTU table building command
otu_table_fp = '%s/otu_table.biom' % output_dir
try:
params_str = get_params_str(params['make_otu_table'])
except KeyError:
params_str = ''
if taxonomy_fp:
taxonomy_str = '-t %s' % taxonomy_fp
else:
taxonomy_str = ''
# Build the OTU table building command
make_otu_table_cmd = '%s %s/make_otu_table.py -i %s %s -o %s %s' %\
(python_exe_fp, script_dir, otu_fp, taxonomy_str, otu_table_fp, params_str)
commands.append([('Make OTU table', make_otu_table_cmd)])
# 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)
def run_ampliconnoise(mapping_fp,
output_dir,
command_handler,
params,
qiime_config,
logger=None,
status_update_callback=print_to_stdout,
chimera_alpha=-3.8228,
chimera_beta=0.6200,
sff_txt_fp=None,
numnodes=2,
suppress_perseus=True,
output_filepath=None,
platform='flx',
seqnoise_resolution=None,
truncate_len=None):
""" Run the ampliconnoise pipeline
The steps performed by this function are:
1. Split input sff.txt file into one file per sample
2. Run scripts required for PyroNoise
3. Run scripts required for SeqNoise
4. Run scripts requred for Perseus (chimera removal)
5. Merge output files into one file similar to the output of split_libraries.py
output_filepath should be absolute
seqnoise_resolution should be string
environment variable PYRO_LOOKUP_FILE must be set correctly. Thus be
careful passing command handlers that don't spawn child processes, as they
may not inherit the correct environment variable setting
"""
map_data, headers, comments = parse_mapping_file(open(mapping_fp, 'U'))
create_dir(output_dir)
if seqnoise_resolution == None:
if platform == 'flx': seqnoise_resolution = '30.0'
elif platform == 'titanium': seqnoise_resolution = '25.0'
else: raise RuntimeError('seqnoise_resolution not set, and no'+\
' default for platform '+platform)
if truncate_len == None:
if platform == 'flx': truncate_len = '220'
elif platform == 'titanium': truncate_len = '400'
else: raise RuntimeError('truncate_len not set, and no'+\
' default for platform '+platform)
sample_names = [
] # these are filenames minus extension, and are sample IDs
primer_seqs = [] # same order as sample_names
bc_seqs = [] # same order as sample_names
for i in range(len(map_data)):
sample_names.append(map_data[i][headers.index('SampleID')])
bc_seqs.append(map_data[i][headers.index('BarcodeSequence')])
# don't know why don't just take off the primer now.
# but that's done later
# primer += (map_data[i][headers.index('LinkerPrimerSequence')])
# for char, bases in IUPAC_DNA_ambiguities.items():
# primer = primer.replace(char,'['+''.join(bases)+']')
primer = (map_data[i][headers.index('LinkerPrimerSequence')])
for char, bases in IUPAC_DNA_ambiguities.items():
primer = primer.replace(char, '[' + ''.join(bases) + ']')
primer_seqs.append(primer)
if len(set(primer_seqs)) != 1:
raise RuntimeError(
'Error: only one primer per mapping file supported.')
one_primer = primer_seqs[0]
commands = []
python_exe_fp = qiime_config['python_exe_fp']
script_dir = get_qiime_scripts_dir()
if logger == 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
log_input_md5s(logger, [mapping_fp, sff_txt_fp])
# execute commands in output_dir
called_dir = os.getcwd()
os.chdir(output_dir)
fh = open(os.path.join(output_dir, 'map.csv'), 'w')
for i in range(len(sample_names)):
fh.write(sample_names[i] + ',' + bc_seqs[i] + '\n')
fh.close()
# these are the fasta results, e.g. PC.636_Good.fa
# later we merge them and copy to output file
post_pyro_tail = '_' + truncate_len
if suppress_perseus == True:
fasta_result_names = [
sample_name + post_pyro_tail + '_seqnoise_cd.fa'
for sample_name in sample_names
]
else:
fasta_result_names = [sample_name + '_Good.fa' \
for sample_name in sample_names]
cmd = 'cd ' + output_dir # see also os.chdir above
commands.append([('change to output dir', cmd)])
cmd = 'echo $PYRO_LOOKUP_FILE > pyro_lookup_filepath.txt'
commands.append([('confirm pyro lookup filepath environment variable', cmd)
])
cmd = 'SplitKeys.pl '+one_primer+' map.csv < '+\
os.path.join(called_dir,sff_txt_fp)+\
' > splitkeys_log.txt 2> unassigned.fna'
commands.append([('split sff.txt via barcodes (keys)', cmd)])
for i, sample_name in enumerate(sample_names):
# Build the summarize taxonomy command
if platform == 'flx':
cmd = 'Clean360.pl '+one_primer+' '+sample_name+' < '+\
sample_name+'.raw'
commands.append([('clean flows ' + sample_name, cmd)])
# these run through the whole sff file once per sample, I think
# cmd = "FlowsFA.pl " + primer_seqs[i] + ' '+sample_name +' < '+\
# os.path.join(called_dir,sff_txt_fp)
# commands.append([('extract flows '+sample_name, cmd)])
elif platform == 'titanium':
cmd = 'CleanMinMax.pl '+one_primer+' '+sample_name+' < '+\
sample_name+'.raw'
commands.append([('clean flows ' + sample_name, cmd)])
# cmd = "FlowsMinMax.pl " + primer_seqs[i] + ' '+sample_name +' < '+\
# os.path.join(called_dir,sff_txt_fp)
# commands.append([('extract flows '+sample_name, cmd)])
else:
raise RuntimeError("platform " + platform + " not supported")
cmd = "mpirun -np "+str(numnodes)+" PyroDist -in "+\
sample_name+".dat -out "+sample_name+ " > "+sample_name+".pdout"
commands.append([('pyrodist ' + sample_name, cmd)])
cmd = "FCluster -in "+sample_name+".fdist -out "+sample_name+\
" > "+sample_name+".fcout"
commands.append([('fcluster pyrodist ' + sample_name, cmd)])
# e.g.:
# mpirun -np 2 PyroNoise -din PC.354.dat -out PC.354_pyronoise -lin
# PC.354.list -s 60.0 -c 0.01 > PC.354_pyronoise.pnout
cmd = "mpirun -np "+str(numnodes)+" PyroNoise -din "+\
sample_name+".dat -out "+\
sample_name+"_pyronoise "+"-lin "+\
sample_name+".list -s 60.0 -c 0.01 > "+\
sample_name+"_pyronoise.pnout"
commands.append([('pyronoise ' + sample_name, cmd)])
cmd = 'Parse.pl '+bc_seqs[i]+one_primer+' '+truncate_len+' < '+\
sample_name+'_pyronoise_cd.fa'+' > '+ sample_name+'_'+\
truncate_len+'.fa'
commands.append([('truncate ' + sample_name, cmd)])
# now start with post_pyro_tail
cmd = "mpirun -np "+str(numnodes)+" SeqDist -in "+\
sample_name+post_pyro_tail+\
".fa > "+sample_name+post_pyro_tail+".seqdist"
commands.append([('seqdist ' + sample_name, cmd)])
cmd = "FCluster -in "+sample_name+post_pyro_tail+".seqdist -out "+\
sample_name+post_pyro_tail+"fcl > "+\
sample_name+post_pyro_tail+".fcout"
commands.append([('fcluster seqdist ' + sample_name, cmd)])
# e.g.:
# mpirun -np 2 SeqNoise -in PC.354_pyronoise_cd.fa -din
# PC.354_pyronoise_cd.seqdist -out PC.354_pyronoise_cd_seqnoise -lin
# PC.354_pyronoise_cdfcl.list -min PC.354_pyronoise.mapping -s 30.0 -c 0.08 >
# PC.354_pyronoise_cd.snout
cmd = "mpirun -np "+str(numnodes)+" SeqNoise -in "+\
sample_name+post_pyro_tail+\
".fa -din "+sample_name+post_pyro_tail+".seqdist -out "+\
sample_name+post_pyro_tail+\
"_seqnoise -lin "+sample_name+post_pyro_tail+'fcl.list -min '+\
sample_name+'_pyronoise'+\
'.mapping -s '+seqnoise_resolution+' -c 0.08 > '+\
sample_name+post_pyro_tail+'.snout'
commands.append([('seqnoise ' + sample_name, cmd)])
if suppress_perseus == False:
cmd = 'Perseus -sin '+sample_name+post_pyro_tail+\
'_seqnoise_cd.fa > ' +\
sample_name+'.per'
commands.append([('Perseus ' + sample_name, cmd)])
cmd = 'Class.pl '+sample_name+'.per '+\
str(chimera_alpha) + ' '+ str(chimera_beta)+\
' > '+sample_name+'.class'
commands.append([('Class.pl ' + sample_name, cmd)])
cmd = 'FilterGoodClass.pl '+sample_name+post_pyro_tail+\
'_seqnoise_cd.fa '+\
sample_name+'.class 0.5 > '+sample_name+'_Chi.fa 2> '+\
sample_name+'_Good.fa'
commands.append([('FilterGoodClass ' + sample_name, cmd)])
cmd = '%s %s/unweight_fasta.py -i %s -o %s -l %s' %\
(python_exe_fp, script_dir, fasta_result_names[i],
sample_name+'_unw.fna', sample_name)
commands.append([('unweight fasta ' + sample_name, cmd)])
cmd = 'cat ' +\
' '.join([sample_name+'_unw.fna' for sample_name in sample_names]) +\
' > ' + output_filepath # this should be an abs filepath
commands.append([('cat into one fasta file', cmd)])
# 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)
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,
minimum_failure_threshold=100000):
""" 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/usearch/sortmerna+sumaclust for otu picking
allowed_denovo_otu_picking_methods = ['uclust', 'usearch61', 'sumaclust']
allowed_reference_otu_picking_methods = ['uclust_ref', 'usearch61_ref',
'sortmerna']
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 = []
# name the final otu map
merged_otu_map_fp = '%s/final_otu_map.txt' % output_dir
# count number of sequences in step 1 failures fasta file
with open(abspath(step1_failures_fasta_fp), 'U') as step1_failures_fasta_f:
num_failure_seqs, mean, std = count_seqs_from_file(step1_failures_fasta_f)
# number of failures sequences is greater than the threshold,
# continue to step 2,3 and 4
run_step_2_and_3 = num_failure_seqs > minimum_failure_threshold
if run_step_2_and_3:
# Subsample the failures fasta file to retain (roughly) the
# percent_subsample
step2_dir = '%s/step2_otus/' % output_dir
create_dir(step2_dir)
step2_input_fasta_fp = \
'%s/subsampled_failures.fasta' % step2_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_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
# remove the indexed reference database from the dictionary of
# parameters as it must be forced to build a new database
# using the step2_repset_fasta_fp
if reference_otu_picking_method == 'sortmerna':
if 'sortmerna_db' in params['pick_otus']:
del params['pick_otus']['sortmerna_db']
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)])
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:
step4_dir = '%s/step4_otus/' % output_dir
if run_step_2_and_3:
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)])
failures_fp = step3_failures_fasta_fp
failures_otus_fp = 'failures_failures_otus.txt'
failures_step = 'step3'
else:
failures_fp = step1_failures_fasta_fp
failures_otus_fp = 'failures_otus.txt'
failures_step = 'step1'
step3_otu_map_fp = ""
step4_cmd = pick_denovo_otus(failures_fp,
step4_dir,
'.'.join([new_ref_set_id, 'CleanUp']),
denovo_otu_picking_method,
params,
logger)
step4_otu_map_fp = '%s/%s' % (step4_dir, failures_otus_fp)
commands.append([('Pick de novo OTUs on %s failures' % failures_step, 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, failures_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
if run_step_2_and_3:
failures_fp = step3_failures_list_fp
else:
failures_fp = step1_failures_list_fp
step3_otu_map_fp = ""
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' % (failures_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
copyfile(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.
if run_step_2_and_3:
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()
# steps 1-4 executed
if run_step_2_and_3:
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))
# only steps 1 and 4 executed
else:
logger.write('# Write non-singleton otus representative sequences from ' +
'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)
index_links.append(
('OTU taxonomic assignments',
taxonomy_fp,
_index_headers['taxa_assignments']))
# 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
table = load_table(align_and_tree_input_otu_table)
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 run_jackknifed_beta_diversity(otu_table_fp,
tree_fp,
seqs_per_sample,
output_dir,
command_handler,
params,
qiime_config,
mapping_fp,
parallel=False,
logger=None,
suppress_md5=False,
status_update_callback=print_to_stdout,
master_tree=None):
""" Run the data preparation steps of Qiime
The steps performed by this function are:
1) Compute beta diversity distance matrix from otu table (and
tree, if applicable)
2) Build rarefied OTU tables;
3) Build UPGMA tree from full distance matrix;
4) Compute distance matrics for rarefied OTU tables;
5) Build UPGMA trees from rarefied OTU table distance matrices;
5.5) Build a consensus tree from the rarefied UPGMA trees
6) Compare rarefied OTU table distance matrix UPGMA trees
to tree full UPGMA tree and write support file and newick tree
with support values as node labels.
master_tree can be 'full' or 'consensus', default full
"""
# Prepare some variables for the later steps
if master_tree is None:
master_tree = 'full'
otu_table_dir, otu_table_filename = split(otu_table_fp)
otu_table_basename, otu_table_ext = splitext(otu_table_filename)
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 not suppress_md5:
log_input_md5s(logger, [otu_table_fp, mapping_fp, tree_fp])
try:
beta_diversity_metrics = params['beta_diversity']['metrics'].split(',')
except KeyError:
beta_diversity_metrics = ['weighted_unifrac', 'unweighted_unifrac']
# Prep the beta-diversity command
try:
params_str = get_params_str(params['beta_diversity'])
except KeyError:
params_str = ''
if tree_fp:
params_str = '%s -t %s' % (params_str, tree_fp)
# Build the beta-diversity command
beta_div_cmd = 'beta_diversity.py -i %s -o %s %s' %\
(otu_table_fp, output_dir, params_str)
commands.append([
('Beta Diversity (%s)' % ', '.join(beta_diversity_metrics),
beta_div_cmd)
])
# Prep rarefaction command
rarefaction_dir = '%s/rarefaction/' % output_dir
create_dir(rarefaction_dir)
try:
params_str = get_params_str(params['multiple_rarefactions_even_depth'])
except KeyError:
params_str = ''
# Build the rarefaction command
rarefaction_cmd = \
'multiple_rarefactions_even_depth.py -i %s -d %d -o %s %s' %\
(otu_table_fp, seqs_per_sample, rarefaction_dir, params_str)
commands.append([('Rarefaction', rarefaction_cmd)])
# Begin iterating over beta diversity distance metrics, if more than one
# was provided
for beta_diversity_metric in beta_diversity_metrics:
metric_output_dir = '%s/%s/' % (output_dir, beta_diversity_metric)
distance_matrix_fp = '%s/%s_%s.txt' % \
(output_dir, beta_diversity_metric, otu_table_basename)
# Prep the hierarchical clustering command (for full distance matrix)
full_tree_fp = '%s/%s_upgma.tre' % (metric_output_dir,
otu_table_basename)
try:
params_str = get_params_str(params['upgma_cluster'])
except KeyError:
params_str = ''
# Build the hierarchical clustering command (for full distance matrix)
hierarchical_cluster_cmd = 'upgma_cluster.py -i %s -o %s %s' %\
(distance_matrix_fp, full_tree_fp, params_str)
commands.append([
('UPGMA on full distance matrix: %s' % beta_diversity_metric,
hierarchical_cluster_cmd)
])
# Prep the beta diversity command (for rarefied OTU tables)
dm_dir = '%s/rare_dm/' % metric_output_dir
create_dir(dm_dir)
# the metrics parameter needs to be ignored as we need to run
# beta_diversity one metric at a time to keep the per-metric
# output files in separate directories
try:
d = params['beta_diversity'].copy()
del d['metrics']
except KeyError:
params_str = {}
params_str = get_params_str(d) + ' -m %s ' % beta_diversity_metric
if tree_fp:
params_str = '%s -t %s' % (params_str, tree_fp)
if parallel:
params_str += ' %s' % get_params_str(params['parallel'])
# Build the parallel beta diversity command (for rarefied OTU
# tables)
beta_div_rarefied_cmd = \
'parallel_beta_diversity.py -T -i %s -o %s %s' %\
(rarefaction_dir, dm_dir, params_str)
else:
# Build the serial beta diversity command (for rarefied OTU tables)
beta_div_rarefied_cmd = \
'beta_diversity.py -i %s -o %s %s' %\
(rarefaction_dir, dm_dir, params_str)
commands.append([('Beta diversity on rarefied OTU tables (%s)' %
beta_diversity_metric, beta_div_rarefied_cmd)])
# Prep the hierarchical clustering command (for rarefied
# distance matrices)
upgma_dir = '%s/rare_upgma/' % metric_output_dir
create_dir(upgma_dir)
try:
params_str = get_params_str(params['upgma_cluster'])
except KeyError:
params_str = ''
# Build the hierarchical clustering command (for rarefied
# distance matrices)
hierarchical_cluster_cmd =\
'upgma_cluster.py -i %s -o %s %s' % (dm_dir, upgma_dir, params_str)
commands.append([
('UPGMA on rarefied distance matrix (%s)' % beta_diversity_metric,
hierarchical_cluster_cmd)
])
# Build the consensus tree command
consensus_tree_cmd =\
'consensus_tree.py -i %s -o %s %s' %\
(upgma_dir, metric_output_dir + "/rare_upgma_consensus.tre",
params_str)
commands.append([('consensus on rarefied distance matrices (%s)' %
beta_diversity_metric, consensus_tree_cmd)])
# Prep the tree compare command
tree_compare_dir = '%s/upgma_cmp/' % metric_output_dir
create_dir(tree_compare_dir)
try:
params_str = get_params_str(params['tree_compare'])
except KeyError:
params_str = ''
# Build the tree compare command
if master_tree == "full":
master_tree_fp = full_tree_fp
elif master_tree == "consensus":
master_tree_fp = metric_output_dir + "/rare_upgma_consensus.tre"
else:
raise RuntimeError('master tree method "%s" not found' %
(master_tree, ))
tree_compare_cmd = 'tree_compare.py -s %s -m %s -o %s %s' %\
(upgma_dir, master_tree_fp, tree_compare_dir, params_str)
commands.append([('Tree compare (%s)' % beta_diversity_metric,
tree_compare_cmd)])
# Prep the PCoA command
pcoa_dir = '%s/pcoa/' % metric_output_dir
create_dir(pcoa_dir)
try:
params_str = get_params_str(params['principal_coordinates'])
except KeyError:
params_str = ''
# Build the PCoA command
pcoa_cmd = 'principal_coordinates.py -i %s -o %s %s' %\
(dm_dir, pcoa_dir, params_str)
commands.append([('Principal coordinates (%s)' % beta_diversity_metric,
pcoa_cmd)])
# Prep the emperor plots command
emperor_dir = '%s/emperor_pcoa_plots/' % metric_output_dir
create_dir(emperor_dir)
try:
params_str = get_params_str(params['make_emperor'])
except KeyError:
params_str = ''
emperor_cmd = 'make_emperor.py -i %s -o %s -m %s %s' %\
(pcoa_dir, emperor_dir, mapping_fp, params_str)
commands.append([('emperor plots (%s)' % beta_diversity_metric,
emperor_cmd)])
# 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)
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,
minimum_failure_threshold=100000):
""" 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,
minimum_failure_threshold=minimum_failure_threshold)
# 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
table = load_table(align_and_tree_input_otu_table)
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 run_alpha_rarefaction(otu_table_fp,
mapping_fp,
output_dir,
command_handler,
params,
qiime_config,
tree_fp=None,
num_steps=10,
parallel=False,
logger=None,
min_rare_depth=10,
max_rare_depth=None,
suppress_md5=False,
status_update_callback=print_to_stdout,
plot_stderr_and_stddev=False,
retain_intermediate_files=True):
""" Run the data preparation steps of Qiime
The steps performed by this function are:
1) Generate rarefied OTU tables;
2) Compute alpha diversity metrics for each rarefied OTU table;
3) Collate alpha diversity results;
4) Generate alpha rarefaction plots.
