def dep_check_blast(dir_dep, os_id, dist_id, debian_dists, redhat_dists,
force):
if os_id == 'mac':
url = ('https://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/2.10.1/'
'ncbi-blast-2.10.1+-x64-macosx.tar.gz')
elif os_id == 'linux':
if dist_id in debian_dists:
url = ('https://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/'
'2.10.1/ncbi-blast-2.10.1+-x64-linux.tar.gz')
elif dist_id in redhat_dists:
url = ('https://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/'
'2.10.1/ncbi-blast-2.10.1+-x64-linux.tar.gz')
dnld_path = opj(dir_dep, 'ncbi-blast.tar.gz')
makeblastdb = None
blastn = None
tblastn = None
try:
if force is True:
raise
makeblastdb = which('makeblastdb')
blastn = which('blastn')
tblastn = which('tblastn')
run([makeblastdb, '-help'])
except Exception:
try:
dir_bin = opj(dir_dep, get_dep_dir(dir_dep, 'ncbi-blast'))
makeblastdb = opj(dir_bin, 'bin', 'makeblastdb')
blastn = opj(dir_bin, 'bin', 'blastn')
tblastn = opj(dir_bin, 'bin', 'tblastn')
run([makeblastdb, '-help'])
except Exception:
Log.wrn('BLAST+ was not found on this system, trying to download.')
download_file(url, dnld_path)
tar_ref = tarfile.open(dnld_path, 'r:gz')
tar_ref.extractall(dir_dep)
tar_ref.close()
dir_bin = opj(dir_dep, get_dep_dir(dir_dep, 'ncbi-blast'))
makeblastdb = opj(dir_bin, 'bin', 'makeblastdb')
blastn = opj(dir_bin, 'bin', 'blastn')
tblastn = opj(dir_bin, 'bin', 'tblastn')
if not ope(makeblastdb) or \
not ope(blastn) or \
not ope(tblastn):
Log.err('Could not download BLAST+.')
return None, None, None
regexp = r'\sblast\s([\d\.]*)'
v = get_dep_version([makeblastdb, '-version'], regexp)
Log.msg('makeblastdb is available:', v + ' ' + makeblastdb)
v = get_dep_version([blastn, '-version'], regexp)
Log.msg('blastn is available:', v + ' ' + blastn)
v = get_dep_version([tblastn, '-version'], regexp)
Log.msg('tblastn is available:', v + ' ' + tblastn)
return makeblastdb, blastn, tblastn
def dnld_pfam_uniprot_seqs(ss, uniprot_acc, aa_uniprot_file, dir_cache_prj):
if len(uniprot_acc) != 0:
_ = opj(dir_cache_prj, 'aa_uniprot_acc_cache__' + ss)
prev_uniprot_acc = []
if ope(_):
with open(_, 'rb') as f:
prev_uniprot_acc = pickle.load(f)
with open(_, 'wb') as f:
pickle.dump(uniprot_acc, f, protocol=PICKLE_PROTOCOL)
if (set(uniprot_acc) != set(prev_uniprot_acc)) or \
(not ope(aa_uniprot_file)):
Log.inf('Downloading Pfam protein sequences from UniProt:', ss)
# Note: the number of sequences downloaded from UniProt may
# be less than the total number of accessions. This is normal
# as Pfam may return "obsolete" accessions, which will not be
# downloaded here.
_ = fasta_by_accession_list(uniprot_acc)
_ = standardize_fasta_text(_, SEQ_TYPE_AA, pfam=True)
write_fasta(_, aa_uniprot_file)
else:
if ope(aa_uniprot_file):
osremove(aa_uniprot_file)
def dep_check_kakapolib(force=False, quiet=False):
kkpl = KAKAPOLIB
if not ope(kkpl):
if quiet is False:
Log.wrn('Compiling kakapolib.')
run(['make', 'install'], cwd=DIR_C_SRC)
if ope(kkpl):
if quiet is False:
Log.msg('kakapolib is available:', kkpl)
else:
Log.err('Compilation of kakapolib failed.')
return None
return ctypes.CDLL(kkpl)
def dnld_prot_seqs(ss, prot_acc_user, aa_prot_ncbi_file, dir_cache_prj):
if len(prot_acc_user) != 0:
acc_old = set()
if ope(aa_prot_ncbi_file):
_ = read_fasta(aa_prot_ncbi_file, SEQ_TYPE_AA)
acc_old = set([x.definition.split('|')[0] for x in _])
if acc_old == set(prot_acc_user):
return prot_acc_user
else:
pickle_file = opj(dir_cache_prj, 'ncbi_prot_metadata_cache__' + ss)
if ope(pickle_file):
with open(pickle_file, 'rb') as f:
pa_info = pickle.load(f)
print()
Log.inf('Downloading protein sequences from NCBI:', ss)
_ = dnld_ncbi_seqs('protein',
prot_acc_user,
rettype='gb',
retmode='xml')
prot_acc_user_new = list()
for rec in _:
acc_ver = rec.accession_version
defn = rec.definition
organism = rec.organism
prot_acc_user_new.append(acc_ver)
defn_new = defn.split('[' + organism + ']')[0]
defn_new = defn_new.lower().strip()
defn_new = defn_new.replace(' ', '_').replace('-', '_')
defn_new = defn_new.replace(',', '')
defn_new = defn_new[0].upper() + defn_new[1:]
defn_new = acc_ver + '|' + defn_new + '|' + organism
defn_new = defn_new.replace(' ', '_').replace('-', '_')
rec.definition = defn_new
prot_acc_user = prot_acc_user_new
write_fasta(_, aa_prot_ncbi_file)
else:
if ope(aa_prot_ncbi_file):
osremove(aa_prot_ncbi_file)
return prot_acc_user
def dnld_cds_for_ncbi_prot_acc(ss, prot_acc_user, prot_cds_ncbi_file, tax,
dir_cache_prj):
pickle_file = opj(dir_cache_prj, 'ncbi_prot_cds_cache__' + ss)
acc_old = set()
if ope(pickle_file):
with open(pickle_file, 'rb') as f:
pickled = pickle.load(f)
acc_old = set(pickled[0])
if acc_old == set(prot_acc_user):
cds_rec_dict = pickled[1]
Log.inf('The CDS for the dereplicated set of the user-provided '
'NCBI protein accessions have already been '
'downloaded:', ss)
else:
Log.inf('Downloading CDS for the dereplicated set of the user-provided '
'NCBI protein accessions:', ss)
cds_rec_dict = seq_records_to_dict(cds_for_prot(prot_acc_user),
prepend_acc=True)
with open(pickle_file, 'wb') as f:
pickle.dump((prot_acc_user, cds_rec_dict), f,
protocol=PICKLE_PROTOCOL)
write_fasta(cds_rec_dict, prot_cds_ncbi_file)
def dep_check_bowtie2(dir_dep, os_id, force):
if os_id == 'mac':
url = ('https://sourceforge.net/projects/bowtie-bio/files/bowtie2/'
'2.4.1/bowtie2-2.4.1-macos-x86_64.zip/download')
elif os_id == 'linux':
url = ('https://sourceforge.net/projects/bowtie-bio/files/bowtie2/'
'2.4.1/bowtie2-2.4.1-linux-x86_64.zip/download')
dnld_path = opj(dir_dep, 'bowtie2.zip')
try:
if force is True:
raise
bowtie2 = which('bowtie2')
bowtie2_build = which('bowtie2-build')
run([bowtie2, '-h'])
run([bowtie2_build, '-h'])
except Exception:
try:
dir_bin = opj(dir_dep, get_dep_dir(dir_dep, 'bowtie2'))
bowtie2 = opj(dir_bin, 'bowtie2')
bowtie2_build = opj(dir_bin, 'bowtie2-build')
run([bowtie2, '-h'])
run([bowtie2_build, '-h'])
except Exception:
Log.wrn('Bowtie 2 was not found on this system, trying to '
'download.')
