def _default_metadata():
cls = namedtuple("Sample", ['SampleID'])
for seq_list in filter(bool, maybe_seqs):
# return the first one that contains filenames
return [
cls(basename(util.rmext(f, all=True))) for f in seq_list
]
def _configure(self):
if self.raw_seq_files or self.raw_demuxed_fastq_files:
for task in self._handle_raw_seqs():
yield task
if self.raw_seq_files:
for task in self._demultiplex():
yield task
# ensure all files are decompressed
# possibly stitch paired reads, demultiplex, and quality filter
# do closed reference otu picking
for fasta_fname in self.demuxed_fasta_files:
dirname = util.rmext(os.path.basename(fasta_fname))
otu_dir = join(os.path.dirname(fasta_fname), dirname + "_otus")
yield sixteen.pick_otus_closed_ref(input_fname=fasta_fname,
output_dir=otu_dir,
**self.options.get(
'pick_otus_closed_ref',
dict()))
self.otu_tables.append(join(otu_dir, "otu_table.biom"))
# convert biom file to tsv
for otu_table in self.otu_tables:
tsv_filename = otu_table + ".tsv"
yield biom.biom_to_tsv(otu_table, tsv_filename)
# infer genes and pathways with picrust
for otu_table in self.otu_tables:
yield sixteen.picrust(otu_table,
**self.options.get('picrust', dict()))
def _default_metadata():
cls = namedtuple("Sample", ['SampleID'])
for seq_attr in self.sequence_attrs:
maybe_seqs = getattr(self, seq_attr, None)
if maybe_seqs:
return [ cls(basename(util.rmext(f, all=True)))
for f in maybe_seqs ]
def _configure(self):
if self.options['infer_pairs'].get('infer'):
paired, notpaired = infer_pairs(self.raw_seq_files)
self.raw_seq_files = paired + notpaired
maybe_tasks = list()
for maybe_pair in self.raw_seq_files:
is_pair = type(maybe_pair) in (tuple, list)
if is_pair:
pair, tasks = maybe_convert_to_fastq(maybe_pair,
self.products_dir)
self.paired_fastq_files.append(pair)
maybe_tasks.extend(tasks)
elif util.guess_seq_filetype(maybe_pair) == 'bam':
prefix = util.new_file(util.rmext(basename(maybe_pair)),
basedir=self.products_dir)
t = samtools.to_paired_fastq(maybe_pair, prefix)
paired, single = t['targets'][:2], t['targets'][2]
self.paired_fastq_files.append(paired)
self.unpaired_fastq_files.append(single)
maybe_tasks.append(t)
else:
single, tasks = maybe_convert_to_fastq([maybe_pair],
self.products_dir)
self.unpaired_fastq_files.append(single[0])
maybe_tasks.extend(tasks)
for task in maybe_tasks:
yield task
for pair in self.paired_fastq_files:
align_sam = util.new_file(_to_merged(basename(pair[0]),
tag="align"),
basedir=self.products_dir)
align_sam += ".sam"
self.align_sams.append(align_sam)
yield subread.align(pair, align_sam,
self.options.get('subread_align', dict()))
for single in self.unpaired_fastq_files:
align_sam = util.new_file(util.addtag(basename(single), "align"),
basedir=self.products_dir)
align_sam += ".sam"
self.align_sams.append(align_sam)
yield subread.align(single, align_sam,
self.options.get('subread_align', dict()))
for align_sam in self.align_sams:
count_table = util.new_file(util.addtag(basename(align_sam),
"count"),
basedir=self.products_dir)
self.count_tables.append(count_table)
yield subread.featureCounts([align_sam], count_table,
self.options.get(
'featureCounts', dict()))
def _maybe_mangle():
if not do_mangle:
return
if not os.path.exists(
output_fasta) or os.stat(output_fasta).st_size < 1:
return
m = rmext(output_fasta, all=True) if mangle_to is False else mangle_to
cmd = "sequence_convert -m {m} -f fasta -t fasta {o} > {o}.tmp".format(