"""
# Prepare some variables for the later steps
otu_table_dir, otu_table_filename = split(otu_table_fp)
otu_table_basename, otu_table_ext = splitext(otu_table_filename)
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 not suppress_md5:
log_input_md5s(logger, [otu_table_fp, mapping_fp, tree_fp])
if max_rare_depth is None:
min_count, max_count, median_count, mean_count, counts_per_sample =\
compute_counts_per_sample_stats(
load_table(otu_table_fp))
max_rare_depth = median_count
step = int((max_rare_depth - min_rare_depth) / num_steps) or 1
max_rare_depth = int(max_rare_depth)
rarefaction_dir = '%s/rarefaction/' % output_dir
create_dir(rarefaction_dir)
try:
params_str = get_params_str(params['multiple_rarefactions'])
except KeyError:
params_str = ''
if parallel:
params_str += ' %s' % get_params_str(params['parallel'])
# Build the rarefaction command
rarefaction_cmd = \
'parallel_multiple_rarefactions.py -T -i %s -m %s -x %s -s %s -o %s %s' %\
(otu_table_fp, min_rare_depth, max_rare_depth, step,
rarefaction_dir, params_str)
else:
# Build the rarefaction command
rarefaction_cmd = \
'multiple_rarefactions.py -i %s -m %s -x %s -s %s -o %s %s' %\
(otu_table_fp, min_rare_depth, max_rare_depth, step,
rarefaction_dir, params_str)
commands.append([('Alpha rarefaction', rarefaction_cmd)])
# Prep the alpha diversity command
alpha_diversity_dir = '%s/alpha_div/' % output_dir
create_dir(alpha_diversity_dir)
try:
params_str = get_params_str(params['alpha_diversity'])
except KeyError:
params_str = ''
if tree_fp:
params_str += ' -t %s' % tree_fp
if parallel:
params_str += ' %s' % get_params_str(params['parallel'])
# Build the alpha diversity command
alpha_diversity_cmd = \
"parallel_alpha_diversity.py -T -i %s -o %s %s" %\
(rarefaction_dir, alpha_diversity_dir, params_str)
else:
# Build the alpha diversity command
alpha_diversity_cmd = \
"alpha_diversity.py -i %s -o %s %s" %\
(rarefaction_dir, alpha_diversity_dir, params_str)
commands.append([('Alpha diversity on rarefied OTU tables',
alpha_diversity_cmd)])
# Prep the alpha diversity collation command
alpha_collated_dir = '%s/alpha_div_collated/' % output_dir
create_dir(alpha_collated_dir)
try:
params_str = get_params_str(params['collate_alpha'])
except KeyError:
params_str = ''
# Build the alpha diversity collation command
alpha_collated_cmd = 'collate_alpha.py -i %s -o %s %s' %\
(alpha_diversity_dir, alpha_collated_dir, params_str)
commands.append([('Collate alpha', alpha_collated_cmd)])
if not retain_intermediate_files:
commands.append([
('Removing intermediate files',
'rm -r %s %s' % (rarefaction_dir, alpha_diversity_dir))
])
else:
commands.append([('Skipping removal of intermediate files.', '')])
# Prep the make rarefaction plot command(s)
try:
params_str = get_params_str(params['make_rarefaction_plots'])
except KeyError:
params_str = ''
if 'std_type' in params[
'make_rarefaction_plots'] or not plot_stderr_and_stddev:
rarefaction_plot_dir = '%s/alpha_rarefaction_plots/' % output_dir
create_dir(rarefaction_plot_dir)
# Build the make rarefaction plot command(s)
# for metric in alpha_diversity_metrics:
make_rarefaction_plot_cmd =\
'make_rarefaction_plots.py -i %s -m %s -o %s %s' %\
(alpha_collated_dir, mapping_fp, rarefaction_plot_dir, params_str)
commands.append([('Rarefaction plot: %s' % 'All metrics',
make_rarefaction_plot_cmd)])
else:
rarefaction_plot_dir_stddev = '%s/alpha_rarefaction_plots_stddev/' % output_dir
rarefaction_plot_dir_stderr = '%s/alpha_rarefaction_plots_stderr/' % output_dir
create_dir(rarefaction_plot_dir_stddev)
create_dir(rarefaction_plot_dir_stderr)
# Build the make rarefaction plot command(s)
# for metric in alpha_diversity_metrics:
make_rarefaction_plot_cmd =\
'make_rarefaction_plots.py -i %s -m %s -o %s %s --std_type stddev' %\
(alpha_collated_dir, mapping_fp, rarefaction_plot_dir_stddev,
params_str)
commands.append([('Rarefaction plot: %s' % 'All metrics',
make_rarefaction_plot_cmd)])
make_rarefaction_plot_cmd =\
'make_rarefaction_plots.py -i %s -m %s -o %s %s --std_type stderr' %\
(alpha_collated_dir, mapping_fp, rarefaction_plot_dir_stderr,
params_str)
commands.append([('Rarefaction plot: %s' % 'All metrics',
make_rarefaction_plot_cmd)])
# 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)
def run_beta_diversity_through_plots(otu_table_fp,
mapping_fp,
output_dir,
command_handler,
params,
qiime_config,
color_by_interesting_fields_only=True,
sampling_depth=None,
tree_fp=None,
parallel=False,
logger=None,
suppress_emperor_plots=False,
suppress_md5=False,
status_update_callback=print_to_stdout):
""" Compute beta diversity distance matrices, run PCoA, and generate emperor plots
The steps performed by this function are:
1) Compute a beta diversity distance matrix for each metric
2) Peform a principal coordinates analysis on the result of step 1
3) Generate an emperor plot for each result of step 2
"""
# Prepare some variables for the later steps
otu_table_dir, otu_table_filename = split(otu_table_fp)
otu_table_basename, otu_table_ext = splitext(otu_table_filename)
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 not suppress_md5:
log_input_md5s(logger, [otu_table_fp, mapping_fp, tree_fp])
mapping_data, mapping_header, mapping_comments =\
parse_mapping_file(open(mapping_fp, 'U'))
# Get the interesting mapping fields to color by -- if none are
# interesting, take all of them. Interesting is defined as those
# which have greater than one value and fewer values than the number
# of samples
if color_by_interesting_fields_only:
mapping_fields =\
get_interesting_mapping_fields(mapping_data, mapping_header) or\
mapping_header
else:
mapping_fields = mapping_header
mapping_fields = ','.join(mapping_fields)
if sampling_depth:
# Sample the OTU table at even depth
even_sampled_otu_table_fp = '%s/%s_even%d%s' %\
(output_dir, otu_table_basename,
sampling_depth, otu_table_ext)
single_rarefaction_cmd = \
'single_rarefaction.py -i %s -o %s -d %d' %\
(otu_table_fp, even_sampled_otu_table_fp, sampling_depth)
commands.append([
('Sample OTU table at %d seqs/sample' % sampling_depth,
single_rarefaction_cmd)
])
otu_table_fp = even_sampled_otu_table_fp
otu_table_dir, otu_table_filename = split(even_sampled_otu_table_fp)
otu_table_basename, otu_table_ext = splitext(otu_table_filename)
try:
beta_diversity_metrics = params['beta_diversity']['metrics'].split(',')
except KeyError:
beta_diversity_metrics = ['weighted_unifrac', 'unweighted_unifrac']
dm_fps = []
for beta_diversity_metric in beta_diversity_metrics:
# Prep the beta-diversity command
try:
bdiv_params_copy = params['beta_diversity'].copy()
except KeyError:
bdiv_params_copy = {}
try:
del bdiv_params_copy['metrics']
except KeyError:
pass
params_str = get_params_str(bdiv_params_copy)
if tree_fp:
params_str = '%s -t %s ' % (params_str, tree_fp)
# Build the beta-diversity command
if parallel:
# Grab the parallel-specific parameters
try:
params_str += get_params_str(params['parallel'])
except KeyError:
pass
beta_div_cmd = 'parallel_beta_diversity.py -i %s -o %s --metrics %s -T %s' %\
(otu_table_fp, output_dir, beta_diversity_metric, params_str)
commands.append([('Beta Diversity (%s)' % beta_diversity_metric,
beta_div_cmd)])
else:
beta_div_cmd = 'beta_diversity.py -i %s -o %s --metrics %s %s' %\
(otu_table_fp, output_dir, beta_diversity_metric, params_str)
commands.append([('Beta Diversity (%s)' % beta_diversity_metric,
beta_div_cmd)])
orig_beta_div_fp = '%s/%s_%s.txt' % \
(output_dir, beta_diversity_metric, otu_table_basename)
beta_div_fp = '%s/%s_dm.txt' % \
(output_dir, beta_diversity_metric)
commands.append([
('Rename distance matrix (%s)' % beta_diversity_metric,
'mv %s %s' % (orig_beta_div_fp, beta_div_fp))
])
dm_fps.append((beta_diversity_metric, beta_div_fp))
# Prep the principal coordinates command
pc_fp = '%s/%s_pc.txt' % (output_dir, beta_diversity_metric)
try:
params_str = get_params_str(params['principal_coordinates'])
except KeyError:
params_str = ''
# Build the principal coordinates command
pc_cmd = 'principal_coordinates.py -i %s -o %s %s' %\
(beta_div_fp, pc_fp, params_str)
commands.append([('Principal coordinates (%s)' % beta_diversity_metric,
pc_cmd)])
# Generate emperor plots
if not suppress_emperor_plots:
# Prep the emperor plots command
emperor_dir = '%s/%s_emperor_pcoa_plot/' % (output_dir,
beta_diversity_metric)
create_dir(emperor_dir)
try:
params_str = get_params_str(params['make_emperor'])
except KeyError:
params_str = ''
# Build the continuous-coloring 3d plots command
emperor_command = \
'make_emperor.py -i %s -o %s -m %s %s' % (pc_fp,
emperor_dir,
mapping_fp,
params_str)
commands.append([
('Make emperor plots, %s)' % beta_diversity_metric,
emperor_command)
])
# 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 dm_fps
def run_beta_diversity_through_plots(otu_table_fp,
mapping_fp,
output_dir,
command_handler,
params,
qiime_config,
color_by_interesting_fields_only=True,
sampling_depth=None,
histogram_categories=None,
tree_fp=None,
parallel=False,
logger=None,
suppress_3d_plots=False,
suppress_2d_plots=False,
suppress_md5=False,
status_update_callback=print_to_stdout):
""" Run the data preparation steps of Qiime
The steps performed by this function are:
1) Compute a beta diversity distance matrix;
2) Peform a principal coordinates analysis on the result of
Step 1;
3) Generate a 3D prefs file for optimized coloring of continuous
variables;
4) Generate a 3D plot for all mapping fields with colors
optimized for continuous data;
5) Generate a 3D plot for all mapping fields with colors
optimized for discrete data.
"""
# Prepare some variables for the later steps
otu_table_dir, otu_table_filename = split(otu_table_fp)
otu_table_basename, otu_table_ext = splitext(otu_table_filename)
create_dir(output_dir)
commands = []
python_exe_fp = qiime_config['python_exe_fp']
script_dir = get_qiime_scripts_dir()
if logger == 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 not suppress_md5:
log_input_md5s(logger, [otu_table_fp, mapping_fp, tree_fp])
mapping_data, mapping_header, mapping_comments =\
parse_mapping_file(open(mapping_fp,'U'))
if histogram_categories:
invalid_categories = set(histogram_categories) - set(mapping_header)
if invalid_categories:
raise ValueError,\
"Invalid histogram categories - these must exactly match "+\
"mapping file column headers: %s" % (' '.join(invalid_categories))
# Get the interesting mapping fields to color by -- if none are
# interesting, take all of them. Interesting is defined as those
# which have greater than one value and fewer values than the number
# of samples
if color_by_interesting_fields_only:
mapping_fields =\
get_interesting_mapping_fields(mapping_data, mapping_header) or\
mapping_header
else:
mapping_fields = mapping_header
mapping_fields = ','.join(mapping_fields)
if sampling_depth:
# Sample the OTU table at even depth
even_sampled_otu_table_fp = '%s/%s_even%d%s' %\
(output_dir, otu_table_basename,
sampling_depth, otu_table_ext)
single_rarefaction_cmd = \
'%s %s/single_rarefaction.py -i %s -o %s -d %d' %\
(python_exe_fp, script_dir, otu_table_fp,
even_sampled_otu_table_fp, sampling_depth)
commands.append([
('Sample OTU table at %d seqs/sample' % sampling_depth,
single_rarefaction_cmd)
])
otu_table_fp = even_sampled_otu_table_fp
otu_table_dir, otu_table_filename = split(even_sampled_otu_table_fp)
otu_table_basename, otu_table_ext = splitext(otu_table_filename)
try:
beta_diversity_metrics = params['beta_diversity']['metrics'].split(',')
except KeyError:
beta_diversity_metrics = ['weighted_unifrac', 'unweighted_unifrac']
# Prep the 3d prefs file generator command
prefs_fp = '%s/prefs.txt' % output_dir
try:
params_str = get_params_str(params['make_prefs_file'])
except KeyError:
params_str = ''
if not 'mapping_headers_to_use' in params['make_prefs_file']:
params_str = '%s --mapping_headers_to_use %s' \
% (params_str,mapping_fields)
# Build the 3d prefs file generator command
prefs_cmd = \
'%s %s/make_prefs_file.py -m %s -o %s %s' %\
(python_exe_fp, script_dir, mapping_fp, prefs_fp, params_str)
commands.append([('Build prefs file', prefs_cmd)])
dm_fps = []
for beta_diversity_metric in beta_diversity_metrics:
# Prep the beta-diversity command
try:
bdiv_params_copy = params['beta_diversity'].copy()
except KeyError:
bdiv_params_copy = {}
try:
del bdiv_params_copy['metrics']
except KeyError:
pass
params_str = get_params_str(bdiv_params_copy)
if tree_fp:
params_str = '%s -t %s ' % (params_str, tree_fp)
# Build the beta-diversity command
if parallel:
# Grab the parallel-specific parameters
try:
params_str += get_params_str(params['parallel'])
except KeyError:
pass
beta_div_cmd = '%s %s/parallel_beta_diversity.py -i %s -o %s --metrics %s -T %s' %\
(python_exe_fp, script_dir, otu_table_fp,
output_dir, beta_diversity_metric, params_str)
commands.append(\
[('Beta Diversity (%s)' % beta_diversity_metric, beta_div_cmd)])
else:
beta_div_cmd = '%s %s/beta_diversity.py -i %s -o %s --metrics %s %s' %\
(python_exe_fp, script_dir, otu_table_fp,
output_dir, beta_diversity_metric, params_str)
commands.append(\
[('Beta Diversity (%s)' % beta_diversity_metric, beta_div_cmd)])
orig_beta_div_fp = '%s/%s_%s.txt' % \
(output_dir, beta_diversity_metric, otu_table_basename)
beta_div_fp = '%s/%s_dm.txt' % \
(output_dir, beta_diversity_metric)
commands.append([
('Rename distance matrix (%s)' % beta_diversity_metric,
'mv %s %s' % (orig_beta_div_fp, beta_div_fp))
])
dm_fps.append((beta_diversity_metric, beta_div_fp))
# Prep the principal coordinates command
pc_fp = '%s/%s_pc.txt' % (output_dir, beta_diversity_metric)
try:
params_str = get_params_str(params['principal_coordinates'])
except KeyError:
params_str = ''
# Build the principal coordinates command
pc_cmd = '%s %s/principal_coordinates.py -i %s -o %s %s' %\
(python_exe_fp, script_dir, beta_div_fp, pc_fp, params_str)
commands.append(\
[('Principal coordinates (%s)' % beta_diversity_metric, pc_cmd)])
# Generate 3d plots
if not suppress_3d_plots:
# Prep the continuous-coloring 3d plots command
continuous_3d_dir = '%s/%s_3d_continuous/' %\
(output_dir, beta_diversity_metric)
create_dir(continuous_3d_dir)
try:
params_str = get_params_str(params['make_3d_plots'])
except KeyError:
params_str = ''
# Build the continuous-coloring 3d plots command
continuous_3d_command = \
'%s %s/make_3d_plots.py -p %s -i %s -o %s -m %s %s' %\
(python_exe_fp, script_dir, prefs_fp, pc_fp, continuous_3d_dir,
mapping_fp, params_str)
# Prep the discrete-coloring 3d plots command
discrete_3d_dir = '%s/%s_3d_discrete/' %\
(output_dir, beta_diversity_metric)
create_dir(discrete_3d_dir)
try:
params_str = get_params_str(params['make_3d_plots'])
except KeyError:
params_str = ''
# Build the discrete-coloring 3d plots command
discrete_3d_command = \
'%s %s/make_3d_plots.py -b "%s" -i %s -o %s -m %s %s' %\
(python_exe_fp, script_dir, mapping_fields, pc_fp, discrete_3d_dir,
mapping_fp, params_str)
commands.append([\
('Make 3D plots (continuous coloring, %s)' %\
beta_diversity_metric,continuous_3d_command),\
('Make 3D plots (discrete coloring, %s)' %\
beta_diversity_metric,discrete_3d_command,)])
# Generate 3d plots
if not suppress_2d_plots:
# Prep the continuous-coloring 3d plots command
continuous_2d_dir = '%s/%s_2d_continuous/' %\
(output_dir, beta_diversity_metric)
create_dir(continuous_2d_dir)
try:
params_str = get_params_str(params['make_2d_plots'])
except KeyError:
params_str = ''
# Build the continuous-coloring 3d plots command
continuous_2d_command = \
'%s %s/make_2d_plots.py -p %s -i %s -o %s -m %s %s' %\
(python_exe_fp, script_dir, prefs_fp, pc_fp, continuous_2d_dir,
mapping_fp, params_str)
# Prep the discrete-coloring 3d plots command
discrete_2d_dir = '%s/%s_2d_discrete/' %\
(output_dir, beta_diversity_metric)
create_dir(discrete_2d_dir)
try:
params_str = get_params_str(params['make_2d_plots'])
except KeyError:
params_str = ''
# Build the discrete-coloring 2d plots command
discrete_2d_command = \
'%s %s/make_2d_plots.py -b "%s" -i %s -o %s -m %s %s' %\
(python_exe_fp, script_dir, mapping_fields, pc_fp, discrete_2d_dir,
mapping_fp, params_str)
commands.append([\
('Make 2D plots (continuous coloring, %s)' %\
beta_diversity_metric,continuous_2d_command),\
('Make 2D plots (discrete coloring, %s)' %\
beta_diversity_metric,discrete_2d_command,)])
if histogram_categories:
# Prep the discrete-coloring 3d plots command
histograms_dir = '%s/%s_histograms/' %\
(output_dir, beta_diversity_metric)
create_dir(histograms_dir)
try:
params_str = get_params_str(params['make_distance_histograms'])
except KeyError:
params_str = ''
# Build the make_distance_histograms command
distance_histograms_command = \
'%s %s/make_distance_histograms.py -d %s -o %s -m %s -f "%s" %s' %\
(python_exe_fp, script_dir, beta_div_fp,
histograms_dir, mapping_fp,
','.join(histogram_categories), params_str)
commands.append([\
('Make Distance Histograms (%s)' %\
beta_diversity_metric,distance_histograms_command)])
# 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 dm_fps
def run_beta_diversity_through_plots(otu_table_fp,
mapping_fp,
output_dir,
command_handler,
params,
qiime_config,
color_by_interesting_fields_only=True,
sampling_depth=None,
histogram_categories=None,
tree_fp=None,
parallel=False,
logger=None,
suppress_3d_plots=False,
suppress_2d_plots=False,
suppress_md5=False,
status_update_callback=print_to_stdout):
""" Run the data preparation steps of Qiime
The steps performed by this function are:
1) Compute a beta diversity distance matrix;
2) Peform a principal coordinates analysis on the result of
Step 1;
3) Generate a 3D prefs file for optimized coloring of continuous
variables;
4) Generate a 3D plot for all mapping fields with colors
optimized for continuous data;
5) Generate a 3D plot for all mapping fields with colors
optimized for discrete data.