download_file(url, dnld_path)
zip_ref = zipfile.ZipFile(dnld_path, 'r')
zip_ref.extractall(dir_dep)
zip_ref.close()
dir_bin = opj(dir_dep, get_dep_dir(dir_dep, 'bowtie2'))
bowtie2 = opj(dir_bin, 'bowtie2')
bowtie2_build = opj(dir_bin, 'bowtie2-build')
bowtie2_execs = ('', '-align-l', '-align-l-debug', '-align-s',
'-align-s-debug', '-build', '-build-l',
'-build-l-debug', '-build-s', '-build-s-debug',
'-inspect', '-inspect-l', '-inspect-l-debug',
'-inspect-s', '-inspect-s-debug')
for bt2exe in bowtie2_execs:
chmod(
bowtie2 + bt2exe, stat.S_IRWXU | stat.S_IRGRP
| stat.S_IXGRP | stat.S_IROTH | stat.S_IXOTH)
if not ope(bowtie2):
Log.err('Could not download Bowtie 2.')
return None, None
regexp = r'^.*?version\s([\d\.]*)'
v = get_dep_version([bowtie2, '--version'], regexp)
Log.msg('bowtie2 is available:', v + ' ' + bowtie2)
v = get_dep_version([bowtie2_build, '--version'], regexp)
Log.msg('bowtie2-build is available:', v + ' ' + bowtie2_build)
return bowtie2, bowtie2_build
def dnld_refseqs_for_taxid(taxid,
filter_term,
taxonomy,
dir_cache_refseqs,
query='',
db='nuccore'):
ft = None
if filter_term == 'plastid':
ft = '("chloroplast"[filter] OR "plastid"[filter])'
else:
ft = '("' + filter_term + '"[filter])'
tax_terms = tuple(reversed(taxonomy.lineage_for_taxid(taxid)['names']))
for tax_term in tax_terms:
if tax_term is None:
tax_term = taxonomy.scientific_name_for_taxid(taxid)
term = '"RefSeq"[Keyword] AND "{}"[Primary Organism] AND {}'.format(
tax_term, ft)
term = query + term
accs = set(accs_eutil(search_eutil(db, term)))
if len(accs) > 0:
plural = 'sequences'
if len(accs) == 1:
plural = 'sequence'
Log.msg(
'Found {} RefSeq {} {} for'.format(len(accs), filter_term,
plural), tax_term)
# Random sample ###################################################
if len(accs) > 10:
Log.wrn('Using a random sample of ten RefSeq sequences.')
random.seed(a=len(accs), version=2)
accs = set(random.sample(accs, 10))
###################################################################
break
else:
Log.wrn(
'No RefSeq {} sequences were found for'.format(filter_term),
tax_term)
cache_path = opj(
dir_cache_refseqs,
filter_term + '__' + tax_term.replace(' ', '_') + '.fasta')
parsed_fasta_cache = {}
if ope(cache_path):
parsed_fasta_cache = read_fasta(cache_path,
seq_type=SEQ_TYPE_NT,
def_to_first_space=True)
parsed_fasta_cache = seq_records_to_dict(parsed_fasta_cache)
for acc in parsed_fasta_cache:
if acc in accs:
accs.remove(acc)
if len(accs) > 0:
parsed_fasta = dnld_ncbi_seqs(db, list(accs))
parsed_fasta = seq_records_to_dict(parsed_fasta, prepend_acc=True)
parsed_fasta.update(parsed_fasta_cache)
write_fasta(parsed_fasta, cache_path)
return cache_path
def generateTimeseriesPSCSnapViews(self,
out_dir,
nCols,
nRows,
title='',
basename='psc_img',
psc_min_max=PSC_MIN_MAX):
"""
Create a list (size Nt = N timepoints) of figures. Each figure contains the PSC volume corresponding to the
timepoint represented as Nk slices in a matrix-shape
:return:
"""
lf = []
for t in range(self.Nt):
out_graph_vol_fn = opj(
out_dir, '_'.join([basename, str(t).zfill(4)]) + '.png')
if not ope(out_graph_vol_fn):
# Create a figure
fig = plt.figure(figsize=(nCols, nRows))
gs = GridSpec(nRows, nCols)
gs.update(wspace=0.0,
hspace=0.0) # set the spacing between axes.
for i in range(nRows):
for j in range(nCols):
z = i * nCols + j
if z <= self.Nt:
im = self.f_psc_img[:, :, z, t].T
ax = plt.subplot(gs[i, j])
ims = ax.imshow(im,
vmin=-psc_min_max,
vmax=psc_min_max,
cmap='seismic')
ax.set_xticks([])
ax.set_yticks([])
ax.axis('off')
# for tissueName, bin_map, tcol in zip(TISSUE_NAMES, [self.gm_bin,self.wm_bin,self.csf_bin], TISSUE_COLORS):
# cmap1 = colors.LinearSegmentedColormap.from_list('my_cmap', [COL_TRANSP, tcol], 256)
# mask = bin_map[:,:,z].T
# ax.imshow(mask, cmap=cmap1, interpolation='none', alpha=.5)
plt.suptitle(title + ' vol {0}/{1}'.format(t + 1, self.Nt))
# Try to add colorbar at the bottom
fig.subplots_adjust(right=0.9)
cbar_ax = fig.add_axes([0.9, 0.25, 0.01,
0.5]) #left bottom width height
cbar = fig.colorbar(ims,
ticks=[-psc_min_max, 0, psc_min_max],
cax=cbar_ax)
cbar.ax.set_yticklabels([
'-{0}%'.format(psc_min_max), '0',
'+{0}%'.format(psc_min_max)
]) # vertically oriented colorbar
#cbar.set_label('Signal')
# Save
plt.savefig(out_graph_vol_fn)
lf.append(out_graph_vol_fn)
plt.close()
return lf
def makeblastdb_fq(se_fastq_files, pe_fastq_files, dir_blast_fa_trim,
makeblastdb, fpatt):
if len(se_fastq_files) > 0 or len(pe_fastq_files) > 0:
print()
Log.inf('Building BLAST databases for reads.')