m=m, o=output_fasta)
CmdAction(cmd, verbose=verbose).execute()
CmdAction("mv {o}.tmp {o}".format(o=output_fasta),
verbose=verbose).execute()
def _configure(self):
yield self._handle_raw_seqs_and_demultiplex()
for fasta_fname in self.demuxed_fasta_files:
otu_table = util.rmext(fasta_fname) + "_tax.biom"
otu_table = join(self.products_dir, os.path.basename(otu_table))
yield pick_otus_closed_ref(
fasta_fname, otu_table,
**self.options.get('pick_otus_closed_ref', dict()))
self.otu_tables.append(otu_table)
# infer genes and pathways with picrust
for otu_table in self.otu_tables:
yield sixteen.picrust(otu_table,
**self.options.get('picrust', dict()))
def assign_taxonomy(in_fasta, out_dir, qiime_opts={}):
name = rmext(os.path.basename(in_fasta))+"_tax_assignments.txt"
taxonomy_out = os.path.join(out_dir, name)
default_opts = dict([
("r", settings.workflows.sixteen.otu_refseq),
("t", settings.workflows.sixteen.otu_taxonomy),
]+list(qiime_opts.items()))
cmd = ("assign_taxonomy.py -i "+in_fasta+" -o "+out_dir+
" "+dict_to_cmd_opts(default_opts))
return { "name" : "assign_taxonomy: "+taxonomy_out,
"targets" : [taxonomy_out],
"actions" : [cmd],
"file_dep" : [default_opts['r'], default_opts['t'], in_fasta] }
def split_illumina_style(self, seqfiles_to_split, barcode_seqfiles):
demuxed, tasks = list(), list()
bcode_pairs = zip(seqfiles_to_split, barcode_seqfiles)
options = self.options.get("demultiplex_illumina", dict())
if 'barcode_type' not in options:
options['barcode_type'] = _determine_barcode_type(
self.sample_metadata)
do_groupby = options.pop("group_by_sampleid", False)
for seqfile, bcode_file in bcode_pairs:
sample_dir = join(self.products_dir, basename(seqfile) + "_split")
map_fname = util.new_file("map.txt", basedir=sample_dir)
sample_group = self._filter_samples_for_file(
self.sample_metadata,
seqfile,
key=lambda val: val.Run_accession)
tasks.append(
sixteen.write_map(sample_group, sample_dir,
**self.options.get('write_map', dict())))
outfile = util.new_file(util.rmext(basename(seqfile)) +
"_demuxed.fna",
basedir=sample_dir)
tasks.append(
sixteen.demultiplex_illumina([seqfile], [bcode_file],
map_fname,
outfile,
qiime_opts=options))
demuxed.append(outfile)
if do_groupby:
output_dir = join(self.products_dir, "demuxed_by-sampleid")
sample_ids = [s[0] for s in sample_group]
task_dict = general.group_by_sampleid(demuxed, output_dir,
sample_ids)
demuxed = task_dict['targets']
tasks.append(task_dict)
return demuxed, tasks
def pick_otus_closed_ref(input_fname, output_dir, verbose=None, qiime_opts={}):
"""Workflow to perform OTU picking, generates a biom-formatted OTU
table from demultiplexed 16S reads. This workflow (in general
terms) wraps qiime's pick_closed_reference_otus.py, which itself
wraps either uclust or usearch. Note that uclust and usearch
require a fairly large memory footprint (1.5-2.0G in some cases).
:param input_fname: String; File path to the input,
fasta-formatted 16S sequences
:param output_dir: String; Path to the directory where the output OTU
table will be saved as 'otu_table.biom'. Other
qiime-specific logs will go there, too.
:keyword verbose: Boolean: set to true to print the commands that are
run as they are run
:keyword qiime_opts: Dictionary; A dictionary of command line options to
be passed to the wrapped split_libraries.py script.
No - or -- flags are necessary; the correct - or --t
flags are inferred based on the length of the option.