"""
# Prepare some variables for the later steps
otu_table_dir, otu_table_filename = split(otu_table_fp)
otu_table_basename, otu_table_ext = splitext(otu_table_filename)
create_dir(output_dir)
commands = []
python_exe_fp = qiime_config['python_exe_fp']
script_dir = get_qiime_scripts_dir()
if logger == 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 not suppress_md5:
log_input_md5s(logger,[otu_table_fp,mapping_fp,tree_fp])
mapping_data, mapping_header, mapping_comments =\
parse_mapping_file(open(mapping_fp,'U'))
if histogram_categories:
invalid_categories = set(histogram_categories) - set(mapping_header)
if invalid_categories:
raise ValueError,\
"Invalid histogram categories - these must exactly match "+\
"mapping file column headers: %s" % (' '.join(invalid_categories))
# Get the interesting mapping fields to color by -- if none are
# interesting, take all of them. Interesting is defined as those
# which have greater than one value and fewer values than the number
# of samples
if color_by_interesting_fields_only:
mapping_fields =\
get_interesting_mapping_fields(mapping_data, mapping_header) or\
mapping_header
else:
mapping_fields = mapping_header
mapping_fields = ','.join(mapping_fields)
if sampling_depth:
# Sample the OTU table at even depth
even_sampled_otu_table_fp = '%s/%s_even%d%s' %\
(output_dir, otu_table_basename,
sampling_depth, otu_table_ext)
single_rarefaction_cmd = \
'%s %s/single_rarefaction.py -i %s -o %s -d %d' %\
(python_exe_fp, script_dir, otu_table_fp,
even_sampled_otu_table_fp, sampling_depth)
commands.append([
('Sample OTU table at %d seqs/sample' % sampling_depth,
single_rarefaction_cmd)])
otu_table_fp = even_sampled_otu_table_fp
otu_table_dir, otu_table_filename = split(even_sampled_otu_table_fp)
otu_table_basename, otu_table_ext = splitext(otu_table_filename)
try:
beta_diversity_metrics = params['beta_diversity']['metrics'].split(',')
except KeyError:
beta_diversity_metrics = ['weighted_unifrac','unweighted_unifrac']
# Prep the 3d prefs file generator command
prefs_fp = '%s/prefs.txt' % output_dir
try:
params_str = get_params_str(params['make_prefs_file'])
except KeyError:
params_str = ''
if not 'mapping_headers_to_use' in params['make_prefs_file']:
params_str = '%s --mapping_headers_to_use %s' \
% (params_str,mapping_fields)
# Build the 3d prefs file generator command
prefs_cmd = \
'%s %s/make_prefs_file.py -m %s -o %s %s' %\
(python_exe_fp, script_dir, mapping_fp, prefs_fp, params_str)
commands.append([('Build prefs file', prefs_cmd)])
dm_fps = []
for beta_diversity_metric in beta_diversity_metrics:
# Prep the beta-diversity command
try:
bdiv_params_copy = params['beta_diversity'].copy()
except KeyError:
bdiv_params_copy = {}
try:
del bdiv_params_copy['metrics']
except KeyError:
pass
params_str = get_params_str(bdiv_params_copy)
if tree_fp:
params_str = '%s -t %s ' % (params_str,tree_fp)
# Build the beta-diversity command
if parallel:
# Grab the parallel-specific parameters
try:
params_str += get_params_str(params['parallel'])
except KeyError:
pass
beta_div_cmd = '%s %s/parallel_beta_diversity.py -i %s -o %s --metrics %s -T %s' %\
(python_exe_fp, script_dir, otu_table_fp,
output_dir, beta_diversity_metric, params_str)
commands.append(\
[('Beta Diversity (%s)' % beta_diversity_metric, beta_div_cmd)])
else:
beta_div_cmd = '%s %s/beta_diversity.py -i %s -o %s --metrics %s %s' %\
(python_exe_fp, script_dir, otu_table_fp,
output_dir, beta_diversity_metric, params_str)
commands.append(\
[('Beta Diversity (%s)' % beta_diversity_metric, beta_div_cmd)])
orig_beta_div_fp = '%s/%s_%s.txt' % \
(output_dir, beta_diversity_metric, otu_table_basename)
beta_div_fp = '%s/%s_dm.txt' % \
(output_dir, beta_diversity_metric)
commands.append([('Rename distance matrix (%s)' % beta_diversity_metric,
'mv %s %s' % (orig_beta_div_fp, beta_div_fp))])
dm_fps.append((beta_diversity_metric, beta_div_fp))
# Prep the principal coordinates command
pc_fp = '%s/%s_pc.txt' % (output_dir, beta_diversity_metric)
try:
params_str = get_params_str(params['principal_coordinates'])
except KeyError:
params_str = ''
# Build the principal coordinates command
pc_cmd = '%s %s/principal_coordinates.py -i %s -o %s %s' %\
(python_exe_fp, script_dir, beta_div_fp, pc_fp, params_str)
commands.append(\
[('Principal coordinates (%s)' % beta_diversity_metric, pc_cmd)])
# Generate 3d plots
if not suppress_3d_plots:
# Prep the continuous-coloring 3d plots command
continuous_3d_dir = '%s/%s_3d_continuous/' %\
(output_dir, beta_diversity_metric)
create_dir(continuous_3d_dir)
try:
params_str = get_params_str(params['make_3d_plots'])
except KeyError:
params_str = ''
# Build the continuous-coloring 3d plots command
continuous_3d_command = \
'%s %s/make_3d_plots.py -p %s -i %s -o %s -m %s %s' %\
(python_exe_fp, script_dir, prefs_fp, pc_fp, continuous_3d_dir,
mapping_fp, params_str)
# Prep the discrete-coloring 3d plots command
discrete_3d_dir = '%s/%s_3d_discrete/' %\
(output_dir, beta_diversity_metric)
create_dir(discrete_3d_dir)
try:
params_str = get_params_str(params['make_3d_plots'])
except KeyError:
params_str = ''
# Build the discrete-coloring 3d plots command
discrete_3d_command = \
'%s %s/make_3d_plots.py -b "%s" -i %s -o %s -m %s %s' %\
(python_exe_fp, script_dir, mapping_fields, pc_fp, discrete_3d_dir,
mapping_fp, params_str)
commands.append([\
('Make 3D plots (continuous coloring, %s)' %\
beta_diversity_metric,continuous_3d_command),\
('Make 3D plots (discrete coloring, %s)' %\
beta_diversity_metric,discrete_3d_command,)])
# Generate 3d plots
if not suppress_2d_plots:
# Prep the continuous-coloring 3d plots command
continuous_2d_dir = '%s/%s_2d_continuous/' %\
(output_dir, beta_diversity_metric)
create_dir(continuous_2d_dir)
try:
params_str = get_params_str(params['make_2d_plots'])
except KeyError:
params_str = ''
# Build the continuous-coloring 3d plots command
continuous_2d_command = \
'%s %s/make_2d_plots.py -p %s -i %s -o %s -m %s %s' %\
(python_exe_fp, script_dir, prefs_fp, pc_fp, continuous_2d_dir,
mapping_fp, params_str)
# Prep the discrete-coloring 3d plots command
discrete_2d_dir = '%s/%s_2d_discrete/' %\
(output_dir, beta_diversity_metric)
create_dir(discrete_2d_dir)
try:
params_str = get_params_str(params['make_2d_plots'])
except KeyError:
params_str = ''
# Build the discrete-coloring 2d plots command
discrete_2d_command = \
'%s %s/make_2d_plots.py -b "%s" -i %s -o %s -m %s %s' %\
(python_exe_fp, script_dir, mapping_fields, pc_fp, discrete_2d_dir,
mapping_fp, params_str)
commands.append([\
('Make 2D plots (continuous coloring, %s)' %\
beta_diversity_metric,continuous_2d_command),\
('Make 2D plots (discrete coloring, %s)' %\
beta_diversity_metric,discrete_2d_command,)])
if histogram_categories:
# Prep the discrete-coloring 3d plots command
histograms_dir = '%s/%s_histograms/' %\
(output_dir, beta_diversity_metric)
create_dir(histograms_dir)
try:
params_str = get_params_str(params['make_distance_histograms'])
except KeyError:
params_str = ''
# Build the make_distance_histograms command
distance_histograms_command = \
'%s %s/make_distance_histograms.py -d %s -o %s -m %s -f "%s" %s' %\
(python_exe_fp, script_dir, beta_div_fp,
histograms_dir, mapping_fp,
','.join(histogram_categories), params_str)
commands.append([\
('Make Distance Histograms (%s)' %\
beta_diversity_metric,distance_histograms_command)])
# 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 dm_fps
def run_pick_closed_reference_otus(
input_fp,
refseqs_fp,
output_dir,
taxonomy_fp,
command_handler,
params,
qiime_config,
assign_taxonomy=False,
parallel=False,
logger=None,
suppress_md5=False,
status_update_callback=print_to_stdout):
""" Run the data preparation steps of Qiime
The steps performed by this function are:
1) Pick OTUs;
2) If assignment_taxonomy is True, choose representative sequence
for OTUs and assign taxonomy using a classifier.
3) Build an OTU table with optional predefined taxonomy
(if assign_taxonomy=False) or taxonomic assignments from step 2
(if assign_taxonomy=True).
"""
# confirm that a valid otu picking method was supplied before doing
# any work
reference_otu_picking_methods = ['blast', 'uclust_ref', 'usearch61_ref',
'usearch_ref', 'sortmerna']
try:
otu_picking_method = params['pick_otus']['otu_picking_method']
except KeyError:
otu_picking_method = 'uclust_ref'
assert otu_picking_method in reference_otu_picking_methods,\
"Invalid OTU picking method supplied: %s. Valid choices are: %s"\
% (otu_picking_method, ' '.join(reference_otu_picking_methods))
# Prepare some variables for the later steps
input_dir, input_filename = split(input_fp)
input_basename, input_ext = splitext(input_filename)
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 not suppress_md5:
log_input_md5s(logger, [input_fp, refseqs_fp, taxonomy_fp])
# Prep the OTU picking command
pick_otu_dir = '%s/%s_picked_otus' % (output_dir, otu_picking_method)
otu_fp = '%s/%s_otus.txt' % (pick_otu_dir, input_basename)
if parallel and (otu_picking_method == 'blast' or
otu_picking_method == 'uclust_ref' or
otu_picking_method == 'usearch61_ref' or
otu_picking_method == 'sortmerna'):
# 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['pick_otus'].copy()
if 'otu_picking_method' in d:
del d['otu_picking_method']
params_str += ' %s' % get_params_str(d)
except KeyError:
pass
otu_picking_script = 'parallel_pick_otus_%s.py' % otu_picking_method
# Build the OTU picking command
pick_otus_cmd = '%s -i %s -o %s -r %s -T %s' %\
(otu_picking_script,
input_fp,
pick_otu_dir,
refseqs_fp,
params_str)
else:
try:
params_str = get_params_str(params['pick_otus'])
except KeyError:
params_str = ''
# Since this is reference-based OTU picking we always want to
# suppress new clusters -- force it here.
params_str += ' --suppress_new_clusters'
logger.write(
"Forcing --suppress_new_clusters as this is "
"closed-reference OTU picking.\n\n")
# Build the OTU picking command
pick_otus_cmd = 'pick_otus.py -i %s -o %s -r %s -m %s %s' %\
(input_fp,
pick_otu_dir,
refseqs_fp,
otu_picking_method,
params_str)
commands.append([('Pick OTUs', pick_otus_cmd)])
# Assign taxonomy using a taxonomy classifier, if request by the user.
# (Alternatively predefined taxonomic assignments will be used, if provided.)
if assign_taxonomy:
# Prep the representative set picking command
rep_set_dir = '%s/rep_set/' % output_dir
create_dir(rep_set_dir)
rep_set_fp = '%s/%s_rep_set.fasta' % (rep_set_dir, input_basename)
rep_set_log_fp = '%s/%s_rep_set.log' % (rep_set_dir, input_basename)
try:
params_str = get_params_str(params['pick_rep_set'])
except KeyError:
params_str = ''
# Build the representative set picking command
pick_rep_set_cmd = 'pick_rep_set.py -i %s -f %s -l %s -o %s %s' %\
(otu_fp, input_fp, rep_set_log_fp, rep_set_fp, params_str)
commands.append([('Pick representative set', pick_rep_set_cmd)])
# Prep the taxonomy assignment command
try:
assignment_method = params['assign_taxonomy']['assignment_method']
except KeyError:
assignment_method = 'uclust'
assign_taxonomy_dir = '%s/%s_assigned_taxonomy' %\
(output_dir, assignment_method)
taxonomy_fp = '%s/%s_rep_set_tax_assignments.txt' % \
(assign_taxonomy_dir, input_basename)
if parallel and (assignment_method == 'rdp' or
assignment_method == 'blast' or
assignment_method == 'uclust'):
# Grab the parallel-specific parameters
try:
params_str = get_params_str(params['parallel'])
except KeyError:
params_str = ''
# Grab the taxonomy assignment parameters
try:
# Want to find a cleaner strategy for this: the parallel script
# is method-specific, so doesn't take a --assignment_method
# option. This works for now though.
d = params['assign_taxonomy'].copy()
if 'assignment_method' in d:
del d['assignment_method']
params_str += ' %s' % get_params_str(d)
except KeyError:
pass
# Build the parallel taxonomy assignment command
assign_taxonomy_cmd = \
'parallel_assign_taxonomy_%s.py -i %s -o %s -T %s' %\
(assignment_method, rep_set_fp, assign_taxonomy_dir, params_str)
else:
try:
params_str = get_params_str(params['assign_taxonomy'])
except KeyError:
params_str = ''
# Build the taxonomy assignment command
assign_taxonomy_cmd = 'assign_taxonomy.py -o %s -i %s %s' %\
(assign_taxonomy_dir, rep_set_fp, params_str)
commands.append([('Assign taxonomy', assign_taxonomy_cmd)])
# Prep the OTU table building command
otu_table_fp = '%s/otu_table.biom' % output_dir
try:
params_str = get_params_str(params['make_otu_table'])
except KeyError:
params_str = ''
# If assign_taxonomy is True, this will be the path to the taxonomic
# assignment results. If assign_taxonomy is False this will be either
# the precomputed taxonomic assignments that the user passed in,
# or None.
if taxonomy_fp:
taxonomy_str = '-t %s' % taxonomy_fp
else:
taxonomy_str = ''
# Build the OTU table building command
make_otu_table_cmd = 'make_otu_table.py -i %s %s -o %s %s' %\
(otu_fp, taxonomy_str, otu_table_fp, params_str)
commands.append([('Make OTU table', make_otu_table_cmd)])
# 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)
def run_core_diversity_analyses(
biom_fp,
mapping_fp,
sampling_depth,
output_dir,
qiime_config,
command_handler=call_commands_serially,
tree_fp=None,
params=None,
categories=None,
arare_min_rare_depth=10,
arare_num_steps=10,
parallel=False,
suppress_taxa_summary=False,
suppress_beta_diversity=False,
suppress_alpha_diversity=False,
suppress_otu_category_significance=False,
status_update_callback=print_to_stdout):
"""
"""
if categories != None:
# Validate categories provided by the users
mapping_data, mapping_comments = \
parse_mapping_file_to_dict(open(mapping_fp,'U'))
metadata_map = MetadataMap(mapping_data, mapping_comments)
for c in categories:
if c not in metadata_map.CategoryNames:
raise ValueError, ("Category '%s' is not a column header "
"in your mapping file. "
"Categories are case and white space sensitive. Valid "
"choices are: (%s)" % (c,', '.join(metadata_map.CategoryNames)))
if metadata_map.hasSingleCategoryValue(c):
raise ValueError, ("Category '%s' contains only one value. "
"Categories analyzed here require at least two values." % c)
else:
categories= []
# prep some variables
if params == None:
params = parse_qiime_parameters([])
create_dir(output_dir)
index_fp = '%s/index.html' % output_dir
index_links = []
commands = []
# begin logging
old_log_fps = glob(join(output_dir,'log_20*txt'))
log_fp = generate_log_fp(output_dir)
index_links.append(('Master run log',log_fp,_index_headers['run_summary']))
for old_log_fp in old_log_fps:
index_links.append(('Previous run log',old_log_fp,_index_headers['run_summary']))
logger = WorkflowLogger(log_fp,
params=params,
qiime_config=qiime_config)
input_fps = [biom_fp,mapping_fp]
if tree_fp != None:
input_fps.append(tree_fp)
log_input_md5s(logger,input_fps)
# run 'biom summarize-table' on input BIOM table
try:
params_str = get_params_str(params['biom-summarize-table'])
except KeyError:
params_str = ''
biom_table_stats_output_fp = '%s/biom_table_summary.txt' % output_dir
if not exists(biom_table_stats_output_fp):
biom_table_summary_cmd = \
"biom summarize-table -i %s -o %s --suppress-md5 %s" % \
(biom_fp, biom_table_stats_output_fp,params_str)
commands.append([('Generate BIOM table summary',
biom_table_summary_cmd)])
else:
logger.write("Skipping 'biom summarize-table' as %s exists.\n\n" \
% biom_table_stats_output_fp)
index_links.append(('BIOM table statistics',
biom_table_stats_output_fp,
_index_headers['run_summary']))