if makeblastdb is None:
Log.err('makeblastdb is not available. Cannot continue. Exiting.')
exit(0)
for se in se_fastq_files:
dir_blast_fa_trim_sample = opj(dir_blast_fa_trim, se)
fa_path = se_fastq_files[se]['filter_path_fa']
out_f = opj(dir_blast_fa_trim_sample, se)
se_fastq_files[se]['blast_db_path'] = out_f
if ope(dir_blast_fa_trim_sample):
Log.msg('BLAST database already exists:', se)
else:
make_dirs(dir_blast_fa_trim_sample)
Log.msg(basename(fa_path))
make_blast_db(exec_file=makeblastdb,
in_file=fa_path,
out_file=out_f,
title=se,
dbtype='nucl')
for pe in pe_fastq_files:
dir_blast_fa_trim_sample = opj(dir_blast_fa_trim, pe)
fa_paths = pe_fastq_files[pe]['filter_path_fa']
out_fs = [x.replace('@D@', dir_blast_fa_trim_sample) for x in fpatt]
out_fs = [x.replace('@N@', pe) for x in out_fs]
pe_fastq_files[pe]['blast_db_path'] = out_fs
if ope(dir_blast_fa_trim_sample):
Log.msg('BLAST database already exists:', pe)
else:
make_dirs(dir_blast_fa_trim_sample)
pe_trim_files = zip(fa_paths, out_fs)
for x in pe_trim_files:
Log.msg(basename(x[0]))
make_blast_db(exec_file=makeblastdb,
in_file=x[0],
out_file=x[1],
title=basename(x[1]),
dbtype='nucl')
def combine_aa_fasta(ss, fasta_files, aa_queries_file):
Log.inf('Combining all AA query sequences:', ss)
_ = ''
for fasta_file in fasta_files:
if ope(fasta_file):
with open(fasta_file, 'r') as f:
_ = _ + f.read()
with open(aa_queries_file, 'w') as f:
f.write(_)
def user_protein_accessions(ss, prot_acc_user, dir_cache_prj, taxonomy):
if len(prot_acc_user) > 0:
Log.inf('Reading user provided protein accessions:', ss)
print()
pickle_file = opj(dir_cache_prj, 'ncbi_prot_metadata_cache__' + ss)
acc_old = set()
if ope(pickle_file):
with open(pickle_file, 'rb') as f:
pickled = pickle.load(f)
acc_old = set([x['accessionversion'] for x in pickled])
if acc_old == set(prot_acc_user):
pa_info = pickled
else:
pa_info = summary_eutil('protein', prot_acc_user)
prot_acc = []
prot_info_to_print = []
max_acc_len = 0
for pa in pa_info:
acc = pa['accessionversion']
prot_acc.append(acc)
title = pa['title']
title_split = title.split('[')
taxid = pa['taxid']
if 'organism' in pa:
organism = pa['organism']
else:
organism = taxonomy.scientific_name_for_taxid(taxid)
pa['organism'] = organism
# title = title_split[0]
# title = title.lower().strip()
# title = title.replace('_', ' ').replace('-', ' ')
# title = title.replace(',', '')
# title = title[0].upper() + title[1:] + ' [' + organism + ']'
max_acc_len = max(max_acc_len, len(acc))
prot_info_to_print.append((title, acc))
prot_info_to_print = sorted(prot_info_to_print)
for pi in prot_info_to_print:
title = pi[0]
acc = pi[1]
if len(title) > 80:
title = title[:77] + '...'
Log.msg(acc.rjust(max_acc_len) + ':', title, False)
with open(pickle_file, 'wb') as f:
pickle.dump(pa_info, f, protocol=PICKLE_PROTOCOL)
return prot_acc
else:
return prot_acc_user
def dep_check_sra_toolkit(dir_dep, os_id, dist_id, debian_dists, redhat_dists,
force):
if os_id == 'mac':
url = ('https://ftp-trace.ncbi.nlm.nih.gov/sra/sdk/2.10.8/'
'sratoolkit.2.10.8-mac64.tar.gz')
elif os_id == 'linux':
if dist_id in debian_dists:
url = ('https://ftp-trace.ncbi.nlm.nih.gov/sra/sdk/2.10.8/'
'sratoolkit.2.10.8-ubuntu64.tar.gz')
elif dist_id in redhat_dists:
url = ('https://ftp-trace.ncbi.nlm.nih.gov/sra/sdk/2.10.8/'
'sratoolkit.2.10.8-centos_linux64.tar.gz')
dnld_path = opj(dir_dep, 'sra-toolkit.tar.gz')
fasterq_dump = None
try:
if force is True:
raise
fasterq_dump = which('fasterq-dump')
dir_bin = dirname(fasterq_dump).strip('bin')
_ensure_vdb_cfg(dir_bin)
run(fasterq_dump)
except Exception:
try:
dir_bin = opj(dir_dep, get_dep_dir(dir_dep, 'sratoolkit'))
_ensure_vdb_cfg(dir_bin)
fasterq_dump = opj(dir_bin, 'bin', 'fasterq-dump')
run(fasterq_dump)
except Exception:
Log.wrn('SRA Toolkit was not found on this system, trying to '
'download.')
download_file(url, dnld_path)
tar_ref = tarfile.open(dnld_path, 'r:gz')
tar_ref.extractall(dir_dep)
tar_ref.close()
dir_bin = opj(dir_dep, get_dep_dir(dir_dep, 'sratoolkit'))
fasterq_dump = opj(dir_bin, 'bin', 'fasterq-dump')
_ensure_vdb_cfg(dir_bin)
if not ope(fasterq_dump):
Log.err('Could not download SRA Toolkit.')
return None
v = get_dep_version([fasterq_dump, '--version'], r':\s([\d\.]*)')
if v == '?':
v = get_dep_version([fasterq_dump, '--version'], r'version\s([\d\.]*)')
Log.msg('fasterq-dump is available:', v + ' ' + fasterq_dump)
return fasterq_dump
def dep_check_vsearch(dir_dep, os_id, dist_id, debian_dists, redhat_dists,
force):
if os_id == 'mac':
url = ('https://github.com/torognes/vsearch/releases/download/v2.15.0/'
'vsearch-2.15.0-macos-x86_64.tar.gz')
elif os_id == 'linux':
if dist_id in debian_dists:
url = ('https://github.com/torognes/vsearch/releases/download/'
'v2.15.0/vsearch-2.15.0-linux-x86_64.tar.gz')
elif dist_id in redhat_dists:
url = ('https://github.com/torognes/vsearch/releases/download/'
'v2.15.0/vsearch-2.15.0-linux-x86_64.tar.gz')
dnld_path = opj(dir_dep, 'vsearch.tar.gz')
try:
if force is True:
raise
vsearch = which('vsearch')
run(vsearch)
except Exception:
try:
dir_bin = opj(dir_dep, get_dep_dir(dir_dep, 'vsearch'))
vsearch = opj(dir_bin, 'bin', 'vsearch')
run(vsearch)
except Exception:
Log.wrn(
'Vsearch was not found on this system, trying to download.')
download_file(url, dnld_path)
tar_ref = tarfile.open(dnld_path, 'r:gz')
tar_ref.extractall(dir_dep)
tar_ref.close()
try:
dir_bin = opj(dir_dep, get_dep_dir(dir_dep, 'vsearch'))
vsearch = opj(dir_bin, 'bin', 'vsearch')
if not ope(vsearch):
Log.err('Could not download Vsearch.')
return None
else:
run(vsearch)
except Exception:
Log.err('Vsearch was downloaded, but does not execute.')