For boolean options, use the key/value pattern
of { "my-option": "" }.
External dependencies:
- Qiime 1.8.0: https://github.com/qiime/qiime-deploy
- USEARCH: (only if using the usearch option)
http://www.drive5.com/usearch/
Resource utilization:
- RAM: >1.5 G
"""
output_fname = new_file("otu_table.biom", basedir=output_dir)
revcomp_fname = new_file(
os.path.basename(rmext(input_fname))+"_revcomp.fna",
basedir=os.path.dirname(input_fname))
verbose = settings.workflows.verbose if verbose is None else verbose
default_opts = {
"taxonomy_fp": settings.workflows.sixteen.otu_taxonomy,
"reference_fp": settings.workflows.sixteen.otu_refseq
}
default_opts.update(qiime_opts)
opts = dict_to_cmd_opts(default_opts)
cmd = ("pick_closed_reference_otus.py"+
" --input_fp={}"+
" --output_dir="+output_dir+
" -f"+
" "+opts)
revcomp_cmd = ("sequence_convert"+
" --format=fasta"+
" --to=fasta "+
" -r"+
" "+input_fname+
" > "+revcomp_fname)
def run(targets):
strategies.backup(
(CmdAction(cmd.format(input_fname),verbose=verbose),
strategies.Group(
CmdAction(revcomp_cmd),
CmdAction(cmd.format(revcomp_fname),verbose=verbose))),
extra_conditions = [
lambda ret, output_fname: os.stat(output_fname).st_size == 0
],
output_fname=output_fname,
)
return {
"name": "pick_otus_closed_ref:"+input_fname,
"actions": [run],
"targets": [output_fname],
"file_dep": [input_fname],
"title": lambda t: t.name+" Estimated mem=3000"
}
def _configure(self):
for attr in self.sequence_attrs:
seq_set = getattr(self, attr)
if self.options['infer_pairs'].get('infer'):
paired, notpaired = infer_pairs(seq_set)
seq_set = paired + notpaired
seq_set, maybe_tasks = maybe_concatenate(seq_set, self.products_dir)
setattr(self, attr, seq_set)
for t in maybe_tasks:
yield t
for file_ in self.raw_seq_files:
if util.guess_seq_filetype(file_) != "fastq":
fastq_file = util.new_file( basename(file_)+"_filtered.fastq",
basedir=self.products_dir )
yield general.sequence_convert(
[file_], fastq_file,
**self.options.get('sequence_convert', dict())
)
else:
fastq_file = file_
self.intermediate_fastq_files.append(fastq_file)
for fastq_file in self.intermediate_fastq_files:
name_base = util.new_file(util.rmext(fastq_file, all=True),
basedir=self.products_dir)
task_dict = next(wgs.knead_data(
[fastq_file], name_base,
**self.options.get('decontaminate', {})
))
decontaminated_fastq = task_dict['targets'][0]
self.decontaminated_fastq_files.append(decontaminated_fastq)
yield task_dict
for d_fastq in self.decontaminated_fastq_files:
metaphlan_file = util.new_file(
basename(d_fastq)+".metaphlan2.tsv",
basedir=self.products_dir )
otu_table = metaphlan_file.replace('.tsv', '.biom')
yield wgs.metaphlan2(
[d_fastq], output_file=metaphlan_file,
biom=otu_table,
# first index is for first item in list of samples
# second index is to get the sample id from the sample
sample_id=self._filter_samples_for_file(self.sample_metadata,
d_fastq)[0][0],
input_type="multifastq",
**self.options.get('metaphlan2', dict())
)
self.metaphlan_results.append(metaphlan_file)
self.otu_tables.append(otu_table)
# Finally, HUMAnN all alignment files
humann_output_dir = util.new_file(
util.rmext(basename(d_fastq), all=True)+"_humann",
basedir=self.products_dir
)
yield wgs.humann2( d_fastq, humann_output_dir,
**self.options.get('humann', dict()) )