# filter samples with fewer observations than the requested sampling_depth.
# since these get filtered for some analyses (eg beta diversity after
# even sampling) it's useful to filter them here so they're filtered
# from all analyses.
filtered_biom_fp = "%s/table_mc%d.biom" % (output_dir, sampling_depth)
if not exists(filtered_biom_fp):
filter_samples_cmd = "filter_samples_from_otu_table.py -i %s -o %s -n %d" %\
(biom_fp,filtered_biom_fp,sampling_depth)
commands.append([('Filter low sequence count samples from table (minimum sequence count: %d)' % sampling_depth,
filter_samples_cmd)])
else:
logger.write("Skipping filter_samples_from_otu_table.py as %s exists.\n\n" \
% filtered_biom_fp)
biom_fp = filtered_biom_fp
# run initial commands and reset the command list
if len(commands) > 0:
command_handler(commands,
status_update_callback,
logger,
close_logger_on_success=False)
commands = []
if not suppress_beta_diversity:
bdiv_even_output_dir = '%s/bdiv_even%d/' % (output_dir,sampling_depth)
# Need to check for the existence of any distance matrices, since the user
# can select which will be generated.
existing_dm_fps = glob('%s/*_dm.txt' % bdiv_even_output_dir)
if len(existing_dm_fps) == 0:
even_dm_fps = run_beta_diversity_through_plots(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=bdiv_even_output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
sampling_depth=sampling_depth,
tree_fp=tree_fp,
parallel=parallel,
logger=logger,
suppress_md5=True,
status_update_callback=status_update_callback)
else:
logger.write("Skipping beta_diversity_through_plots.py as %s exist(s).\n\n" \
% ', '.join(existing_dm_fps))
even_dm_fps = [(split(fp)[1].strip('_dm.txt'),fp) for fp in existing_dm_fps]
# Get make_distance_boxplots parameters
try:
params_str = get_params_str(params['make_distance_boxplots'])
except KeyError:
params_str = ''
for bdiv_metric, dm_fp in even_dm_fps:
for category in categories:
boxplots_output_dir = '%s/%s_boxplots/' % (bdiv_even_output_dir,bdiv_metric)
plot_output_fp = '%s/%s_Distances.pdf' % (boxplots_output_dir,category)
stats_output_fp = '%s/%s_Stats.txt' % (boxplots_output_dir,category)
if not exists(plot_output_fp):
boxplots_cmd = \
'make_distance_boxplots.py -d %s -f %s -o %s -m %s -n 999 %s' %\
(dm_fp, category, boxplots_output_dir, mapping_fp, params_str)
commands.append([('Boxplots (%s)' % category,
boxplots_cmd)])
else:
logger.write("Skipping make_distance_boxplots.py for %s as %s exists.\n\n" \
% (category, plot_output_fp))
index_links.append(('Distance boxplots (%s)' % bdiv_metric,
plot_output_fp,
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(('Distance boxplots statistics (%s)' % bdiv_metric,
stats_output_fp,
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(('PCoA plot (%s)' % bdiv_metric,
'%s/%s_emperor_pcoa_plot/index.html' % \
(bdiv_even_output_dir,bdiv_metric),
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(('Distance matrix (%s)' % bdiv_metric,
'%s/%s_dm.txt' % \
(bdiv_even_output_dir,bdiv_metric),
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(('Principal coordinate matrix (%s)' % bdiv_metric,
'%s/%s_pc.txt' % \
(bdiv_even_output_dir,bdiv_metric),
_index_headers['beta_diversity_even'] % sampling_depth))
if not suppress_alpha_diversity:
## Alpha rarefaction workflow
arare_full_output_dir = '%s/arare_max%d/' % (output_dir,sampling_depth)
rarefaction_plots_output_fp = \
'%s/alpha_rarefaction_plots/rarefaction_plots.html' % arare_full_output_dir
if not exists(rarefaction_plots_output_fp):
run_alpha_rarefaction(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=arare_full_output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
tree_fp=tree_fp,
num_steps=arare_num_steps,
parallel=parallel,
logger=logger,
min_rare_depth=arare_min_rare_depth,
max_rare_depth=sampling_depth,
suppress_md5=True,
status_update_callback=status_update_callback)
else:
logger.write("Skipping alpha_rarefaction.py as %s exists.\n\n" \
% rarefaction_plots_output_fp)
index_links.append(('Alpha rarefaction plots',
rarefaction_plots_output_fp,
_index_headers['alpha_diversity']))
collated_alpha_diversity_fps = \
glob('%s/alpha_div_collated/*txt' % arare_full_output_dir)
try:
params_str = get_params_str(params['compare_alpha_diversity'])
except KeyError:
params_str = ''
for category in categories:
for collated_alpha_diversity_fp in collated_alpha_diversity_fps:
alpha_metric = splitext(split(collated_alpha_diversity_fp)[1])[0]
alpha_comparison_output_fp = '%s/%s_%s.txt' % \
(arare_full_output_dir,category,alpha_metric)
if not exists(alpha_comparison_output_fp):
compare_alpha_cmd = \
'compare_alpha_diversity.py -i %s -m %s -c %s -o %s -n 999 %s' %\
(collated_alpha_diversity_fp, mapping_fp, category,
alpha_comparison_output_fp, params_str)
commands.append([('Compare alpha diversity (%s, %s)' %\
(category,alpha_metric),
compare_alpha_cmd)])
else:
logger.write("Skipping compare_alpha_diversity.py for %s as %s exists.\n\n" \
% (category, alpha_comparison_output_fp))
index_links.append(
('Alpha diversity statistics (%s, %s)' % (category,alpha_metric),
alpha_comparison_output_fp,
_index_headers['alpha_diversity']))
if not suppress_taxa_summary:
taxa_plots_output_dir = '%s/taxa_plots/' % output_dir
# need to check for existence of any html files, since the user can
# select only certain ones to be generated
existing_taxa_plot_html_fps = glob(join(output_dir,'taxa_summary_plots','*.html'))
if len(existing_taxa_plot_html_fps) == 0:
run_summarize_taxa_through_plots(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=taxa_plots_output_dir,
mapping_cat=None,
sort=True,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
logger=logger,
suppress_md5=True,
status_update_callback=status_update_callback)
else:
logger.write("Skipping summarize_taxa_through_plots.py for as %s exist(s).\n\n" \
% ', '.join(existing_taxa_plot_html_fps))
index_links.append(('Taxa summary bar plots',
'%s/taxa_summary_plots/bar_charts.html'\
% taxa_plots_output_dir,
_index_headers['taxa_summary']))
index_links.append(('Taxa summary area plots',
'%s/taxa_summary_plots/area_charts.html'\
% taxa_plots_output_dir,
_index_headers['taxa_summary']))
for category in categories:
taxa_plots_output_dir = '%s/taxa_plots_%s/' % (output_dir,category)
# need to check for existence of any html files, since the user can
# select only certain ones to be generated
existing_taxa_plot_html_fps = glob('%s/taxa_summary_plots/*.html' % taxa_plots_output_dir)
if len(existing_taxa_plot_html_fps) == 0:
run_summarize_taxa_through_plots(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=taxa_plots_output_dir,
mapping_cat=category,
sort=True,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
logger=logger,
suppress_md5=True,
status_update_callback=status_update_callback)
else:
logger.write("Skipping summarize_taxa_through_plots.py for %s as %s exist(s).\n\n" \
% (category, ', '.join(existing_taxa_plot_html_fps)))
index_links.append(('Taxa summary bar plots',
'%s/taxa_summary_plots/bar_charts.html'\
% taxa_plots_output_dir,
_index_headers['taxa_summary_categorical'] % category))
index_links.append(('Taxa summary area plots',
'%s/taxa_summary_plots/area_charts.html'\
% taxa_plots_output_dir,
_index_headers['taxa_summary_categorical'] % category))
if not suppress_otu_category_significance:
try:
params_str = get_params_str(params['otu_category_significance'])
except KeyError:
params_str = ''
# OTU category significance
for category in categories:
category_signifance_fp = \
'%s/category_significance_%s.txt' % (output_dir, category)
if not exists(category_signifance_fp):
# Build the OTU cateogry significance command
category_significance_cmd = \
'otu_category_significance.py -i %s -m %s -c %s -o %s %s' %\
(biom_fp, mapping_fp, category,
category_signifance_fp, params_str)
commands.append([('OTU category significance (%s)' % category,
category_significance_cmd)])
else:
logger.write("Skipping otu_category_significance.py for %s as %s exists.\n\n" \
% (category, category_signifance_fp))
index_links.append(('Category significance (%s)' % category,
category_signifance_fp,
_index_headers['otu_category_sig']))
filtered_biom_gzip_fp = '%s.gz' % filtered_biom_fp
if not exists(filtered_biom_gzip_fp):
commands.append([('Compress the filtered BIOM table','gzip %s' % filtered_biom_fp)])
index_links.append(('Filtered BIOM table (minimum sequence count: %d)' % sampling_depth,
filtered_biom_gzip_fp,
_index_headers['run_summary']))
else:
logger.write("Skipping compressing of filtered BIOM table as %s exists.\n\n" \
% filtered_biom_gzip_fp)
if len(commands) > 0:
command_handler(commands, status_update_callback, logger)
else:
logger.close()
generate_index_page(index_links,index_fp)
def run_summarize_taxa_through_plots(otu_table_fp,
mapping_fp,
output_dir,
mapping_cat,
sort,
command_handler,
params,
qiime_config,
logger=None,
suppress_md5=False,
status_update_callback=print_to_stdout):
""" Run the data preparation for summarizing taxonomies and generating plots
The steps performed by this function are:
1) Summarize OTU by Category
2) Summarize Taxonomy
3) Plot Taxonomy Summary
"""
# Prepare some variables for the later steps
otu_table_dir, otu_table_filename = split(otu_table_fp)
otu_table_basename, otu_table_ext = splitext(otu_table_filename)
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 not suppress_md5:
log_input_md5s(logger, [otu_table_fp, mapping_fp])
# if mapping category not passed via command-line,
# check if it is passed in params file
if not mapping_cat:
try:
mapping_cat = params['collapse_samples']['collapse_fields']
except:
mapping_cat = None
try:
params_str = get_params_str(params['collapse_samples'])
# Need to remove the mapping category option, since it is defined above.
# Using this method since we don't want to change the params dict
split_params = params_str.split('--')
updated_params_str = []
for i in split_params:
if not i.startswith('collapse_fields'):
updated_params_str.append(i)
params_str = '--'.join(updated_params_str)
except:
params_str = ''
if mapping_cat:
base_filename = mapping_cat.replace(' ', '-').replace(',', '')
output_biom_fp = join(output_dir, '%s_otu_table.biom' % base_filename)
output_map_fp = join(output_dir, '%s_map.txt' % base_filename)
# Build the collapse samples command
collapse_samples_cmd = \
"collapse_samples.py -m %s -b %s --output_biom_fp %s --output_mapping_fp %s --collapse_fields '%s' %s" %\
(mapping_fp, otu_table_fp, output_biom_fp, output_map_fp, mapping_cat, params_str)
commands.append([('Collapse samples in OTU table by categories',
collapse_samples_cmd)])
otu_table_fp = output_biom_fp
# Build the sort OTU table command
if sort:
# Prep the sort_otu_table command
try:
params_str = get_params_str(params['sort_otu_table'])
except:
params_str = ''
# define output otu table
sorted_fp = join(output_dir,
splitext(split(otu_table_fp)[-1])[0] + '_sorted.biom')
if mapping_cat or params_str == '':
# for this case we don't have a collapsed mapping file so must
# handle separately
sort_otu_table_cmd = \
"sort_otu_table.py -i %s -o %s" % (otu_table_fp, sorted_fp)
else:
sort_otu_table_cmd = \
"sort_otu_table.py -i %s -o %s -m %s %s" %\
(otu_table_fp, sorted_fp, mapping_fp, params_str)
commands.append([('Sort OTU Table', sort_otu_table_cmd)])
# redefine otu_table_fp to use
otu_table_fp = sorted_fp
# Prep the summarize taxonomy command
try:
params_str = get_params_str(params['summarize_taxa'])
except:
params_str = ''
try:
sum_taxa_levels = params['summarize_taxa']['level']
except:
sum_taxa_levels = None
# Build the summarize taxonomy command
summarize_taxa_cmd = 'summarize_taxa.py -i %s -o %s %s' %\
(otu_table_fp, output_dir, params_str)
commands.append([('Summarize Taxonomy', summarize_taxa_cmd)])
sum_taxa_fps = []
if sum_taxa_levels:
basename = join(output_dir, splitext(split(otu_table_fp)[-1])[0])
for i in sum_taxa_levels.split(','):
sum_taxa_fps.append(basename + '_L%s.txt' % (str(i)))
else:
basename = join(output_dir, splitext(split(otu_table_fp)[-1])[0])
# this is the default levels from summarize_taxa, but cannot import
# script to get these values
for i in [2, 3, 4, 5, 6]:
sum_taxa_fps.append(basename + '_L%s.txt' % (str(i)))
# Prep the plot taxa summary plot command(s)
taxa_summary_plots_dir = '%s/taxa_summary_plots/' % output_dir
create_dir(taxa_summary_plots_dir)
try:
params_str = get_params_str(params['plot_taxa_summary'])
except:
params_str = ''
# Build the plot taxa summary plot command(s)
plot_taxa_summary_cmd =\
'plot_taxa_summary.py -i %s -o %s %s' %\
(','.join(sum_taxa_fps), taxa_summary_plots_dir, params_str)
commands.append([('Plot Taxonomy Summary', plot_taxa_summary_cmd)])
# 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)
def run_ampliconnoise(
mapping_fp,
output_dir,
command_handler,
params,
qiime_config,
logger=None,
status_update_callback=print_to_stdout,
chimera_alpha=-3.8228,
chimera_beta=0.6200,
sff_txt_fp=None,
numnodes=2,
suppress_perseus=True,
output_filepath=None,
platform="flx",
seqnoise_resolution=None,
truncate_len=None,
):
""" Run the ampliconnoise pipeline
The steps performed by this function are:
1. Split input sff.txt file into one file per sample
2. Run scripts required for PyroNoise
3. Run scripts required for SeqNoise
4. Run scripts requred for Perseus (chimera removal)
5. Merge output files into one file similar to the output of split_libraries.py
output_filepath should be absolute
seqnoise_resolution should be string
environment variable PYRO_LOOKUP_FILE must be set correctly. Thus be
careful passing command handlers that don't spawn child processes, as they
may not inherit the correct environment variable setting
"""
map_data, headers, comments = parse_mapping_file(open(mapping_fp, "U"))
create_dir(output_dir)
if seqnoise_resolution is None:
if platform == "flx":
seqnoise_resolution = "30.0"
elif platform == "titanium":
seqnoise_resolution = "25.0"
else:
raise RuntimeError("seqnoise_resolution not set, and no" + " default for platform " + platform)
if truncate_len is None:
if platform == "flx":
truncate_len = "220"
elif platform == "titanium":
truncate_len = "400"
else:
raise RuntimeError("truncate_len not set, and no" + " default for platform " + platform)
# these are filenames minus extension, and are sample IDs
sample_names = []
primer_seqs = [] # same order as sample_names
bc_seqs = [] # same order as sample_names
for i in range(len(map_data)):
sample_names.append(map_data[i][headers.index("SampleID")])
bc_seqs.append(map_data[i][headers.index("BarcodeSequence")])
primer = map_data[i][headers.index("LinkerPrimerSequence")]
for char, bases in DNASequence.iupac_degeneracies().iteritems():
primer = primer.replace(char, "[" + "".join(bases) + "]")
primer_seqs.append(primer)
if len(set(primer_seqs)) != 1:
raise RuntimeError("Error: only one primer per mapping file supported.")
one_primer = primer_seqs[0]
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
log_input_md5s(logger, [mapping_fp, sff_txt_fp])
# execute commands in output_dir
called_dir = os.getcwd()
os.chdir(output_dir)
fh = open(os.path.join(output_dir, "map.csv"), "w")
for i in range(len(sample_names)):
fh.write(sample_names[i] + "," + bc_seqs[i] + "\n")
fh.close()
# these are the fasta results, e.g. PC.636_Good.fa
# later we merge them and copy to output file
post_pyro_tail = "_" + truncate_len
if suppress_perseus:
fasta_result_names = [sample_name + post_pyro_tail + "_seqnoise_cd.fa" for sample_name in sample_names]
else:
fasta_result_names = [sample_name + "_Good.fa" for sample_name in sample_names]
cmd = "cd " + output_dir # see also os.chdir above
commands.append([("change to output dir", cmd)])
cmd = "echo $PYRO_LOOKUP_FILE > pyro_lookup_filepath.txt"
commands.append([("confirm pyro lookup filepath environment variable", cmd)])
cmd = (
"SplitKeys.pl "
+ one_primer
+ " map.csv < "
+ os.path.join(called_dir, sff_txt_fp)
+ " > splitkeys_log.txt 2> unassigned.fna"
)
commands.append([("split sff.txt via barcodes (keys)", cmd)])
for i, sample_name in enumerate(sample_names):
# Build the summarize taxonomy command
if platform == "flx":
cmd = "Clean360.pl " + one_primer + " " + sample_name + " < " + sample_name + ".raw"
commands.append([("clean flows " + sample_name, cmd)])
# these run through the whole sff file once per sample, I think
# cmd = "FlowsFA.pl " + primer_seqs[i] + ' '+sample_name +' < '+\
# os.path.join(called_dir,sff_txt_fp)
# commands.append([('extract flows '+sample_name, cmd)])
elif platform == "titanium":
cmd = "CleanMinMax.pl " + one_primer + " " + sample_name + " < " + sample_name + ".raw"
commands.append([("clean flows " + sample_name, cmd)])
# cmd = "FlowsMinMax.pl " + primer_seqs[i] + ' '+sample_name +' < '+\
# os.path.join(called_dir,sff_txt_fp)
# commands.append([('extract flows '+sample_name, cmd)])
else:
raise RuntimeError("platform " + platform + " not supported")
cmd = (
"mpirun -np "
+ str(numnodes)
+ " PyroDist -in "
+ sample_name
+ ".dat -out "
+ sample_name
+ " > "
+ sample_name
+ ".pdout"
)
commands.append([("pyrodist " + sample_name, cmd)])
cmd = "FCluster -in " + sample_name + ".fdist -out " + sample_name + " > " + sample_name + ".fcout"
commands.append([("fcluster pyrodist " + sample_name, cmd)])
# e.g.:
# mpirun -np 2 PyroNoise -din PC.354.dat -out PC.354_pyronoise -lin
# PC.354.list -s 60.0 -c 0.01 > PC.354_pyronoise.pnout
cmd = (
"mpirun -np "
+ str(numnodes)
+ " PyroNoise -din "
+ sample_name
+ ".dat -out "
+ sample_name
+ "_pyronoise "
+ "-lin "
+ sample_name
+ ".list -s 60.0 -c 0.01 > "
+ sample_name
+ "_pyronoise.pnout"
)
commands.append([("pyronoise " + sample_name, cmd)])
cmd = (
"Parse.pl "
+ bc_seqs[i]
+ one_primer
+ " "
+ truncate_len
+ " < "
+ sample_name
+ "_pyronoise_cd.fa"
+ " > "
+ sample_name
+ "_"
+ truncate_len
+ ".fa"
)
commands.append([("truncate " + sample_name, cmd)])
# now start with post_pyro_tail
cmd = (
"mpirun -np "
+ str(numnodes)
+ " SeqDist -in "
+ sample_name
+ post_pyro_tail
+ ".fa > "
+ sample_name
+ post_pyro_tail
+ ".seqdist"
)
commands.append([("seqdist " + sample_name, cmd)])
cmd = (
"FCluster -in "
+ sample_name
+ post_pyro_tail
+ ".seqdist -out "
+ sample_name
+ post_pyro_tail
+ "fcl > "
+ sample_name
+ post_pyro_tail
+ ".fcout"
)
commands.append([("fcluster seqdist " + sample_name, cmd)])
# e.g.:
# mpirun -np 2 SeqNoise -in PC.354_pyronoise_cd.fa -din
# PC.354_pyronoise_cd.seqdist -out PC.354_pyronoise_cd_seqnoise -lin
# PC.354_pyronoise_cdfcl.list -min PC.354_pyronoise.mapping -s 30.0 -c 0.08 >
# PC.354_pyronoise_cd.snout
cmd = (
"mpirun -np "
+ str(numnodes)
+ " SeqNoise -in "
+ sample_name
+ post_pyro_tail
+ ".fa -din "
+ sample_name
+ post_pyro_tail
+ ".seqdist -out "
+ sample_name
+ post_pyro_tail
+ "_seqnoise -lin "
+ sample_name
+ post_pyro_tail
+ "fcl.list -min "
+ sample_name
+ "_pyronoise"
+ ".mapping -s "
+ seqnoise_resolution
+ " -c 0.08 > "
+ sample_name
+ post_pyro_tail
+ ".snout"
)
commands.append([("seqnoise " + sample_name, cmd)])
if not suppress_perseus:
cmd = "Perseus -sin " + sample_name + post_pyro_tail + "_seqnoise_cd.fa > " + sample_name + ".per"
commands.append([("Perseus " + sample_name, cmd)])
cmd = (
"Class.pl "
+ sample_name
+ ".per "
+ str(chimera_alpha)
+ " "
+ str(chimera_beta)
+ " > "
+ sample_name
+ ".class"
)
commands.append([("Class.pl " + sample_name, cmd)])
cmd = (
"FilterGoodClass.pl "
+ sample_name
+ post_pyro_tail
+ "_seqnoise_cd.fa "
+ sample_name
+ ".class 0.5 > "
+ sample_name
+ "_Chi.fa 2> "
+ sample_name
+ "_Good.fa"
)
commands.append([("FilterGoodClass " + sample_name, cmd)])
cmd = "unweight_fasta.py -i %s -o %s -l %s" % (fasta_result_names[i], sample_name + "_unw.fna", sample_name)
commands.append([("unweight fasta " + sample_name, cmd)])
cmd = (
"cat " + " ".join([sample_name + "_unw.fna" for sample_name in sample_names]) + " > " + output_filepath
) # this should be an abs filepath
commands.append([("cat into one fasta file", cmd)])
# 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)
def run_alpha_rarefaction(otu_table_fp,
mapping_fp,
output_dir,
command_handler,
params,
qiime_config,
tree_fp=None,
num_steps=10,
parallel=False,
logger=None,
min_rare_depth=10,
max_rare_depth=None,
suppress_md5=False,
status_update_callback=print_to_stdout,
plot_stderr_and_stddev=False,
retain_intermediate_files=True):
""" Run the data preparation steps of Qiime
The steps performed by this function are:
1) Generate rarefied OTU tables;
2) Compute alpha diversity metrics for each rarefied OTU table;
3) Collate alpha diversity results;
4) Generate alpha rarefaction plots.