Log.msg('Try downloading and installing it manually from: '
'https://github.com/torognes/vsearch')
return None
v = get_dep_version([vsearch, '-version'], r'vsearch\sv([\d\.]*)')
Log.msg('Vsearch is available:', v + ' ' + vsearch)
return vsearch
def dep_check_trimmomatic(dir_dep):
url = ('http://www.usadellab.org/cms/uploads/supplementary/Trimmomatic/'
'Trimmomatic-0.39.zip')
dnld_path = opj(dir_dep, 'Trimmomatic-0.39.zip')
dir_bin = opj(dir_dep, 'Trimmomatic-0.39')
trimmomatic = opj(dir_bin, 'trimmomatic-0.39.jar')
if not ope(trimmomatic):
download_file(url, dnld_path)
zip_ref = zipfile.ZipFile(dnld_path, 'r')
zip_ref.extractall(dir_dep)
zip_ref.close()
if not ope(trimmomatic):
Log.err('Could not download Trimmomatic.')
return None, None
v = get_dep_version(['java', '-jar', trimmomatic, '-version'], r'\d+\.\d+')
Log.msg('Trimmomatic is available:', v + ' ' + trimmomatic)
path_adapters = _write_trimmomatic_adapters_file(dir_dep)
return trimmomatic, path_adapters
def user_entrez_search(ss, queries, dir_cache_prj, requery_after):
dnld_needed = True
accs = []
if len(queries) != 0:
time_stamp_now = datetime.datetime.now()
time_stamp_file = opj(dir_cache_prj, 'ncbi_prot_time_stamp__' + ss)
time_stamp = None
if ope(time_stamp_file):
with open(time_stamp_file, 'rb') as f:
time_stamp = pickle.load(f)
time_diff = time_stamp_now - time_stamp
if time_diff < requery_after:
dnld_needed = False
if dnld_needed is True:
Log.inf('Searching for protein sequences on NCBI:', ss)
for q in queries:
esearch_results = search_eutil(db='protein', term=q)
accs = accs + accs_eutil(esearch_results)
with open(time_stamp_file, 'wb') as f:
pickle.dump(datetime.datetime.now(),
f,
protocol=PICKLE_PROTOCOL)
else:
days = requery_after.total_seconds() / 60 / 60 / 24
days = '{:.2f}'.format(days)
Log.inf(
'NCBI results are less than ' + days +
' day(s) old. Will not search again.:', ss)
pickle_file = opj(dir_cache_prj, 'ncbi_prot_metadata_cache__' + ss)
if ope(pickle_file):
with open(pickle_file, 'rb') as f:
pickled = pickle.load(f)
accs = [x['accessionversion'] for x in pickled]
return accs
def dep_check_spades(dir_dep, os_id, force):
if os_id == 'mac':
url = ('http://cab.spbu.ru/files/release3.14.1/'
'SPAdes-3.14.1-Darwin.tar.gz')
elif os_id == 'linux':
url = ('http://cab.spbu.ru/files/release3.14.1/'
'SPAdes-3.14.1-Linux.tar.gz')
dnld_path = opj(dir_dep, 'SPAdes.tar.gz')
try:
if force is True:
raise
spades = which('spades.py')
run([PY3, spades])
except Exception:
try:
dir_bin = opj(dir_dep, get_dep_dir(dir_dep, 'SPAdes'))
spades = opj(dir_bin, 'bin', 'spades.py')
run([PY3, spades])
except Exception:
Log.wrn('SPAdes was not found on this system, trying to download.')
try:
download_file(url, dnld_path)
tar_ref = tarfile.open(dnld_path, 'r:gz')
tar_ref.extractall(dir_dep)
tar_ref.close()
except Exception:
Log.err('Could not download SPAdes.')
return None
try:
dir_bin = opj(dir_dep, get_dep_dir(dir_dep, 'SPAdes'))
spades = opj(dir_bin, 'bin', 'spades.py')
# replace_line_in_file(spades,
# '#!/usr/bin/env python',
# '#!/usr/bin/env python3')
if ope(spades):
run([PY3, spades])
else:
Log.err('Could not download SPAdes.')
return None
except Exception:
Log.err('SPAdes was downloaded, but does not execute.')
return None
v = get_dep_version([PY3, spades, '--version'], r'^.*SPAdes.*v([\d\.]*)')
Log.msg('SPAdes is available:', v + ' ' + spades)
return spades
def filtered_fq_to_fa(se_fastq_files, pe_fastq_files, dir_fa_trim_data, seqtk,
fpatt):
if len(se_fastq_files) > 0 or len(pe_fastq_files) > 0:
print()
Log.inf('Converting FASTQ to FASTA using Seqtk.')
if seqtk is None:
Log.err('seqtk is not available. Cannot continue. Exiting.')
exit(0)
for se in se_fastq_files:
dir_fa_trim_data_sample = opj(dir_fa_trim_data, se)
fq_path = se_fastq_files[se]['filter_path_fq']
out_f = opj(dir_fa_trim_data_sample, se + '.fasta')
se_fastq_files[se]['filter_path_fa'] = out_f
if ope(dir_fa_trim_data_sample):
Log.msg('Filtered FASTA files already exist:', se)
else:
make_dirs(dir_fa_trim_data_sample)
Log.msg(basename(fq_path))
seqtk_fq_to_fa(seqtk, fq_path, out_f)
for pe in pe_fastq_files:
dir_fa_trim_data_sample = opj(dir_fa_trim_data, pe)
fq_paths = pe_fastq_files[pe]['filter_path_fq']
out_fs = [x.replace('@D@', dir_fa_trim_data_sample) for x in fpatt]
out_fs = [x.replace('@N@', pe) for x in out_fs]
pe_fastq_files[pe]['filter_path_fa'] = out_fs
if ope(dir_fa_trim_data_sample):
Log.msg('Filtered FASTA files already exist:', pe)
else:
make_dirs(dir_fa_trim_data_sample)
pe_trim_files = zip(fq_paths, out_fs)
for x in pe_trim_files:
Log.msg(basename(x[0]))
seqtk_fq_to_fa(seqtk, x[0], x[1])
def _write_trimmomatic_adapters_file(dir_dep):
path_adapters = opj(dir_dep, 'trimmomatic_adapters.fasta')
adapters = ('>TruSeq2_SE'
'AGATCGGAAGAGCTCGTATGCCGTCTTCTGCTTG'
'>TruSeq2_PE_f'
'AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT'
'>TruSeq2_PE_r'
'AGATCGGAAGAGCGGTTCAGCAGGAATGCCGAG'
'>TruSeq3_IndexedAdapter'
'AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC'
'>TruSeq3_UniversalAdapter'
'AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTA'
'>PrefixPE/1'
'AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT'
'>PrefixPE/2'
'CAAGCAGAAGACGGCATACGAGATCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT'
'>PCR_Primer1'
'AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT'
'>PCR_Primer1_rc'
'AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTGGTCGCCGTATCATT'
'>PCR_Primer2'
'CAAGCAGAAGACGGCATACGAGATCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT'
'>PCR_Primer2_rc'
'AGATCGGAAGAGCGGTTCAGCAGGAATGCCGAGACCGATCTCGTATGCCGTCTTCTGCTTG'
'>FlowCell1'
'TTTTTTTTTTAATGATACGGCGACCACCGAGATCTACAC'
'>FlowCell2'
'TTTTTTTTTTCAAGCAGAAGACGGCATACGA'
'>PrefixPE/1'
'TACACTCTTTCCCTACACGACGCTCTTCCGATCT'
'>PrefixPE/2'
'GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT'
'>PE1'
'TACACTCTTTCCCTACACGACGCTCTTCCGATCT'
'>PE1_rc'
'AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTA'
'>PE2'
'GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT'
'>PE2_rc'
'AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC')
if not ope(path_adapters):
Log.msg('Writing Trimmomatic adapter files: ' + path_adapters)
with open(path_adapters, mode='w') as f:
f.write(adapters)
return path_adapters
def saveBinaryMasks(self, outdir, base):
"""
Saves the computed binary masks in directory
:param outdir: string, path to the output directory
:param base: string, prefix for the filenaming
"""
self.getCarpetData()
total_bin = self.csf_bin + 2 * self.gm_bin + 3 * self.wm_bin
arrays = [self.gm_bin, self.wm_bin, self.csf_bin, total_bin]
tissues = TISSUE_NAMES + ['all']
for arr, tissue in zip(arrays, tissues):
img = nib.Nifti1Image(arr, affine=self.f_aff, header=self.f_hdr)
basename = '_'.join([base, tissue, 'binary']) + '.nii.gz'
bin_fn = opj(outdir, basename)
if not ope(bin_fn):
nib.save(img, filename=bin_fn)
def _should_run_bt2(taxid, taxonomy, bt2_order, bowtie2, bowtie2_build):
dbs = OrderedDict()
for x in bt2_order:
db_path_ok = False
if x == MT:
if taxonomy.is_eukaryote(taxid) is True:
if bt2_order[MT] == '':
dbs[MT] = MT
db_path_ok = True
elif x == PT:
if taxonomy.is_eukaryote(taxid) is True:
if taxonomy.contains_plastid(taxid) is True:
if bt2_order[PT] == '':
dbs[PT] = PT
db_path_ok = True
if db_path_ok is False:
db_path = bt2_order[x]
if ope(db_path) and isfile(db_path):
dbs[x] = db_path
else:
Log.err('File not found:', db_path)
exit(1)
if len(dbs) > 0:
if bowtie2 is None:
Log.err('bowtie2 is not available. ' + 'Cannot continue. Exiting.')
exit(0)
if bowtie2_build is None:
Log.err('bowtie2-build is not available. ' +
'Cannot continue. Exiting.')
exit(0)
return dbs
def pfam_uniprot_accessions(ss, pfam_acc, tax_ids, dir_cache_pfam_acc):
if len(pfam_acc) > 0:
Log.inf('Downloading UniProt accessions for Pfam accessions:', ss)
pfam_seqs_list = []
for pa in pfam_acc:
pfam_id = pfam_entry(pa)[0]['id']
Log.msg(pa + ':', pfam_id)
_ = opj(dir_cache_pfam_acc, pa + '__' + ss)
if ope(_):
with open(_, 'rb') as f:
acc = pickle.load(f)
pfam_seqs_list = pfam_seqs_list + acc
else:
# Note: the results may include "obsolete" accessions.
# This is not a problem, they will not appear in the set of
# downloaded sequences from UniProt.
acc = pfam_seqs(query=pa)
pfam_seqs_list = pfam_seqs_list + acc
with open(_, 'wb') as f:
pickle.dump(acc, f, protocol=PICKLE_PROTOCOL)
pfam_uniprot_acc = prot_ids_for_tax_ids(pfam_seqs_list, tax_ids)
return pfam_uniprot_acc
def makeblastdb_assemblies(assemblies, dir_prj_blast_assmbl, makeblastdb):
if len(assemblies) > 0:
print()
Log.inf('Building BLAST databases for assemblies.')
if makeblastdb is None:
Log.err('makeblastdb is not available. Cannot continue. Exiting.')
exit(0)
for a in assemblies:
assmbl_name = a['name']
assmbl_blast_db_dir = opj(dir_prj_blast_assmbl, assmbl_name)
assmbl_blast_db_file = opj(assmbl_blast_db_dir, assmbl_name)
a['blast_db_path'] = assmbl_blast_db_file
if ope(assmbl_blast_db_dir):
Log.msg('BLAST database already exists:', assmbl_name)
else:
Log.msg(assmbl_name)
make_dirs(assmbl_blast_db_dir)
make_blast_db(exec_file=makeblastdb,
in_file=a['path'],
out_file=assmbl_blast_db_file,
title=assmbl_name)
## The classes folder name.
class_folder_name = args.classdir
## The classes folder path.
class_folder_path = opa(opj(output_path, class_folder_name))
#
# Does the class subfolder already exist?
if not os.path.isdir(class_folder_path):
lg.info(" * Making class folder path '%s'" % (class_folder_path))
os.mkdir(class_folder_path)
else:
lg.info(" * Class folder '%s' already exists." % (class_folder_name))
lg.info(" *")
## The __init__.py file path.
init_file_path = opj(class_folder_path, "__init__.py")
#
if not ope(init_file_path):
with open(init_file_path, "w") as f:
f.write("")
# Write out the class file string to the output path.
with open(os.path.join(output_path, class_folder_name, "%s.py"%(class_name_lower)), "w") as cf:
cf.write(sc)
# Write out the test file string to the output path.
with open(os.path.join(output_path, class_folder_name, "test_%s.py"%(class_name_lower)), "w") as tf:
tf.write(st)
def main():
"""Run the script."""