"""
# Prepare some variables for the later steps
otu_table_dir, otu_table_filename = split(otu_table_fp)
otu_table_basename, otu_table_ext = splitext(otu_table_filename)
create_dir(output_dir)
commands = []
python_exe_fp = qiime_config['python_exe_fp']
script_dir = get_qiime_scripts_dir()
if logger == 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 not suppress_md5:
log_input_md5s(logger,[otu_table_fp,mapping_fp,tree_fp])
if max_rare_depth == None:
min_count, max_count, median_count, mean_count, counts_per_sample =\
compute_counts_per_sample_stats(parse_biom_table(open(otu_table_fp,'U')))
max_rare_depth = median_count
step = int((max_rare_depth - min_rare_depth) / num_steps) or 1
max_rare_depth = int(max_rare_depth)
rarefaction_dir = '%s/rarefaction/' % output_dir
create_dir(rarefaction_dir)
try:
params_str = get_params_str(params['multiple_rarefactions'])
except KeyError:
params_str = ''
if parallel:
params_str += ' %s' % get_params_str(params['parallel'])
# Build the rarefaction command
rarefaction_cmd = \
'%s %s/parallel_multiple_rarefactions.py -T -i %s -m %s -x %s -s %s -o %s %s' %\
(python_exe_fp, script_dir, otu_table_fp, min_rare_depth, max_rare_depth,
step, rarefaction_dir, params_str)
else:
# Build the rarefaction command
rarefaction_cmd = \
'%s %s/multiple_rarefactions.py -i %s -m %s -x %s -s %s -o %s %s' %\
(python_exe_fp, script_dir, otu_table_fp, min_rare_depth, max_rare_depth,
step, rarefaction_dir, params_str)
commands.append([('Alpha rarefaction', rarefaction_cmd)])
# Prep the alpha diversity command
alpha_diversity_dir = '%s/alpha_div/' % output_dir
create_dir(alpha_diversity_dir)
try:
params_str = get_params_str(params['alpha_diversity'])
except KeyError:
params_str = ''
if tree_fp:
params_str += ' -t %s' % tree_fp
if parallel:
params_str += ' %s' % get_params_str(params['parallel'])
# Build the alpha diversity command
alpha_diversity_cmd = \
"%s %s/parallel_alpha_diversity.py -T -i %s -o %s %s" %\
(python_exe_fp, script_dir, rarefaction_dir, alpha_diversity_dir,
params_str)
else:
# Build the alpha diversity command
alpha_diversity_cmd = \
"%s %s/alpha_diversity.py -i %s -o %s %s" %\
(python_exe_fp, script_dir, rarefaction_dir, alpha_diversity_dir,
params_str)
commands.append(\
[('Alpha diversity on rarefied OTU tables',alpha_diversity_cmd)])
# Prep the alpha diversity collation command
alpha_collated_dir = '%s/alpha_div_collated/' % output_dir
create_dir(alpha_collated_dir)
try:
params_str = get_params_str(params['collate_alpha'])
except KeyError:
params_str = ''
# Build the alpha diversity collation command
alpha_collated_cmd = '%s %s/collate_alpha.py -i %s -o %s %s' %\
(python_exe_fp, script_dir, alpha_diversity_dir, \
alpha_collated_dir, params_str)
commands.append([('Collate alpha',alpha_collated_cmd)])
if not retain_intermediate_files:
commands.append([('Removing intermediate files',
'rm -r %s %s' % (rarefaction_dir,alpha_diversity_dir))])
else:
commands.append([('Skipping removal of intermediate files.','')])
# Prep the make rarefaction plot command(s)
try:
params_str = get_params_str(params['make_rarefaction_plots'])
except KeyError:
params_str = ''
if 'std_type' in params['make_rarefaction_plots'] or not plot_stderr_and_stddev:
rarefaction_plot_dir = '%s/alpha_rarefaction_plots/' % output_dir
create_dir(rarefaction_plot_dir)
# Build the make rarefaction plot command(s)
#for metric in alpha_diversity_metrics:
make_rarefaction_plot_cmd =\
'%s %s/make_rarefaction_plots.py -i %s -m %s -o %s %s' %\
(python_exe_fp, script_dir, alpha_collated_dir, mapping_fp,
rarefaction_plot_dir, params_str)
commands.append(\
[('Rarefaction plot: %s' % 'All metrics',make_rarefaction_plot_cmd)])
else:
rarefaction_plot_dir_stddev = '%s/alpha_rarefaction_plots_stddev/' % output_dir
rarefaction_plot_dir_stderr = '%s/alpha_rarefaction_plots_stderr/' % output_dir
create_dir(rarefaction_plot_dir_stddev)
create_dir(rarefaction_plot_dir_stderr)
# Build the make rarefaction plot command(s)
# for metric in alpha_diversity_metrics:
make_rarefaction_plot_cmd =\
'%s %s/make_rarefaction_plots.py -i %s -m %s -o %s %s --std_type stddev' %\
(python_exe_fp, script_dir, alpha_collated_dir, mapping_fp,
rarefaction_plot_dir_stddev, params_str)
commands.append(\
[('Rarefaction plot: %s' % 'All metrics',make_rarefaction_plot_cmd)])
make_rarefaction_plot_cmd =\
'%s %s/make_rarefaction_plots.py -i %s -m %s -o %s %s --std_type stderr' %\
(python_exe_fp, script_dir, alpha_collated_dir, mapping_fp,
rarefaction_plot_dir_stderr, params_str)
commands.append(\
[('Rarefaction plot: %s' % 'All metrics',make_rarefaction_plot_cmd)])
# 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)
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,
minimum_failure_threshold=100000):
""" 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/usearch/sortmerna+sumaclust for otu picking
allowed_denovo_otu_picking_methods = ['uclust', 'usearch61', 'sumaclust']
allowed_reference_otu_picking_methods = [
'uclust_ref', 'usearch61_ref', 'sortmerna'
]
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 = []
# name the final otu map
merged_otu_map_fp = '%s/final_otu_map.txt' % output_dir
# count number of sequences in step 1 failures fasta file
with open(abspath(step1_failures_fasta_fp), 'U') as step1_failures_fasta_f:
num_failure_seqs, mean, std = count_seqs_from_file(
step1_failures_fasta_f)
# number of failures sequences is greater than the threshold,
# continue to step 2,3 and 4
run_step_2_and_3 = num_failure_seqs > minimum_failure_threshold
if run_step_2_and_3:
# Subsample the failures fasta file to retain (roughly) the
# percent_subsample
step2_dir = '%s/step2_otus/' % output_dir
create_dir(step2_dir)
step2_input_fasta_fp = \
'%s/subsampled_failures.fasta' % step2_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_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
# remove the indexed reference database from the dictionary of
# parameters as it must be forced to build a new database
# using the step2_repset_fasta_fp
if reference_otu_picking_method == 'sortmerna':
if 'sortmerna_db' in params['pick_otus']:
del params['pick_otus']['sortmerna_db']
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)])
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:
step4_dir = '%s/step4_otus/' % output_dir
if run_step_2_and_3:
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)])
failures_fp = step3_failures_fasta_fp
failures_otus_fp = 'failures_failures_otus.txt'
failures_step = 'step3'
else:
failures_fp = step1_failures_fasta_fp
failures_otus_fp = 'failures_otus.txt'
failures_step = 'step1'
step3_otu_map_fp = ""
step4_cmd = pick_denovo_otus(failures_fp, step4_dir,
'.'.join([new_ref_set_id, 'CleanUp']),
denovo_otu_picking_method, params, logger)
step4_otu_map_fp = '%s/%s' % (step4_dir, failures_otus_fp)
commands.append([('Pick de novo OTUs on %s failures' % failures_step,
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, failures_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
if run_step_2_and_3:
failures_fp = step3_failures_list_fp
else:
failures_fp = step1_failures_list_fp
step3_otu_map_fp = ""
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' % (failures_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
copyfile(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.
if run_step_2_and_3:
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()
# steps 1-4 executed
if run_step_2_and_3:
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))
# only steps 1 and 4 executed
else:
logger.write(
'# Write non-singleton otus representative sequences from ' +
'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)
index_links.append(('OTU taxonomic assignments', taxonomy_fp,
_index_headers['taxa_assignments']))
# 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
table = load_table(align_and_tree_input_otu_table)
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 run_beta_diversity_through_plots(otu_table_fp,
mapping_fp,
output_dir,
command_handler,
params,
qiime_config,
color_by_interesting_fields_only=True,
sampling_depth=None,
tree_fp=None,
parallel=False,
logger=None,
suppress_emperor_plots=False,
suppress_md5=False,
status_update_callback=print_to_stdout):
""" Compute beta diversity distance matrices, run PCoA, and generate emperor plots
The steps performed by this function are:
1) Compute a beta diversity distance matrix for each metric
2) Peform a principal coordinates analysis on the result of step 1
3) Generate an emperor plot for each result of step 2
"""
# Prepare some variables for the later steps
otu_table_dir, otu_table_filename = split(otu_table_fp)
otu_table_basename, otu_table_ext = splitext(otu_table_filename)
create_dir(output_dir)
commands = []
python_exe_fp = qiime_config['python_exe_fp']
script_dir = get_qiime_scripts_dir()
if logger == 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 not suppress_md5:
log_input_md5s(logger,[otu_table_fp,mapping_fp,tree_fp])
mapping_data, mapping_header, mapping_comments =\
parse_mapping_file(open(mapping_fp,'U'))
# Get the interesting mapping fields to color by -- if none are
# interesting, take all of them. Interesting is defined as those
# which have greater than one value and fewer values than the number
# of samples
if color_by_interesting_fields_only:
mapping_fields =\
get_interesting_mapping_fields(mapping_data, mapping_header) or\
mapping_header
else:
mapping_fields = mapping_header
mapping_fields = ','.join(mapping_fields)
if sampling_depth:
# Sample the OTU table at even depth
even_sampled_otu_table_fp = '%s/%s_even%d%s' %\
(output_dir, otu_table_basename,
sampling_depth, otu_table_ext)
single_rarefaction_cmd = \
'%s %s/single_rarefaction.py -i %s -o %s -d %d' %\
(python_exe_fp, script_dir, otu_table_fp,
even_sampled_otu_table_fp, sampling_depth)
commands.append([
('Sample OTU table at %d seqs/sample' % sampling_depth,
single_rarefaction_cmd)])
otu_table_fp = even_sampled_otu_table_fp
otu_table_dir, otu_table_filename = split(even_sampled_otu_table_fp)
otu_table_basename, otu_table_ext = splitext(otu_table_filename)
try:
beta_diversity_metrics = params['beta_diversity']['metrics'].split(',')
except KeyError:
beta_diversity_metrics = ['weighted_unifrac','unweighted_unifrac']
dm_fps = []
for beta_diversity_metric in beta_diversity_metrics:
# Prep the beta-diversity command
try:
bdiv_params_copy = params['beta_diversity'].copy()
except KeyError:
bdiv_params_copy = {}
try:
del bdiv_params_copy['metrics']
except KeyError:
pass
params_str = get_params_str(bdiv_params_copy)
if tree_fp:
params_str = '%s -t %s ' % (params_str,tree_fp)
# Build the beta-diversity command
if parallel:
# Grab the parallel-specific parameters
try:
params_str += get_params_str(params['parallel'])
except KeyError:
pass
beta_div_cmd = '%s %s/parallel_beta_diversity.py -i %s -o %s --metrics %s -T %s' %\
(python_exe_fp, script_dir, otu_table_fp,
output_dir, beta_diversity_metric, params_str)
commands.append(\
[('Beta Diversity (%s)' % beta_diversity_metric, beta_div_cmd)])
else:
beta_div_cmd = '%s %s/beta_diversity.py -i %s -o %s --metrics %s %s' %\
(python_exe_fp, script_dir, otu_table_fp,
output_dir, beta_diversity_metric, params_str)
commands.append(\
[('Beta Diversity (%s)' % beta_diversity_metric, beta_div_cmd)])
orig_beta_div_fp = '%s/%s_%s.txt' % \
(output_dir, beta_diversity_metric, otu_table_basename)
beta_div_fp = '%s/%s_dm.txt' % \
(output_dir, beta_diversity_metric)
commands.append([('Rename distance matrix (%s)' % beta_diversity_metric,
'mv %s %s' % (orig_beta_div_fp, beta_div_fp))])
dm_fps.append((beta_diversity_metric, beta_div_fp))
# Prep the principal coordinates command
pc_fp = '%s/%s_pc.txt' % (output_dir, beta_diversity_metric)
try:
params_str = get_params_str(params['principal_coordinates'])
except KeyError:
params_str = ''
# Build the principal coordinates command
pc_cmd = '%s %s/principal_coordinates.py -i %s -o %s %s' %\
(python_exe_fp, script_dir, beta_div_fp, pc_fp, params_str)
commands.append(\
[('Principal coordinates (%s)' % beta_diversity_metric, pc_cmd)])
# Generate emperor plots
if not suppress_emperor_plots:
# Prep the emperor plots command
emperor_dir = '%s/%s_emperor_pcoa_plot/' % (output_dir, beta_diversity_metric)
create_dir(emperor_dir)
try:
params_str = get_params_str(params['make_emperor'])
except KeyError:
params_str = ''
# Build the continuous-coloring 3d plots command
emperor_command = \
'make_emperor.py -i %s -o %s -m %s %s' % (pc_fp,
emperor_dir,
mapping_fp,
params_str)
commands.append([('Make emperor plots, %s)' % beta_diversity_metric,
emperor_command)])
# 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 dm_fps
def run_pick_closed_reference_otus(input_fp,
refseqs_fp,
output_dir,
taxonomy_fp,
command_handler,
params,
qiime_config,
assign_taxonomy=False,
parallel=False,
logger=None,
suppress_md5=False,
status_update_callback=print_to_stdout):
""" Run the data preparation steps of Qiime
The steps performed by this function are:
1) Pick OTUs;
2) If assignment_taxonomy is True, choose representative sequence
for OTUs and assign taxonomy using a classifier.
3) Build an OTU table with optional predefined taxonomy
(if assign_taxonomy=False) or taxonomic assignments from step 2
(if assign_taxonomy=True).