# Prepare initial logger (before we know the log file path) --------------
prj_log_file_suffix = time_stamp() + '.log'
log_stream = StringIO()
Log.set_colors(COLORS)
Log.set_file(log_stream)
Log.set_write(True)
# Prepare configuration directory ----------------------------------------
if ope(DIR_CFG):
Log.inf('Found configuration directory:', DIR_CFG)
else:
Log.wrn('Creating configuration directory:', DIR_CFG)
make_dirs(DIR_CFG)
print()
# Check for dependencies -------------------------------------------------
Log.inf('Checking for dependencies.')
make_dirs(DIR_DEP)
make_dirs(DIR_KRK)
seqtk = deps.dep_check_seqtk(DIR_DEP, FORCE_DEPS)
trimmomatic, adapters = deps.dep_check_trimmomatic(DIR_DEP)
fasterq_dump = deps.dep_check_sra_toolkit(DIR_DEP, OS_ID, DIST_ID,
DEBIAN_DISTS, REDHAT_DISTS,
FORCE_DEPS)
makeblastdb, _, tblastn = deps.dep_check_blast(DIR_DEP, OS_ID, DIST_ID,
DEBIAN_DISTS, REDHAT_DISTS,
FORCE_DEPS)
vsearch = deps.dep_check_vsearch(DIR_DEP, OS_ID, DIST_ID, DEBIAN_DISTS,
REDHAT_DISTS, FORCE_DEPS)
spades = deps.dep_check_spades(DIR_DEP, OS_ID, FORCE_DEPS)
bowtie2, bowtie2_build = deps.dep_check_bowtie2(DIR_DEP, OS_ID, FORCE_DEPS)
rcorrector = deps.dep_check_rcorrector(DIR_DEP, FORCE_DEPS)
kraken2, kraken2_build = deps.dep_check_kraken2(DIR_DEP, OS_ID,
RELEASE_NAME, FORCE_DEPS)
print()
kraken2_dbs = deps.dnld_kraken2_dbs(DIR_KRK)
if INSTALL_DEPS is True or DNLD_KRAKEN_DBS is True:
exit(0)
print()
# Initialize NCBI taxonomy database --------------------------------------
tax = Taxonomy()
if tax.is_initialized() is False:
tax.init(data_dir_path=DIR_TAX, logger=Log)
print()
# Parse configuration file -----------------------------------------------
Log.inf('Reading configuration file:', CONFIG_FILE_PATH)
_ = config_file_parse(CONFIG_FILE_PATH, tax)
allow_no_stop_cod = _['allow_no_stop_cod']
allow_no_strt_cod = _['allow_no_strt_cod']
allow_non_aug = _['allow_non_aug']
blast_1_evalue = _['blast_1_evalue']
blast_1_max_hsps = _['blast_1_max_hsps']
blast_1_qcov_hsp_perc = _['blast_1_qcov_hsp_perc']
blast_1_best_hit_overhang = _['blast_1_best_hit_overhang']
blast_1_best_hit_score_edge = _['blast_1_best_hit_score_edge']
blast_1_max_target_seqs = _['blast_1_max_target_seqs']
blast_2_evalue = _['blast_2_evalue']
blast_2_max_hsps = _['blast_2_max_hsps']
blast_2_qcov_hsp_perc = _['blast_2_qcov_hsp_perc']
blast_2_best_hit_overhang = _['blast_2_best_hit_overhang']
blast_2_best_hit_score_edge = _['blast_2_best_hit_score_edge']
blast_2_max_target_seqs = _['blast_2_max_target_seqs']
dir_out = _['output_directory']
email = _['email']
requery_after = _['requery_after']
fq_pe = _['fq_pe']
fq_se = _['fq_se']
should_run_rcorrector = _['should_run_rcorrector']
should_run_ipr = _['should_run_ipr']
bt2_order = _['bt2_order']
kraken_confidence = _['kraken_confidence']
krkn_order = _['krkn_order']
prepend_assmbl = _['prepend_assmbl']
prj_name = _['project_name']
sras = _['sras']
tax_group = _['tax_group']
# tax_group_name = _['tax_group_name']
tax_ids_user = _['tax_ids']
user_assemblies = _['assmbl']
print()
# Parse search strategies file -------------------------------------------
if SS_FILE_PATH is not None:
Log.inf('Reading search strategies file:', SS_FILE_PATH)
sss = ss_file_parse(SS_FILE_PATH)
else:
Log.wrn('Search strategies file was not provided.\n' +
'Will process reads, assemblies and then stop.')
sss = dict()
print()
# Create output directory ------------------------------------------------
if dir_out is not None:
if ope(dir_out):
Log.inf('Found output directory:', dir_out)
else:
Log.wrn('Creating output directory:', dir_out)
make_dirs(dir_out)
print()
# Write Kakapo version information to the output directory ---------------
version_file = opj(dir_out, 'kakapo_version.txt')
if ope(version_file):
with open(version_file, 'r') as f:
version_prev = f.read().strip()
if __version__ != version_prev:
Log.wrn('The output directory contains data produced by a ' +
'different version of Kakapo: ' + version_prev +
'.\nThe currently running version is: ' + __version__ +
'.\n' +
'Delete "kakapo_version.txt" file located in the ' +
'output directory if you would like to continue.')
exit(0)
with open(version_file, 'w') as f:
f.write(__version__)
# Create subdirectories in the output directory --------------------------
_ = prepare_output_directories(dir_out, prj_name)
dir_temp = _['dir_temp']
dir_cache_pfam_acc = _['dir_cache_pfam_acc']
dir_cache_fq_minlen = _['dir_cache_fq_minlen']
dir_cache_prj = _['dir_cache_prj']
dir_cache_refseqs = _['dir_cache_refseqs']
dir_prj_logs = _['dir_prj_logs']
dir_prj_queries = _['dir_prj_queries']
dir_fq_data = _['dir_fq_data']
dir_fq_cor_data = _['dir_fq_cor_data']
dir_fq_trim_data = _['dir_fq_trim_data']
dir_fq_filter_bt2_data = _['dir_fq_filter_bt2_data']
dir_fq_filter_krkn2_data = _['dir_fq_filter_krkn2_data']
dir_fa_trim_data = _['dir_fa_trim_data']
dir_blast_fa_trim = _['dir_blast_fa_trim']
dir_prj_blast_results_fa_trim = _['dir_prj_blast_results_fa_trim']
dir_prj_vsearch_results_fa_trim = _['dir_prj_vsearch_results_fa_trim']
dir_prj_spades_assemblies = _['dir_prj_spades_assemblies']
dir_prj_blast_assmbl = _['dir_prj_blast_assmbl']
dir_prj_assmbl_blast_results = _['dir_prj_assmbl_blast_results']
dir_prj_transcripts = _['dir_prj_transcripts']
dir_prj_ips = _['dir_prj_ips']
dir_prj_transcripts_combined = _['dir_prj_transcripts_combined']
# Prepare logger ---------------------------------------------------------
prj_log_file = opj(dir_prj_logs, prj_name + '_' + prj_log_file_suffix)
with open(prj_log_file, 'w') as f:
f.write(SCRIPT_INFO.strip() + '\n\n' + log_stream.getvalue())
Log.set_colors(COLORS)
Log.set_file(prj_log_file)
Log.set_write(True)
log_stream.close()
# Resolve descending taxonomy nodes --------------------------------------
tax_ids = tax.all_descending_taxids_for_taxids([tax_group])
# Pfam uniprot accessions ------------------------------------------------
pfam_uniprot_acc = OrderedDict()
for ss in sss:
pfam_acc = sss[ss]['pfam_families']
pfam_uniprot_acc[ss] = pfam_uniprot_accessions(ss, pfam_acc, tax_ids,
dir_cache_pfam_acc)
# Download Pfam uniprot sequences if needed ------------------------------
aa_uniprot_files = OrderedDict()
for ss in sss:
aa_uniprot_files[ss] = opj(dir_prj_queries,
'aa_uniprot__' + ss + '.fasta')
# ToDo: add support for the requery_after parameter.
dnld_pfam_uniprot_seqs(ss, pfam_uniprot_acc[ss], aa_uniprot_files[ss],
dir_cache_prj)
# User provided entrez query ---------------------------------------------
prot_acc_user_from_query = OrderedDict()
for ss in sss:
entrez_queries = sss[ss]['entrez_search_queries']
prot_acc_user_from_query[ss] = user_entrez_search(
ss, entrez_queries, dir_cache_prj, requery_after)
# User provided protein accessions ---------------------------------------
prot_acc_user = OrderedDict()
for ss in sss:
print()
prot_acc_all = sorted(
set(sss[ss]['ncbi_accessions_aa'] + prot_acc_user_from_query[ss]))
prot_acc_user[ss] = user_protein_accessions(ss, prot_acc_all,
dir_cache_prj, tax)
# Download from NCBI if needed -------------------------------------------
aa_prot_ncbi_files = OrderedDict()
for ss in sss:
aa_prot_ncbi_files[ss] = opj(dir_prj_queries,
'aa_prot_ncbi__' + ss + '.fasta')
prot_acc_user[ss] = dnld_prot_seqs(ss, prot_acc_user[ss],
aa_prot_ncbi_files[ss],
dir_cache_prj)
# User provided protein sequences ----------------------------------------
aa_prot_user_files = OrderedDict()
for ss in sss:
user_queries = sss[ss]['fasta_files_aa']
aa_prot_user_files[ss] = opj(dir_prj_queries,
'aa_prot_user__' + ss + '.fasta')
user_aa_fasta(ss, user_queries, aa_prot_user_files[ss])
# Combine all AA queries -------------------------------------------------
print()
aa_queries_files = OrderedDict()
for ss in sss:
aa_queries_files[ss] = opj(dir_prj_queries, 'aa_all__' + ss + '.fasta')
combine_aa_fasta(ss, [
aa_uniprot_files[ss], aa_prot_ncbi_files[ss],
aa_prot_user_files[ss]
], aa_queries_files[ss])
# Filter AA queries ------------------------------------------------------
prot_acc_user_filtered = OrderedDict()
for ss in sss:
min_query_length = sss[ss]['min_query_length']
max_query_length = sss[ss]['max_query_length']
max_query_identity = sss[ss]['max_query_identity']
# Dereplicate all queries
filter_queries(ss,
aa_queries_files[ss],
min_query_length,
max_query_length,
max_query_identity,
vsearch,
prot_acc_user[ss],
overwrite=True)
# Dereplicate only NCBI queries. CDS for these will be downloaded
# later for reference.
if ope(aa_prot_ncbi_files[ss]):
prot_acc_user_filtered[ss] = filter_queries(ss,
aa_prot_ncbi_files[ss],
min_query_length,
max_query_length,
max_query_identity,
vsearch,
prot_acc_user[ss],
overwrite=False,
logging=False)
# Download SRA run metadata if needed ------------------------------------
sra_runs_info, sras_acceptable = dnld_sra_info(sras, dir_cache_prj)
# Download SRA run FASTQ files if needed ---------------------------------
x, y, z = dnld_sra_fastq_files(sras_acceptable, sra_runs_info, dir_fq_data,
fasterq_dump, THREADS, dir_temp)
se_fastq_files_sra = x
pe_fastq_files_sra = y
sra_runs_info = z
# User provided FASTQ files ----------------------------------------------
se_fastq_files_usr, pe_fastq_files_usr = user_fastq_files(fq_se, fq_pe)
# Collate FASTQ file info ------------------------------------------------
se_fastq_files = se_fastq_files_sra.copy()
se_fastq_files.update(se_fastq_files_usr)
pe_fastq_files = pe_fastq_files_sra.copy()
pe_fastq_files.update(pe_fastq_files_usr)
def gc_tt(k, d, tax):
taxid = d[k]['tax_id']
gc = tax.genetic_code_for_taxid(taxid)
d[k]['gc_id'] = gc
d[k]['gc_tt'] = TranslationTable(gc)
gc_mito = None
tt_mito = None
gc_plastid = None
tt_plastid = None
if tax.is_eukaryote(taxid) is True:
gc_mito = tax.mito_genetic_code_for_taxid(taxid)
if gc_mito != '0':
tt_mito = TranslationTable(gc_mito)
if tax.contains_plastid(taxid) is True:
gc_plastid = tax.plastid_genetic_code_for_taxid(taxid)
if gc_plastid != '0':
tt_plastid = TranslationTable(gc_plastid)
d[k]['gc_id_mito'] = gc_mito
d[k]['gc_tt_mito'] = tt_mito
d[k]['gc_id_plastid'] = gc_plastid
d[k]['gc_tt_plastid'] = tt_plastid
for se in se_fastq_files:
gc_tt(se, se_fastq_files, tax)
for pe in pe_fastq_files:
gc_tt(pe, pe_fastq_files, tax)
# Minimum acceptable read length -----------------------------------------
min_accept_read_len(se_fastq_files, pe_fastq_files, dir_temp,
dir_cache_fq_minlen, vsearch)
# Run Rcorrector ---------------------------------------------------------
run_rcorrector(se_fastq_files, pe_fastq_files, dir_fq_cor_data, rcorrector,
THREADS, dir_temp, should_run_rcorrector)
# File name patterns -----------------------------------------------------
a, b, c, d, e = file_name_patterns()
pe_trim_fq_file_patterns = a
pe_trim_fa_file_patterns = b
pe_blast_db_file_patterns = c
pe_blast_results_file_patterns = d
pe_vsearch_results_file_patterns = e
# Run Trimmomatic --------------------------------------------------------
run_trimmomatic(se_fastq_files, pe_fastq_files, dir_fq_trim_data,
trimmomatic, adapters, pe_trim_fq_file_patterns, THREADS)
# Run Bowtie 2 -----------------------------------------------------------
run_bt2_fq(se_fastq_files, pe_fastq_files, dir_fq_filter_bt2_data, bowtie2,
bowtie2_build, THREADS, dir_temp, bt2_order,
pe_trim_fq_file_patterns, tax, dir_cache_refseqs)
# Run Kraken2 ------------------------------------------------------------
run_kraken2(krkn_order, kraken2_dbs, se_fastq_files, pe_fastq_files,
dir_fq_filter_krkn2_data, kraken_confidence, kraken2, THREADS,
dir_temp, pe_trim_fq_file_patterns)
se_fastq_files = OrderedDict(se_fastq_files)
pe_fastq_files = OrderedDict(pe_fastq_files)
se_fastq_files = OrderedDict(
sorted(se_fastq_files.items(), key=lambda x: x[1]['filter_path_fq']))
pe_fastq_files = OrderedDict(
sorted(pe_fastq_files.items(), key=lambda x: x[1]['filter_path_fq']))
# Stop After Filter ------------------------------------------------------
if STOP_AFTER_FILTER is True:
Log.wrn('Stopping after Kraken2/Bowtie2 filtering step as requested.')