"""
# confirm that a valid otu picking method was supplied before doing
# any work
reference_otu_picking_methods = [
'blast', 'uclust_ref', 'usearch61_ref', 'usearch_ref', 'sortmerna'
]
try:
otu_picking_method = params['pick_otus']['otu_picking_method']
except KeyError:
otu_picking_method = 'uclust_ref'
assert otu_picking_method in reference_otu_picking_methods,\
"Invalid OTU picking method supplied: %s. Valid choices are: %s"\
% (otu_picking_method, ' '.join(reference_otu_picking_methods))
# Prepare some variables for the later steps
input_dir, input_filename = split(input_fp)
input_basename, input_ext = splitext(input_filename)
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 not suppress_md5:
log_input_md5s(logger, [input_fp, refseqs_fp, taxonomy_fp])
# Prep the OTU picking command
pick_otu_dir = '%s/%s_picked_otus' % (output_dir, otu_picking_method)
otu_fp = '%s/%s_otus.txt' % (pick_otu_dir, input_basename)
if parallel and (otu_picking_method == 'blast' or otu_picking_method
== 'uclust_ref' or otu_picking_method == 'usearch61_ref'):
# 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['pick_otus'].copy()
if 'otu_picking_method' in d:
del d['otu_picking_method']
params_str += ' %s' % get_params_str(d)
except KeyError:
pass
otu_picking_script = 'parallel_pick_otus_%s.py' % otu_picking_method
# Build the OTU picking command
pick_otus_cmd = '%s -i %s -o %s -r %s -T %s' %\
(otu_picking_script,
input_fp,
pick_otu_dir,
refseqs_fp,
params_str)
else:
try:
params_str = get_params_str(params['pick_otus'])
except KeyError:
params_str = ''
# Since this is reference-based OTU picking we always want to
# suppress new clusters -- force it here.
params_str += ' --suppress_new_clusters'
logger.write("Forcing --suppress_new_clusters as this is "
"closed-reference OTU picking.\n\n")
# Build the OTU picking command
pick_otus_cmd = 'pick_otus.py -i %s -o %s -r %s -m %s %s' %\
(input_fp,
pick_otu_dir,
refseqs_fp,
otu_picking_method,
params_str)
commands.append([('Pick OTUs', pick_otus_cmd)])
# Assign taxonomy using a taxonomy classifier, if request by the user.
# (Alternatively predefined taxonomic assignments will be used, if provided.)
if assign_taxonomy:
# Prep the representative set picking command
rep_set_dir = '%s/rep_set/' % output_dir
create_dir(rep_set_dir)
rep_set_fp = '%s/%s_rep_set.fasta' % (rep_set_dir, input_basename)
rep_set_log_fp = '%s/%s_rep_set.log' % (rep_set_dir, input_basename)
try:
params_str = get_params_str(params['pick_rep_set'])
except KeyError:
params_str = ''
# Build the representative set picking command
pick_rep_set_cmd = 'pick_rep_set.py -i %s -f %s -l %s -o %s %s' %\
(otu_fp, input_fp, rep_set_log_fp, rep_set_fp, params_str)
commands.append([('Pick representative set', pick_rep_set_cmd)])
# Prep the taxonomy assignment command
try:
assignment_method = params['assign_taxonomy']['assignment_method']
except KeyError:
assignment_method = 'uclust'
assign_taxonomy_dir = '%s/%s_assigned_taxonomy' %\
(output_dir, assignment_method)
taxonomy_fp = '%s/%s_rep_set_tax_assignments.txt' % \
(assign_taxonomy_dir, input_basename)
if parallel and (assignment_method == 'rdp' or assignment_method
== 'blast' or assignment_method == 'uclust'):
# Grab the parallel-specific parameters
try:
params_str = get_params_str(params['parallel'])
except KeyError:
params_str = ''
# Grab the taxonomy assignment parameters
try:
# Want to find a cleaner strategy for this: the parallel script
# is method-specific, so doesn't take a --assignment_method
# option. This works for now though.
d = params['assign_taxonomy'].copy()
if 'assignment_method' in d:
del d['assignment_method']
params_str += ' %s' % get_params_str(d)
except KeyError:
pass
# Build the parallel taxonomy assignment command
assign_taxonomy_cmd = \
'parallel_assign_taxonomy_%s.py -i %s -o %s -T %s' %\
(assignment_method, rep_set_fp, assign_taxonomy_dir, params_str)
else:
try:
params_str = get_params_str(params['assign_taxonomy'])
except KeyError:
params_str = ''
# Build the taxonomy assignment command
assign_taxonomy_cmd = 'assign_taxonomy.py -o %s -i %s %s' %\
(assign_taxonomy_dir, rep_set_fp, params_str)
commands.append([('Assign taxonomy', assign_taxonomy_cmd)])
# Prep the OTU table building command
otu_table_fp = '%s/otu_table.biom' % output_dir
try:
params_str = get_params_str(params['make_otu_table'])
except KeyError:
params_str = ''
# If assign_taxonomy is True, this will be the path to the taxonomic
# assignment results. If assign_taxonomy is False this will be either
# the precomputed taxonomic assignments that the user passed in,
# or None.
if taxonomy_fp:
taxonomy_str = '-t %s' % taxonomy_fp
else:
taxonomy_str = ''
# Build the OTU table building command
make_otu_table_cmd = 'make_otu_table.py -i %s %s -o %s %s' %\
(otu_fp, taxonomy_str, otu_table_fp, params_str)
commands.append([('Make OTU table', make_otu_table_cmd)])
# 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)
def run_jackknifed_beta_diversity(otu_table_fp,
tree_fp,
seqs_per_sample,
output_dir,
command_handler,
params,
qiime_config,
mapping_fp,
parallel=False,
logger=None,
suppress_md5=False,
status_update_callback=print_to_stdout,
master_tree=None):
""" Run the data preparation steps of Qiime
The steps performed by this function are:
1) Compute beta diversity distance matrix from otu table (and
tree, if applicable)
2) Build rarefied OTU tables;
3) Build UPGMA tree from full distance matrix;
4) Compute distance matrics for rarefied OTU tables;
5) Build UPGMA trees from rarefied OTU table distance matrices;
5.5) Build a consensus tree from the rarefied UPGMA trees
6) Compare rarefied OTU table distance matrix UPGMA trees
to tree full UPGMA tree and write support file and newick tree
with support values as node labels.
master_tree can be 'full' or 'consensus', default full
"""
# Prepare some variables for the later steps
if master_tree == None:
master_tree = 'full'
otu_table_dir, otu_table_filename = split(otu_table_fp)
otu_table_basename, otu_table_ext = splitext(otu_table_filename)
create_dir(output_dir)
commands = []
python_exe_fp = qiime_config['python_exe_fp']
script_dir = get_qiime_scripts_dir()
if logger == 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 not suppress_md5:
log_input_md5s(logger,[otu_table_fp,mapping_fp,tree_fp])
try:
beta_diversity_metrics = params['beta_diversity']['metrics'].split(',')
except KeyError:
beta_diversity_metrics = ['weighted_unifrac','unweighted_unifrac']
# Prep the beta-diversity command
try:
params_str = get_params_str(params['beta_diversity'])
except KeyError:
params_str = ''
if tree_fp:
params_str = '%s -t %s' % (params_str,tree_fp)
# Build the beta-diversity command
beta_div_cmd = '%s %s/beta_diversity.py -i %s -o %s %s' %\
(python_exe_fp, script_dir, otu_table_fp, output_dir, params_str)
commands.append(\
[('Beta Diversity (%s)' % ', '.join(beta_diversity_metrics), beta_div_cmd)])
# Prep rarefaction command
rarefaction_dir = '%s/rarefaction/' % output_dir
create_dir(rarefaction_dir)
try:
params_str = get_params_str(params['multiple_rarefactions_even_depth'])
except KeyError:
params_str = ''
# Build the rarefaction command
rarefaction_cmd = \
'%s %s/multiple_rarefactions_even_depth.py -i %s -d %d -o %s %s' %\
(python_exe_fp, script_dir, otu_table_fp, seqs_per_sample,
rarefaction_dir, params_str)
commands.append([('Rarefaction', rarefaction_cmd)])
# Begin iterating over beta diversity distance metrics, if more than one
# was provided
for beta_diversity_metric in beta_diversity_metrics:
metric_output_dir = '%s/%s/' % (output_dir, beta_diversity_metric)
distance_matrix_fp = '%s/%s_%s.txt' % \
(output_dir, beta_diversity_metric, otu_table_basename)
# Prep the hierarchical clustering command (for full distance matrix)
full_tree_fp = '%s/%s_upgma.tre' % (metric_output_dir,otu_table_basename)
try:
params_str = get_params_str(params['upgma_cluster'])
except KeyError:
params_str = ''
# Build the hierarchical clustering command (for full distance matrix)
hierarchical_cluster_cmd = '%s %s/upgma_cluster.py -i %s -o %s %s' %\
(python_exe_fp, script_dir, distance_matrix_fp, full_tree_fp, params_str)
commands.append(\
[('UPGMA on full distance matrix: %s' % beta_diversity_metric,\
hierarchical_cluster_cmd)])
# Prep the beta diversity command (for rarefied OTU tables)
dm_dir = '%s/rare_dm/' % metric_output_dir
create_dir(dm_dir)
# the metrics parameter needs to be ignored as we need to run
# beta_diversity one metric at a time to keep the per-metric
# output files in separate directories
try:
d = params['beta_diversity'].copy()
del d['metrics']
except KeyError:
params_str = {}
params_str = get_params_str(d) + ' -m %s ' % beta_diversity_metric
if tree_fp:
params_str = '%s -t %s' % (params_str,tree_fp)
if parallel:
params_str += ' %s' % get_params_str(params['parallel'])
# Build the parallel beta diversity command (for rarefied OTU tables)
beta_div_rarefied_cmd = \
'%s %s/parallel_beta_diversity.py -T -i %s -o %s %s' %\
(python_exe_fp, script_dir, rarefaction_dir, dm_dir, params_str)
else:
# Build the serial beta diversity command (for rarefied OTU tables)
beta_div_rarefied_cmd = \
'%s %s/beta_diversity.py -i %s -o %s %s' %\
(python_exe_fp, script_dir, rarefaction_dir, dm_dir, params_str)
commands.append(\
[('Beta diversity on rarefied OTU tables (%s)' % beta_diversity_metric,
beta_div_rarefied_cmd)])
# Prep the hierarchical clustering command (for rarefied
# distance matrices)
upgma_dir = '%s/rare_upgma/' % metric_output_dir
create_dir(upgma_dir)
try:
params_str = get_params_str(params['upgma_cluster'])
except KeyError:
params_str = ''
# Build the hierarchical clustering command (for rarefied
# distance matrices)
hierarchical_cluster_cmd =\
'%s %s/upgma_cluster.py -i %s -o %s %s' %\
(python_exe_fp, script_dir, dm_dir, upgma_dir, params_str)
commands.append(\
[('UPGMA on rarefied distance matrix (%s)' % beta_diversity_metric,
hierarchical_cluster_cmd)])
# Build the consensus tree command
consensus_tree_cmd =\
'%s %s/consensus_tree.py -i %s -o %s %s' %\
(python_exe_fp, script_dir, upgma_dir, metric_output_dir + "/rare_upgma_consensus.tre",
params_str)
commands.append(\
[('consensus on rarefied distance matrices (%s)' % beta_diversity_metric,
consensus_tree_cmd)])
# Prep the tree compare command
tree_compare_dir = '%s/upgma_cmp/' % metric_output_dir
create_dir(tree_compare_dir)
try:
params_str = get_params_str(params['tree_compare'])
except KeyError:
params_str = ''
# Build the tree compare command
if master_tree == "full":
master_tree_fp = full_tree_fp
elif master_tree == "consensus":
master_tree_fp = metric_output_dir + "/rare_upgma_consensus.tre"
else:
raise RuntimeError('master tree method "%s" not found' % (master_tree,))
tree_compare_cmd = '%s %s/tree_compare.py -s %s -m %s -o %s %s' %\
(python_exe_fp, script_dir, upgma_dir, master_tree_fp,
tree_compare_dir, params_str)
commands.append(\
[('Tree compare (%s)' % beta_diversity_metric, tree_compare_cmd)])
# Prep the PCoA command
pcoa_dir = '%s/pcoa/' % metric_output_dir
create_dir(pcoa_dir)
try:
params_str = get_params_str(params['principal_coordinates'])
except KeyError:
params_str = ''
# Build the PCoA command
pcoa_cmd = '%s %s/principal_coordinates.py -i %s -o %s %s' %\
(python_exe_fp, script_dir, dm_dir, pcoa_dir, params_str)
commands.append(\
[('Principal coordinates (%s)' % beta_diversity_metric, pcoa_cmd)])
# Prep the emperor plots command
emperor_dir = '%s/emperor_pcoa_plots/' % metric_output_dir
create_dir(emperor_dir)
try:
params_str = get_params_str(params['make_emperor'])
except KeyError:
params_str = ''
emperor_cmd = 'make_emperor.py -i %s -o %s -m %s %s' %\
(pcoa_dir, emperor_dir, mapping_fp, params_str)
commands.append(\
[('emperor plots (%s)' % beta_diversity_metric, emperor_cmd)])
# 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)
def run_core_diversity_analyses(biom_fp,
mapping_fp,
sampling_depth,
output_dir,
qiime_config,
command_handler=call_commands_serially,
tree_fp=None,
params=None,
categories=None,
arare_min_rare_depth=10,
arare_num_steps=10,
parallel=False,
suppress_taxa_summary=False,
suppress_beta_diversity=False,
suppress_alpha_diversity=False,
suppress_group_significance=False,
status_update_callback=print_to_stdout):
"""
"""
if categories is not None:
# Validate categories provided by the users
mapping_data, mapping_comments = \
parse_mapping_file_to_dict(open(mapping_fp, 'U'))
metadata_map = MetadataMap(mapping_data, mapping_comments)
for c in categories:
if c not in metadata_map.CategoryNames:
raise ValueError(
"Category '%s' is not a column header "
"in your mapping file. "
"Categories are case and white space sensitive. Valid "
"choices are: (%s)" %
(c, ', '.join(metadata_map.CategoryNames)))
if metadata_map.hasSingleCategoryValue(c):
raise ValueError(
"Category '%s' contains only one value. "
"Categories analyzed here require at least two values." %
c)
else:
categories = []
comma_separated_categories = ','.join(categories)
# prep some variables
if params is None:
params = parse_qiime_parameters([])
create_dir(output_dir)
index_fp = '%s/index.html' % output_dir
index_links = []
commands = []
# begin logging
old_log_fps = glob(join(output_dir, 'log_20*txt'))
log_fp = generate_log_fp(output_dir)
index_links.append(
('Master run log', log_fp, _index_headers['run_summary']))
for old_log_fp in old_log_fps:
index_links.append(
('Previous run log', old_log_fp, _index_headers['run_summary']))
logger = WorkflowLogger(log_fp, params=params, qiime_config=qiime_config)
input_fps = [biom_fp, mapping_fp]
if tree_fp is not None:
input_fps.append(tree_fp)
log_input_md5s(logger, input_fps)
# run 'biom summarize-table' on input BIOM table
try:
params_str = get_params_str(params['biom-summarize-table'])
except KeyError:
params_str = ''
biom_table_stats_output_fp = '%s/biom_table_summary.txt' % output_dir
if not exists(biom_table_stats_output_fp):
biom_table_summary_cmd = \
"biom summarize-table -i %s -o %s %s" % \
(biom_fp, biom_table_stats_output_fp, params_str)
commands.append([('Generate BIOM table summary',
biom_table_summary_cmd)])
else:
logger.write("Skipping 'biom summarize-table' as %s exists.\n\n" %
biom_table_stats_output_fp)
index_links.append(('BIOM table statistics', biom_table_stats_output_fp,
_index_headers['run_summary']))
# filter samples with fewer observations than the requested sampling_depth.
# since these get filtered for some analyses (eg beta diversity after
# even sampling) it's useful to filter them here so they're filtered
# from all analyses.
filtered_biom_fp = "%s/table_mc%d.biom" % (output_dir, sampling_depth)
if not exists(filtered_biom_fp):
filter_samples_cmd = "filter_samples_from_otu_table.py -i %s -o %s -n %d" %\
(biom_fp, filtered_biom_fp, sampling_depth)
commands.append([(
'Filter low sequence count samples from table (minimum sequence count: %d)'
% sampling_depth, filter_samples_cmd)])
else:
logger.write(
"Skipping filter_samples_from_otu_table.py as %s exists.\n\n" %
filtered_biom_fp)
biom_fp = filtered_biom_fp
# rarify the BIOM table to sampling_depth
rarefied_biom_fp = "%s/table_even%d.biom" % (output_dir, sampling_depth)
if not exists(rarefied_biom_fp):
single_rarefaction_cmd = "single_rarefaction.py -i %s -o %s -d %d" %\
(biom_fp, rarefied_biom_fp, sampling_depth)
commands.append([
('Rarify the OTU table to %d sequences/sample' % sampling_depth,
single_rarefaction_cmd)
])
else:
logger.write("Skipping single_rarefaction.py as %s exists.\n\n" %
rarefied_biom_fp)
# run initial commands and reset the command list
if len(commands) > 0:
command_handler(commands,
status_update_callback,
logger,
close_logger_on_success=False)
commands = []
if not suppress_beta_diversity:
bdiv_even_output_dir = '%s/bdiv_even%d/' % (output_dir, sampling_depth)
# Need to check for the existence of any distance matrices, since the user
# can select which will be generated.
existing_dm_fps = glob('%s/*_dm.txt' % bdiv_even_output_dir)
if len(existing_dm_fps) == 0:
even_dm_fps = run_beta_diversity_through_plots(
otu_table_fp=rarefied_biom_fp,
mapping_fp=mapping_fp,
output_dir=bdiv_even_output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
# Note: we pass sampling depth=None here as
# we rarify the BIOM table above and pass that
# in here.
sampling_depth=None,
tree_fp=tree_fp,
parallel=parallel,
logger=logger,
suppress_md5=True,
status_update_callback=status_update_callback)
else:
logger.write(
"Skipping beta_diversity_through_plots.py as %s exist(s).\n\n"
% ', '.join(existing_dm_fps))
even_dm_fps = [(split(fp)[1].strip('_dm.txt'), fp)
for fp in existing_dm_fps]
# Get make_distance_boxplots parameters
try:
params_str = get_params_str(params['make_distance_boxplots'])
except KeyError:
params_str = ''
for bdiv_metric, dm_fp in even_dm_fps:
for category in categories:
boxplots_output_dir = '%s/%s_boxplots/' % (
bdiv_even_output_dir, bdiv_metric)
plot_output_fp = '%s/%s_Distances.pdf' % (boxplots_output_dir,
category)
stats_output_fp = '%s/%s_Stats.txt' % (boxplots_output_dir,
category)
if not exists(plot_output_fp):
boxplots_cmd = \
'make_distance_boxplots.py -d %s -f %s -o %s -m %s -n 999 %s' %\
(dm_fp, category, boxplots_output_dir,
mapping_fp, params_str)
commands.append([('Boxplots (%s)' % category, boxplots_cmd)
])
else:
logger.write(
"Skipping make_distance_boxplots.py for %s as %s exists.\n\n"
% (category, plot_output_fp))
index_links.append(
('Distance boxplots (%s)' % bdiv_metric, plot_output_fp,
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(
('Distance boxplots statistics (%s)' % bdiv_metric,
stats_output_fp,
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(
('PCoA plot (%s)' % bdiv_metric,
'%s/%s_emperor_pcoa_plot/index.html' %
(bdiv_even_output_dir, bdiv_metric),
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(
('Distance matrix (%s)' % bdiv_metric,
'%s/%s_dm.txt' % (bdiv_even_output_dir, bdiv_metric),
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(
('Principal coordinate matrix (%s)' % bdiv_metric,
'%s/%s_pc.txt' % (bdiv_even_output_dir, bdiv_metric),
_index_headers['beta_diversity_even'] % sampling_depth))
if not suppress_alpha_diversity:
# Alpha rarefaction workflow
arare_full_output_dir = '%s/arare_max%d/' % (output_dir,
sampling_depth)
rarefaction_plots_output_fp = \
'%s/alpha_rarefaction_plots/rarefaction_plots.html' % arare_full_output_dir
if not exists(rarefaction_plots_output_fp):
run_alpha_rarefaction(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=arare_full_output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
tree_fp=tree_fp,
num_steps=arare_num_steps,
parallel=parallel,
logger=logger,
min_rare_depth=arare_min_rare_depth,
max_rare_depth=sampling_depth,
suppress_md5=True,
status_update_callback=status_update_callback,
retain_intermediate_files=False)
else:
logger.write("Skipping alpha_rarefaction.py as %s exists.\n\n" %
rarefaction_plots_output_fp)
index_links.append(
('Alpha rarefaction plots', rarefaction_plots_output_fp,
_index_headers['alpha_diversity']))
collated_alpha_diversity_fps = \
glob('%s/alpha_div_collated/*txt' % arare_full_output_dir)
try:
params_str = get_params_str(params['compare_alpha_diversity'])
except KeyError:
params_str = ''
if len(categories) > 0:
for collated_alpha_diversity_fp in collated_alpha_diversity_fps:
alpha_metric = splitext(
split(collated_alpha_diversity_fp)[1])[0]
compare_alpha_output_dir = '%s/compare_%s' % \
(arare_full_output_dir, alpha_metric)
if not exists(compare_alpha_output_dir):
compare_alpha_cmd = \
'compare_alpha_diversity.py -i %s -m %s -c %s -o %s -n 999 %s' %\
(collated_alpha_diversity_fp,
mapping_fp,
comma_separated_categories,
compare_alpha_output_dir,
params_str)
commands.append([
('Compare alpha diversity (%s)' % alpha_metric,
compare_alpha_cmd)
])
for category in categories:
alpha_comparison_stat_fp = '%s/%s_stats.txt' % \
(compare_alpha_output_dir, category)
alpha_comparison_boxplot_fp = '%s/%s_boxplots.pdf' % \
(compare_alpha_output_dir, category)
index_links.append(
('Alpha diversity statistics (%s, %s)' %
(category, alpha_metric),
alpha_comparison_stat_fp,
_index_headers['alpha_diversity']))
index_links.append(
('Alpha diversity boxplots (%s, %s)' %
(category, alpha_metric),
alpha_comparison_boxplot_fp,
_index_headers['alpha_diversity']))
else:
logger.write("Skipping compare_alpha_diversity.py"
" for %s as %s exists.\n\n" %
(alpha_metric, compare_alpha_output_dir))
else:
logger.write("Skipping compare_alpha_diversity.py as"
" no categories were provided.\n\n")
if not suppress_taxa_summary:
taxa_plots_output_dir = '%s/taxa_plots/' % output_dir
# need to check for existence of any html files, since the user can
# select only certain ones to be generated
existing_taxa_plot_html_fps = glob(
join(taxa_plots_output_dir, 'taxa_summary_plots', '*.html'))
if len(existing_taxa_plot_html_fps) == 0:
run_summarize_taxa_through_plots(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=taxa_plots_output_dir,
mapping_cat=None,
sort=True,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
logger=logger,
suppress_md5=True,
status_update_callback=status_update_callback)
else:
logger.write(
"Skipping summarize_taxa_through_plots.py for as %s exist(s).\n\n"
% ', '.join(existing_taxa_plot_html_fps))
index_links.append(
('Taxa summary bar plots',
'%s/taxa_summary_plots/bar_charts.html' % taxa_plots_output_dir,
_index_headers['taxa_summary']))
index_links.append(
('Taxa summary area plots',
'%s/taxa_summary_plots/area_charts.html' % taxa_plots_output_dir,
_index_headers['taxa_summary']))
for category in categories:
taxa_plots_output_dir = '%s/taxa_plots_%s/' % (output_dir,
category)
# need to check for existence of any html files, since the user can
# select only certain ones to be generated
existing_taxa_plot_html_fps = glob('%s/taxa_summary_plots/*.html' %
taxa_plots_output_dir)
if len(existing_taxa_plot_html_fps) == 0:
run_summarize_taxa_through_plots(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=taxa_plots_output_dir,
mapping_cat=category,
sort=True,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
logger=logger,
suppress_md5=True,
status_update_callback=status_update_callback)
else:
logger.write(
"Skipping summarize_taxa_through_plots.py for %s as %s exist(s).\n\n"
% (category, ', '.join(existing_taxa_plot_html_fps)))
index_links.append(
('Taxa summary bar plots',
'%s/taxa_summary_plots/bar_charts.html' %
taxa_plots_output_dir,
_index_headers['taxa_summary_categorical'] % category))
index_links.append(
('Taxa summary area plots',
'%s/taxa_summary_plots/area_charts.html' %
taxa_plots_output_dir,
_index_headers['taxa_summary_categorical'] % category))
if not suppress_group_significance:
params_str = get_params_str(params['group_significance'])
# group significance tests, aka category significance
for category in categories:
group_signifance_fp = \
'%s/group_significance_%s.txt' % (output_dir, category)
if not exists(group_signifance_fp):
# Build the OTU cateogry significance command
group_significance_cmd = \
'group_significance.py -i %s -m %s -c %s -o %s %s' %\
(rarefied_biom_fp, mapping_fp, category,
group_signifance_fp, params_str)
commands.append([('Group significance (%s)' % category,
group_significance_cmd)])
else:
logger.write(
"Skipping group_significance.py for %s as %s exists.\n\n" %
(category, group_signifance_fp))
index_links.append(
('Category significance (%s)' % category, group_signifance_fp,
_index_headers['group_significance']))
filtered_biom_gzip_fp = '%s.gz' % filtered_biom_fp
if not exists(filtered_biom_gzip_fp):
commands.append([('Compress the filtered BIOM table',
'gzip %s' % filtered_biom_fp)])
else:
logger.write(
"Skipping compressing of filtered BIOM table as %s exists.\n\n" %
filtered_biom_gzip_fp)
index_links.append(
('Filtered BIOM table (minimum sequence count: %d)' % sampling_depth,
filtered_biom_gzip_fp, _index_headers['run_summary']))
rarified_biom_gzip_fp = '%s.gz' % rarefied_biom_fp
if not exists(rarified_biom_gzip_fp):
commands.append([('Compress the rarified BIOM table',
'gzip %s' % rarefied_biom_fp)])
else:
logger.write(
"Skipping compressing of rarified BIOM table as %s exists.\n\n" %
rarified_biom_gzip_fp)
index_links.append(
('Rarified BIOM table (sampling depth: %d)' % sampling_depth,
rarified_biom_gzip_fp, _index_headers['run_summary']))
if len(commands) > 0:
command_handler(commands, status_update_callback, logger)
else:
logger.close()
generate_index_page(index_links, index_fp)
def run_summarize_taxa_through_plots(otu_table_fp,
mapping_fp,
output_dir,
mapping_cat,
sort,
command_handler,
params,
qiime_config,
logger=None,
suppress_md5=False,
status_update_callback=print_to_stdout):
""" Run the data preparation for summarizing taxonomies and generating plots
The steps performed by this function are:
1) Summarize OTU by Category
2) Summarize Taxonomy
3) Plot Taxonomy Summary
"""
# Prepare some variables for the later steps
otu_table_dir, otu_table_filename = split(otu_table_fp)
otu_table_basename, otu_table_ext = splitext(otu_table_filename)
create_dir(output_dir)
commands = []
python_exe_fp = qiime_config['python_exe_fp']
script_dir = get_qiime_scripts_dir()
if logger == 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 not suppress_md5:
log_input_md5s(logger,[otu_table_fp,mapping_fp])
# if mapping category not passed via command-line,
# check if it is passed in params file
if not mapping_cat:
try:
mapping_cat=params['summarize_otu_by_cat']['mapping_category']
except:
mapping_cat=None
try:
params_str = get_params_str(params['summarize_otu_by_cat'])
# Need to remove the mapping category option, since it is defined above.
# Using this method since we don't want to change the params dict
split_params=params_str.split('--')
updated_params_str=[]
for i in split_params:
if not i.startswith('mapping_category'):
updated_params_str.append(i)
params_str='--'.join(updated_params_str)
except:
params_str = ''
if mapping_cat:
output_fp=join(output_dir,'%s_otu_table.biom' % (mapping_cat.replace(' ','-')))
# Build the summarize otu by category command
summarize_otu_by_cat_cmd = \
"%s %s/summarize_otu_by_cat.py -m %s -i %s -o %s -c '%s' %s" %\
(python_exe_fp, script_dir, mapping_fp, otu_table_fp, output_fp,
mapping_cat, params_str)
commands.append(\
[('Summarize OTU table by Category',summarize_otu_by_cat_cmd)])
otu_table_fp=output_fp
# Build the sort OTU table command
if sort:
# Prep the sort_otu_table command
try:
params_str = get_params_str(params['sort_otu_table'])
except:
params_str = ''
# define output otu table
sorted_fp=join(output_dir,
splitext(split(otu_table_fp)[-1])[0]+'_sorted.biom')
if mapping_cat or params_str=='':
# for this case we don't have a collapsed mapping file so must
# handle separately
sort_otu_table_cmd = \
"%s %s/sort_otu_table.py -i %s -o %s" %\
(python_exe_fp, script_dir, otu_table_fp, sorted_fp)
else:
sort_otu_table_cmd = \
"%s %s/sort_otu_table.py -i %s -o %s -m %s %s" %\
(python_exe_fp, script_dir, otu_table_fp, sorted_fp,
mapping_fp, params_str)
commands.append([('Sort OTU Table',sort_otu_table_cmd)])
# redefine otu_table_fp to use
otu_table_fp=sorted_fp
# Prep the summarize taxonomy command
try:
params_str = get_params_str(params['summarize_taxa'])
except:
params_str = ''
try:
sum_taxa_levels=params['summarize_taxa']['level']
except:
sum_taxa_levels=None
# Build the summarize taxonomy command
summarize_taxa_cmd = '%s %s/summarize_taxa.py -i %s -o %s %s' %\
(python_exe_fp, script_dir, otu_table_fp, output_dir, params_str)
commands.append([('Summarize Taxonomy',summarize_taxa_cmd)])
sum_taxa_fps=[]
if sum_taxa_levels:
basename=join(output_dir,splitext(split(otu_table_fp)[-1])[0])
for i in sum_taxa_levels.split(','):
sum_taxa_fps.append(basename+'_L%s.txt' % (str(i)))
else:
basename=join(output_dir,splitext(split(otu_table_fp)[-1])[0])
# this is the default levels from summarize_taxa, but cannot import
# script to get these values
for i in [2,3,4,5,6]:
sum_taxa_fps.append(basename+'_L%s.txt' % (str(i)))
# Prep the plot taxa summary plot command(s)
taxa_summary_plots_dir = '%s/taxa_summary_plots/' % output_dir
create_dir(taxa_summary_plots_dir)
try:
params_str = get_params_str(params['plot_taxa_summary'])
except:
params_str = ''
# Build the plot taxa summary plot command(s)
plot_taxa_summary_cmd =\
'%s %s/plot_taxa_summary.py -i %s -o %s %s' %\
(python_exe_fp, script_dir, ','.join(sum_taxa_fps),
taxa_summary_plots_dir, params_str)
commands.append(\
[('Plot Taxonomy Summary',plot_taxa_summary_cmd)])
# 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)
def run_pick_de_novo_otus(input_fp,
output_dir,
command_handler,
params,
qiime_config,
parallel=False,
logger=None,
suppress_md5=False,
status_update_callback=print_to_stdout):
""" Run the data preparation steps of Qiime
The steps performed by this function are:
1) Pick OTUs;
2) Pick a representative set;
3) Align the representative set;
4) Assign taxonomy;
5) Filter the alignment prior to tree building - remove positions
which are all gaps, and specified as 0 in the lanemask
6) Build a phylogenetic tree;
7) Build an OTU table.
"""
# Prepare some variables for the later steps
input_dir, input_filename = split(input_fp)
input_basename, input_ext = splitext(input_filename)
create_dir(output_dir)
commands = []
python_exe_fp = qiime_config['python_exe_fp']
script_dir = get_qiime_scripts_dir()
cluster_failures = False
if logger == 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 not suppress_md5:
log_input_md5s(logger,[input_fp])
# Prep the OTU picking command
try:
otu_picking_method = params['pick_otus']['otu_picking_method']
except KeyError:
otu_picking_method = 'uclust'
pick_otu_dir = '%s/%s_picked_otus' % (output_dir, otu_picking_method)
otu_fp = '%s/%s_otus.txt' % (pick_otu_dir,input_basename)
if parallel and (otu_picking_method == 'blast' or
otu_picking_method == 'uclust_ref'):
# 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 --otu_picking_method
# option. This works for now though.
d = params['pick_otus'].copy()
del d['otu_picking_method']
except KeyError:
pass
if otu_picking_method == 'uclust_ref':
try:
suppress_new_clusters = d['suppress_new_clusters']
del d['suppress_new_clusters']
cluster_failures = False
except KeyError:
cluster_failures = True
failure_otu_picking_method = 'uclust'
params_str += ' %s' % get_params_str(d)
otu_picking_script = 'parallel_pick_otus_%s.py' % otu_picking_method
# Build the OTU picking command
pick_otus_cmd = '%s %s/%s -i %s -o %s -T %s' % (python_exe_fp,
script_dir,
otu_picking_script,
input_fp,
pick_otu_dir,
params_str)
else:
try:
params_str = get_params_str(params['pick_otus'])
except KeyError:
params_str = ''
# Build the OTU picking command
pick_otus_cmd = '%s %s/pick_otus.py -i %s -o %s %s' %\
(python_exe_fp, script_dir, input_fp, pick_otu_dir, params_str)
commands.append([('Pick OTUs', pick_otus_cmd)])
if cluster_failures:
reference_otu_fp = otu_fp
clustered_failures_dir = '%s/failure_otus/' % pick_otu_dir
try:
d = params['pick_otus'].copy()
del d['otu_picking_method']
except KeyError:
pass
if 'uclust_otu_id_prefix' not in d:
d['uclust_otu_id_prefix'] = 'DeNovoOTU'
params_str = ' %s' % get_params_str(d)
failures_list_fp = '%s/%s_failures.txt' % \
(pick_otu_dir,input_basename)
failures_fasta_fp = '%s/%s_failures.fasta' % \
(pick_otu_dir,input_basename)
filter_fasta_cmd = 'filter_fasta.py -f %s -s %s -o %s' %\
(input_fp,failures_list_fp,failures_fasta_fp)
commands.append([('Generate failures fasta file',
filter_fasta_cmd)])
# Prep the OTU picking command for
failure_otu_fp = '%s/%s_failures_otus.txt' % (clustered_failures_dir,input_basename)
# Build the OTU picking command
pick_otus_cmd = '%s %s/pick_otus.py -i %s -o %s -m %s %s' %\
(python_exe_fp, script_dir, failures_fasta_fp, clustered_failures_dir,
failure_otu_picking_method, params_str)
commands.append([('Pick de novo OTUs for new clusters', pick_otus_cmd)])
merged_otu_map_fp = '%s/merged_otu_map.txt' % clustered_failures_dir
cat_otu_tables_cmd = 'cat %s %s >> %s' %\
(reference_otu_fp,failure_otu_fp,merged_otu_map_fp)
commands.append([('Merge OTU maps',cat_otu_tables_cmd)])
otu_fp = merged_otu_map_fp
# Prep the representative set picking command
rep_set_dir = '%s/rep_set/' % output_dir
create_dir(rep_set_dir)
rep_set_fp = '%s/%s_rep_set.fasta' % (rep_set_dir,input_basename)
rep_set_log_fp = '%s/%s_rep_set.log' % (rep_set_dir,input_basename)
try:
params_str = get_params_str(params['pick_rep_set'])
except KeyError:
params_str = ''
# Build the representative set picking command
pick_rep_set_cmd = '%s %s/pick_rep_set.py -i %s -f %s -l %s -o %s %s' %\
(python_exe_fp, script_dir, otu_fp, input_fp, rep_set_log_fp,\
rep_set_fp, params_str)
commands.append([('Pick representative set', pick_rep_set_cmd)])
# Prep the taxonomy assignment command
try:
assignment_method = params['assign_taxonomy']['assignment_method']
except KeyError:
assignment_method = 'uclust'
assign_taxonomy_dir = '%s/%s_assigned_taxonomy' %\
(output_dir,assignment_method)
taxonomy_fp = '%s/%s_rep_set_tax_assignments.txt' % \
(assign_taxonomy_dir,input_basename)
if parallel and (assignment_method == 'rdp' or
assignment_method == 'blast' or
assignment_method == 'uclust'):
# Grab the parallel-specific parameters
try:
params_str = get_params_str(params['parallel'])
except KeyError:
params_str = ''
# Grab the taxonomy assignment parameters
try:
# Want to find a cleaner strategy for this: the parallel script
# is method-specific, so doesn't take a --assignment_method
# option. This works for now though.
d = params['assign_taxonomy'].copy()
if 'assignment_method' in d:
del d['assignment_method']
params_str += ' %s' % get_params_str(d)
except KeyError:
pass
# Build the parallel taxonomy assignment command
assign_taxonomy_cmd = \
'%s %s/parallel_assign_taxonomy_%s.py -i %s -o %s -T %s' %\
(python_exe_fp, script_dir, assignment_method, rep_set_fp,\
assign_taxonomy_dir, params_str)
else:
try:
params_str = get_params_str(params['assign_taxonomy'])
except KeyError:
params_str = ''
# Build the taxonomy assignment command
assign_taxonomy_cmd = '%s %s/assign_taxonomy.py -o %s -i %s %s' %\
(python_exe_fp, script_dir, assign_taxonomy_dir,\
rep_set_fp, params_str)
commands.append([('Assign taxonomy',assign_taxonomy_cmd)])
# Prep the OTU table building command
otu_table_fp = '%s/otu_table.biom' % output_dir
try:
params_str = get_params_str(params['make_otu_table'])
except KeyError:
params_str = ''
# Build the OTU table building command
make_otu_table_cmd = '%s %s/make_otu_table.py -i %s -t %s -o %s %s' %\
(python_exe_fp, script_dir, otu_fp, taxonomy_fp, otu_table_fp, params_str)
commands.append([('Make OTU table', make_otu_table_cmd)])
if cluster_failures:
reference_otu_table_fp = '%s/reference_only_otu_table.biom' % output_dir
# Build the OTU table building command
make_otu_table_cmd = '%s %s/make_otu_table.py -i %s -t %s -o %s %s' %\
(python_exe_fp, script_dir, reference_otu_fp, taxonomy_fp,
reference_otu_table_fp, params_str)
commands.append([('Make reference-only OTU table', make_otu_table_cmd)])
# Prep the pynast alignment command
try:
alignment_method = params['align_seqs']['alignment_method']
except KeyError:
alignment_method = 'pynast'
pynast_dir = '%s/%s_aligned_seqs' % (output_dir,alignment_method)
aln_fp = '%s/%s_rep_set_aligned.fasta' % (pynast_dir,input_basename)
if parallel and alignment_method == 'pynast':
# Grab the parallel-specific parameters
try:
params_str = get_params_str(params['parallel'])
except KeyError:
params_str = ''
# Grab the alignment parameters
# 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.
try:
d = params['align_seqs'].copy()
except KeyError:
d = {}
try:
del d['alignment_method']
except KeyError:
pass
params_str += ' %s' % get_params_str(d)
# Build the parallel pynast alignment command
align_seqs_cmd = '%s %s/parallel_align_seqs_pynast.py -i %s -o %s -T %s' %\
(python_exe_fp, script_dir, rep_set_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 = '%s %s/align_seqs.py -i %s -o %s %s' %\
(python_exe_fp, script_dir, rep_set_fp, pynast_dir, params_str)
commands.append([('Align sequences', align_seqs_cmd)])
# Prep the alignment filtering command
filtered_aln_fp = '%s/%s_rep_set_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 = '%s %s/filter_alignment.py -o %s -i %s %s' %\
(python_exe_fp, script_dir, 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 = '%s %s/make_phylogeny.py -i %s -o %s %s' %\
(python_exe_fp, script_dir, filtered_aln_fp, tree_fp,\
params_str)
commands.append([('Build phylogenetic tree', make_phylogeny_cmd)])
# 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 abspath(tree_fp), abspath(otu_table_fp)
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,
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
input_dir, input_filename = split(input_fp)
input_basename, input_ext = splitext(input_filename)
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 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)])
# 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
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)
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
final_repset_f = open(final_repset_fp, "w")
new_refseqs_fp = "%s/new_refseqs.fna" % output_dir
# write non-singleton otus representative sequences from step1 to the
# final rep set file
for otu_id, seq in MinimalFastaParser(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 MinimalFastaParser(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 MinimalFastaParser(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)])
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 = []
if close_logger_on_success:
logger.close()
def run_ampliconnoise(mapping_fp,
output_dir, command_handler, params, qiime_config,
logger=None, status_update_callback=print_to_stdout,
chimera_alpha=-3.8228,chimera_beta=0.6200, sff_txt_fp=None, numnodes=2,
suppress_perseus=True, output_filepath=None, platform='flx',
seqnoise_resolution=None, truncate_len=None):
""" Run the ampliconnoise pipeline
The steps performed by this function are:
1. Split input sff.txt file into one file per sample
2. Run scripts required for PyroNoise
3. Run scripts required for SeqNoise
4. Run scripts requred for Perseus (chimera removal)
5. Merge output files into one file similar to the output of split_libraries.py
output_filepath should be absolute
seqnoise_resolution should be string
environment variable PYRO_LOOKUP_FILE must be set correctly. Thus be
careful passing command handlers that don't spawn child processes, as they
may not inherit the correct environment variable setting
"""
map_data,headers,comments = parse_mapping_file(open(mapping_fp,'U'))
create_dir(output_dir)
if seqnoise_resolution == None:
if platform=='flx': seqnoise_resolution = '30.0'
elif platform=='titanium': seqnoise_resolution = '25.0'
else: raise RuntimeError('seqnoise_resolution not set, and no'+\
' default for platform '+platform)
if truncate_len == None:
if platform=='flx': truncate_len = '220'
elif platform=='titanium': truncate_len = '400'
else: raise RuntimeError('truncate_len not set, and no'+\
' default for platform '+platform)
sample_names = [] # these are filenames minus extension, and are sample IDs
primer_seqs = [] # same order as sample_names
bc_seqs = [] # same order as sample_names
for i in range(len(map_data)):
sample_names.append(map_data[i][headers.index('SampleID')])
bc_seqs.append(map_data[i][headers.index('BarcodeSequence')])
# don't know why don't just take off the primer now.
# but that's done later
# primer += (map_data[i][headers.index('LinkerPrimerSequence')])
# for char, bases in IUPAC_DNA_ambiguities.items():
# primer = primer.replace(char,'['+''.join(bases)+']')
primer = (map_data[i][headers.index('LinkerPrimerSequence')])
for char, bases in IUPAC_DNA_ambiguities.items():
primer = primer.replace(char,'['+''.join(bases)+']')
primer_seqs.append(primer)
if len(set(primer_seqs)) != 1:
raise RuntimeError(
'Error: only one primer per mapping file supported.')
one_primer = primer_seqs[0]
commands = []
python_exe_fp = qiime_config['python_exe_fp']
script_dir = get_qiime_scripts_dir()
if logger == 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
log_input_md5s(logger,[mapping_fp,sff_txt_fp])
# execute commands in output_dir
called_dir = os.getcwd()
os.chdir(output_dir)
fh = open(os.path.join(output_dir,'map.csv'),'w')
for i in range(len(sample_names)):
fh.write(sample_names[i]+','+bc_seqs[i]+'\n')
fh.close()
# these are the fasta results, e.g. PC.636_Good.fa
# later we merge them and copy to output file
post_pyro_tail = '_'+truncate_len
if suppress_perseus == True:
fasta_result_names = [sample_name + post_pyro_tail+'_seqnoise_cd.fa'
for sample_name in sample_names]
else:
fasta_result_names = [sample_name + '_Good.fa' \
for sample_name in sample_names]
cmd = 'cd '+output_dir # see also os.chdir above
commands.append([('change to output dir', cmd)])
cmd = 'echo $PYRO_LOOKUP_FILE > pyro_lookup_filepath.txt'
commands.append([('confirm pyro lookup filepath environment variable',
cmd)])
cmd = 'SplitKeys.pl '+one_primer+' map.csv < '+\
os.path.join(called_dir,sff_txt_fp)+\
' > splitkeys_log.txt 2> unassigned.fna'
commands.append([('split sff.txt via barcodes (keys)', cmd)])
for i, sample_name in enumerate(sample_names):
# Build the summarize taxonomy command
if platform == 'flx':
cmd = 'Clean360.pl '+one_primer+' '+sample_name+' < '+\
sample_name+'.raw'
commands.append([('clean flows '+sample_name, cmd)])
# these run through the whole sff file once per sample, I think
# cmd = "FlowsFA.pl " + primer_seqs[i] + ' '+sample_name +' < '+\
# os.path.join(called_dir,sff_txt_fp)
# commands.append([('extract flows '+sample_name, cmd)])
elif platform == 'titanium':
cmd = 'CleanMinMax.pl '+one_primer+' '+sample_name+' < '+\
sample_name+'.raw'
commands.append([('clean flows '+sample_name, cmd)])
# cmd = "FlowsMinMax.pl " + primer_seqs[i] + ' '+sample_name +' < '+\
# os.path.join(called_dir,sff_txt_fp)
# commands.append([('extract flows '+sample_name, cmd)])
else:
raise RuntimeError("platform " + platform + " not supported")
cmd = "mpirun -np "+str(numnodes)+" PyroDist -in "+\
sample_name+".dat -out "+sample_name+ " > "+sample_name+".pdout"
commands.append([('pyrodist '+sample_name, cmd)])
cmd = "FCluster -in "+sample_name+".fdist -out "+sample_name+\
" > "+sample_name+".fcout"
commands.append([('fcluster pyrodist '+sample_name, cmd)])
# e.g.:
# mpirun -np 2 PyroNoise -din PC.354.dat -out PC.354_pyronoise -lin
# PC.354.list -s 60.0 -c 0.01 > PC.354_pyronoise.pnout
cmd = "mpirun -np "+str(numnodes)+" PyroNoise -din "+\
sample_name+".dat -out "+\
sample_name+"_pyronoise "+"-lin "+\
sample_name+".list -s 60.0 -c 0.01 > "+\
sample_name+"_pyronoise.pnout"
commands.append([('pyronoise '+sample_name, cmd)])
cmd = 'Parse.pl '+bc_seqs[i]+one_primer+' '+truncate_len+' < '+\
sample_name+'_pyronoise_cd.fa'+' > '+ sample_name+'_'+\
truncate_len+'.fa'
commands.append([('truncate '+sample_name, cmd)])
# now start with post_pyro_tail
cmd = "mpirun -np "+str(numnodes)+" SeqDist -in "+\
sample_name+post_pyro_tail+\
".fa > "+sample_name+post_pyro_tail+".seqdist"
commands.append([('seqdist '+sample_name, cmd)])
cmd = "FCluster -in "+sample_name+post_pyro_tail+".seqdist -out "+\
sample_name+post_pyro_tail+"fcl > "+\
sample_name+post_pyro_tail+".fcout"
commands.append([('fcluster seqdist '+sample_name, cmd)])
# e.g.:
# mpirun -np 2 SeqNoise -in PC.354_pyronoise_cd.fa -din
# PC.354_pyronoise_cd.seqdist -out PC.354_pyronoise_cd_seqnoise -lin
# PC.354_pyronoise_cdfcl.list -min PC.354_pyronoise.mapping -s 30.0 -c 0.08 >
# PC.354_pyronoise_cd.snout
cmd = "mpirun -np "+str(numnodes)+" SeqNoise -in "+\
sample_name+post_pyro_tail+\
".fa -din "+sample_name+post_pyro_tail+".seqdist -out "+\
sample_name+post_pyro_tail+\
"_seqnoise -lin "+sample_name+post_pyro_tail+'fcl.list -min '+\
sample_name+'_pyronoise'+\
'.mapping -s '+seqnoise_resolution+' -c 0.08 > '+\
sample_name+post_pyro_tail+'.snout'
commands.append([('seqnoise '+sample_name, cmd)])
if suppress_perseus == False:
cmd = 'Perseus -sin '+sample_name+post_pyro_tail+\
'_seqnoise_cd.fa > ' +\
sample_name+'.per'
commands.append([('Perseus '+sample_name, cmd)])
cmd = 'Class.pl '+sample_name+'.per '+\
str(chimera_alpha) + ' '+ str(chimera_beta)+\
' > '+sample_name+'.class'
commands.append([('Class.pl '+sample_name, cmd)])
cmd = 'FilterGoodClass.pl '+sample_name+post_pyro_tail+\
'_seqnoise_cd.fa '+\
sample_name+'.class 0.5 > '+sample_name+'_Chi.fa 2> '+\
sample_name+'_Good.fa'
commands.append([('FilterGoodClass '+sample_name, cmd)])
cmd = '%s %s/unweight_fasta.py -i %s -o %s -l %s' %\
(python_exe_fp, script_dir, fasta_result_names[i],
sample_name+'_unw.fna', sample_name)
commands.append([('unweight fasta '+sample_name, cmd)])
cmd = 'cat ' +\
' '.join([sample_name+'_unw.fna' for sample_name in sample_names]) +\
' > ' + output_filepath # this should be an abs filepath
commands.append([('cat into one fasta file', cmd)])
# 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)
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,
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
input_dir, input_filename = split(input_fp)
input_basename, input_ext = splitext(input_filename)
create_dir(output_dir)
commands = []
if logger == 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 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 == 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 != 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)])
input_fp = prefiltered_input_fp
input_dir, input_filename = split(input_fp)
input_basename, input_ext = splitext(input_filename)
## 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)])
## 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)
## 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
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
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
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)
## 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
final_repset_f = open(final_repset_fp,'w')
new_refseqs_fp = '%s/new_refseqs.fna' % output_dir
# write non-singleton otus representative sequences from step1 to the
# final rep set file
for otu_id, seq in MinimalFastaParser(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))
# 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')
# 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 MinimalFastaParser(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 MinimalFastaParser(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()
# 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)])
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 taxa to otu table
add_metadata_cmd = 'add_metadata.py -i %s --observation_mapping_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 = []
if close_logger_on_success:
logger.close()
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,
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
input_dir, input_filename = split(input_fp)
input_basename, input_ext = splitext(input_filename)
create_dir(output_dir)
commands = []
if logger == 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 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 == 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 != 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)])
input_fp = prefiltered_input_fp
input_dir, input_filename = split(input_fp)
input_basename, input_ext = splitext(input_filename)
## 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)])
## 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)
## 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
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
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
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)
## 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
final_repset_f = open(final_repset_fp,'w')
new_refseqs_fp = '%s/new_refseqs.fna' % output_dir
# write non-singleton otus representative sequences from step1 to the
# final rep set file
for otu_id, seq in MinimalFastaParser(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))
# 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')
# 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 MinimalFastaParser(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 MinimalFastaParser(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()
# 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)])
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 = []
if close_logger_on_success:
logger.close()
def run_core_diversity_analyses(
biom_fp,
mapping_fp,
sampling_depth,
output_dir,
qiime_config,
command_handler=call_commands_serially,
tree_fp=None,
params=None,
categories=None,
arare_min_rare_depth=10,
arare_num_steps=10,
parallel=False,
suppress_taxa_summary=False,
suppress_beta_diversity=False,
suppress_alpha_diversity=False,
suppress_otu_category_significance=False,
status_update_callback=print_to_stdout):
"""
"""
if categories != None:
# Validate categories provided by the users
mapping_data, mapping_comments = \
parse_mapping_file_to_dict(open(mapping_fp,'U'))
metadata_map = MetadataMap(mapping_data, mapping_comments)
for c in categories:
if c not in metadata_map.CategoryNames:
raise ValueError, ("Category '%s' is not a column header "
"in your mapping file. "
"Categories are case and white space sensitive. Valid "
"choices are: (%s)" % (c,', '.join(metadata_map.CategoryNames)))
if metadata_map.hasSingleCategoryValue(c):
raise ValueError, ("Category '%s' contains only one value. "
"Categories analyzed here require at least two values." % c)
else:
categories= []
# prep some variables
if params == None:
params = parse_qiime_parameters([])
create_dir(output_dir)
index_fp = '%s/index.html' % output_dir
index_links = []
commands = []
# begin logging
log_fp = generate_log_fp(output_dir)
index_links.append(('Master run log',log_fp,_index_headers['run_summary']))
logger = WorkflowLogger(log_fp,
params=params,
qiime_config=qiime_config)
input_fps = [biom_fp,mapping_fp]
if tree_fp != None:
input_fps.append(tree_fp)
log_input_md5s(logger,input_fps)
# run print_biom_table_summary.py on input BIOM table
try:
params_str = get_params_str(params['print_biom_table_summary'])
except KeyError:
params_str = ''
biom_table_stats_output_fp = '%s/biom_table_summary.txt' % output_dir
print_biom_table_summary_cmd = \
"print_biom_table_summary.py -i %s -o %s --suppress_md5 %s" % \
(biom_fp, biom_table_stats_output_fp,params_str)
index_links.append(('BIOM table statistics',
biom_table_stats_output_fp,
_index_headers['run_summary']))
commands.append([('Generate BIOM table summary',
print_biom_table_summary_cmd)])
# filter samples with fewer observations than the requested sampling_depth.
# since these get filtered for some analyses (eg beta diversity after
# even sampling) it's useful to filter them here so they're filtered
# from all analyses.
filtered_biom_fp = "%s/table_mc%d.biom" % (output_dir, sampling_depth)
filter_samples_cmd = "filter_samples_from_otu_table.py -i %s -o %s -n %d" %\
(biom_fp,filtered_biom_fp,sampling_depth)
commands.append([('Filter low sequence count samples from table (minimum sequence count: %d)' % sampling_depth,
filter_samples_cmd)])
biom_fp = filtered_biom_fp
# run initial commands and reset the command list
command_handler(commands,
status_update_callback,
logger,
close_logger_on_success=False)
commands = []
if not suppress_beta_diversity:
bdiv_even_output_dir = '%s/bdiv_even%d/' % (output_dir,sampling_depth)
even_dm_fps = run_beta_diversity_through_plots(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=bdiv_even_output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
sampling_depth=sampling_depth,
# force suppression of distance histograms - boxplots work better
# in this context, and are created below.
histogram_categories=[],
tree_fp=tree_fp,
parallel=parallel,
logger=logger,
suppress_md5=True,
status_update_callback=status_update_callback)
for bdiv_metric, dm_fp in even_dm_fps:
for category in categories:
boxplots_output_dir = '%s/%s_boxplots/' % (bdiv_even_output_dir,bdiv_metric)
try:
params_str = get_params_str(params['make_distance_boxplots'])
except KeyError:
params_str = ''
boxplots_cmd = \
'make_distance_boxplots.py -d %s -f %s -o %s -m %s -n 999 %s' %\
(dm_fp, category, boxplots_output_dir, mapping_fp, params_str)
commands.append([('Boxplots (%s)' % category,
boxplots_cmd)])
index_links.append(('Distance boxplots (%s)' % bdiv_metric,
'%s/%s_Distances.pdf' % \
(boxplots_output_dir,category),
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(('Distance boxplots statistics (%s)' % bdiv_metric,
'%s/%s_Stats.txt' % \
(boxplots_output_dir,category),
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(('3D plot (%s, continuous coloring)' % bdiv_metric,
'%s/%s_3d_continuous/%s_pc_3D_PCoA_plots.html' % \
(bdiv_even_output_dir,bdiv_metric,bdiv_metric),
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(('3D plot (%s, discrete coloring)' % bdiv_metric,
'%s/%s_3d_discrete/%s_pc_3D_PCoA_plots.html' % \
(bdiv_even_output_dir,bdiv_metric,bdiv_metric),
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(('2D plot (%s, continuous coloring)' % bdiv_metric,
'%s/%s_2d_continuous/%s_pc_2D_PCoA_plots.html' % \
(bdiv_even_output_dir,bdiv_metric,bdiv_metric),
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(('2D plot (%s, discrete coloring)' % bdiv_metric,
'%s/%s_2d_discrete/%s_pc_2D_PCoA_plots.html' % \
(bdiv_even_output_dir,bdiv_metric,bdiv_metric),
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(('Distance matrix (%s)' % bdiv_metric,
'%s/%s_dm.txt' % \
(bdiv_even_output_dir,bdiv_metric),
_index_headers['beta_diversity_even'] % sampling_depth))
index_links.append(('Principal coordinate matrix (%s)' % bdiv_metric,
'%s/%s_pc.txt' % \
(bdiv_even_output_dir,bdiv_metric),
_index_headers['beta_diversity_even'] % sampling_depth))
if not suppress_alpha_diversity:
## Alpha rarefaction workflow
arare_full_output_dir = '%s/arare_max%d/' % (output_dir,sampling_depth)
run_alpha_rarefaction(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=arare_full_output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
tree_fp=tree_fp,
num_steps=arare_num_steps,
parallel=parallel,
logger=logger,
min_rare_depth=arare_min_rare_depth,
max_rare_depth=sampling_depth,
suppress_md5=True,
status_update_callback=status_update_callback)
index_links.append(('Alpha rarefaction plots',
'%s/alpha_rarefaction_plots/rarefaction_plots.html'\
% arare_full_output_dir,
_index_headers['alpha_diversity']))
collated_alpha_diversity_fps = \
glob('%s/alpha_div_collated/*txt' % arare_full_output_dir)
try:
params_str = get_params_str(params['compare_alpha_diversity'])
except KeyError:
params_str = ''
for category in categories:
for collated_alpha_diversity_fp in collated_alpha_diversity_fps:
alpha_metric = splitext(split(collated_alpha_diversity_fp)[1])[0]
alpha_comparison_output_fp = '%s/%s_%s.txt' % \
(arare_full_output_dir,category,alpha_metric)
compare_alpha_cmd = \
'compare_alpha_diversity.py -i %s -m %s -c %s -o %s -n 999 %s' %\
(collated_alpha_diversity_fp, mapping_fp, category,
alpha_comparison_output_fp, params_str)
commands.append([('Compare alpha diversity (%s, %s)' %\
(category,alpha_metric),
compare_alpha_cmd)])
index_links.append(
('Alpha diversity statistics (%s, %s)' % (category,alpha_metric),
alpha_comparison_output_fp,
_index_headers['alpha_diversity']))
if not suppress_taxa_summary:
taxa_plots_output_dir = '%s/taxa_plots/' % output_dir
run_summarize_taxa_through_plots(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=taxa_plots_output_dir,
mapping_cat=None,
sort=True,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
logger=logger,
suppress_md5=True,
status_update_callback=status_update_callback)
index_links.append(('Taxa summary bar plots',
'%s/taxa_summary_plots/bar_charts.html'\
% taxa_plots_output_dir,
_index_headers['taxa_summary']))
index_links.append(('Taxa summary area plots',
'%s/taxa_summary_plots/area_charts.html'\
% taxa_plots_output_dir,
_index_headers['taxa_summary']))
for category in categories:
taxa_plots_output_dir = '%s/taxa_plots_%s/' % (output_dir,category)
run_summarize_taxa_through_plots(
otu_table_fp=biom_fp,
mapping_fp=mapping_fp,
output_dir=taxa_plots_output_dir,
mapping_cat=category,
sort=True,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
logger=logger,
suppress_md5=True,
status_update_callback=status_update_callback)
index_links.append(('Taxa summary bar plots',
'%s/taxa_summary_plots/bar_charts.html'\
% taxa_plots_output_dir,
_index_headers['taxa_summary_categorical'] % category))
index_links.append(('Taxa summary area plots',
'%s/taxa_summary_plots/area_charts.html'\
% taxa_plots_output_dir,
_index_headers['taxa_summary_categorical'] % category))
if not suppress_otu_category_significance:
# OTU category significance
for category in categories:
category_signifance_fp = \
'%s/category_significance_%s.txt' % (output_dir, category)
try:
params_str = get_params_str(params['otu_category_significance'])
except KeyError:
params_str = ''
# Build the OTU cateogry significance command
category_significance_cmd = \
'otu_category_significance.py -i %s -m %s -c %s -o %s %s' %\
(biom_fp, mapping_fp, category,
category_signifance_fp, params_str)
commands.append([('OTU category significance (%s)' % category,
category_significance_cmd)])
index_links.append(('Category significance (%s)' % category,
category_signifance_fp,
_index_headers['otu_category_sig']))
commands.append([('Compress the filtered BIOM table','gzip %s' % filtered_biom_fp)])
index_links.append(('Filtered BIOM table (minimum sequence count: %d)' % sampling_depth,
'%s.gz' % filtered_biom_fp,
_index_headers['run_summary']))
command_handler(commands, status_update_callback, logger)
generate_index_page(index_links,index_fp)