exit(0)
# Convert filtered FASTQ files to FASTA ----------------------------------
filtered_fq_to_fa(se_fastq_files, pe_fastq_files, dir_fa_trim_data, seqtk,
pe_trim_fa_file_patterns)
# Run makeblastdb on reads -----------------------------------------------
makeblastdb_fq(se_fastq_files, pe_fastq_files, dir_blast_fa_trim,
makeblastdb, pe_blast_db_file_patterns)
# Check if there are any query sequences.
any_queries = False
for ss in sss:
if stat(aa_queries_files[ss]).st_size == 0:
continue
else:
any_queries = True
# Run tblastn on reads ---------------------------------------------------
for ss in sss:
if stat(aa_queries_files[ss]).st_size == 0:
continue
changed_blast_1 = run_tblastn_on_reads(
se_fastq_files, pe_fastq_files, aa_queries_files[ss], tblastn,
blast_1_evalue, blast_1_max_hsps, blast_1_qcov_hsp_perc,
blast_1_best_hit_overhang, blast_1_best_hit_score_edge,
blast_1_max_target_seqs, dir_prj_blast_results_fa_trim,
pe_blast_results_file_patterns, ss, THREADS, seqtk, vsearch,
dir_cache_prj)
if changed_blast_1 is True:
if ope(dir_prj_vsearch_results_fa_trim):
rmtree(dir_prj_vsearch_results_fa_trim)
if ope(dir_prj_spades_assemblies):
rmtree(dir_prj_spades_assemblies)
if ope(dir_prj_blast_assmbl):
rmtree(dir_prj_blast_assmbl)
if ope(dir_prj_assmbl_blast_results):
rmtree(dir_prj_assmbl_blast_results)
if ope(dir_prj_transcripts):
rmtree(dir_prj_transcripts)
if ope(dir_prj_transcripts_combined):
rmtree(dir_prj_transcripts_combined)
prepare_output_directories(dir_out, prj_name)
# Run vsearch on reads ---------------------------------------------------
# should_run_vsearch = False
# for ss in sss:
# if stat(aa_queries_files[ss]).st_size == 0:
# continue
# else:
# should_run_vsearch = True
# break
# if should_run_vsearch is True:
# print()
# Log.inf('Checking if Vsearch should be run.')
for ss in sss:
if stat(aa_queries_files[ss]).st_size == 0:
continue
print()
Log.inf('Checking if Vsearch should be run:', ss)
run_vsearch_on_reads(se_fastq_files, pe_fastq_files, vsearch,
dir_prj_vsearch_results_fa_trim,
pe_vsearch_results_file_patterns, ss, seqtk)
# Run SPAdes -------------------------------------------------------------
# should_run_spades = False
# for ss in sss:
# if stat(aa_queries_files[ss]).st_size == 0:
# continue
# else:
# should_run_spades = True
# break
# if should_run_spades is True:
# print()
# Log.inf('Checking if SPAdes should be run.')
for ss in sss:
if stat(aa_queries_files[ss]).st_size == 0:
for se in se_fastq_files:
se_fastq_files[se]['spades_assembly' + '__' + ss] = None
for pe in pe_fastq_files:
pe_fastq_files[pe]['spades_assembly' + '__' + ss] = None
continue
print()
Log.inf('Checking if SPAdes should be run:', ss)
run_spades(se_fastq_files, pe_fastq_files, dir_prj_spades_assemblies,
spades, dir_temp, ss, THREADS, RAM)
# Combine SPAdes and user provided assemblies ----------------------------
assemblies = combine_assemblies(se_fastq_files, pe_fastq_files,
user_assemblies, tax, sss)
# Run makeblastdb on assemblies -----------------------------------------
makeblastdb_assemblies(assemblies, dir_prj_blast_assmbl, makeblastdb)
if any_queries is False:
Log.wrn('No query sequences were provided.')
# Run tblastn on assemblies ----------------------------------------------
for ss in sss:
if stat(aa_queries_files[ss]).st_size == 0:
continue
should_run_tblastn = False
for a in assemblies:
assmbl_src = a['src']
assmbl_name = a['name']
if assmbl_src != 'user_fasta':
if assmbl_name.endswith('__' + ss):
should_run_tblastn = True
break
else:
should_run_tblastn = True
break
if should_run_tblastn is False:
print()
Log.inf('Will not run BLAST. No transcripts exist:', ss)
continue
blast_2_evalue_ss = sss[ss]['blast_2_evalue']
blast_2_max_hsps_ss = sss[ss]['blast_2_max_hsps']
blast_2_qcov_hsp_perc_ss = sss[ss]['blast_2_qcov_hsp_perc']
blast_2_best_hit_overhang_ss = sss[ss]['blast_2_best_hit_overhang']
blast_2_best_hit_score_edge_ss = sss[ss]['blast_2_best_hit_score_edge']
blast_2_max_target_seqs_ss = sss[ss]['blast_2_max_target_seqs']
if blast_2_evalue_ss is None:
blast_2_evalue_ss = blast_2_evalue
if blast_2_max_hsps_ss is None:
blast_2_max_hsps_ss = blast_2_max_hsps
if blast_2_qcov_hsp_perc_ss is None:
blast_2_qcov_hsp_perc_ss = blast_2_qcov_hsp_perc
if blast_2_best_hit_overhang_ss is None:
blast_2_best_hit_overhang_ss = blast_2_best_hit_overhang
if blast_2_best_hit_score_edge_ss is None:
blast_2_best_hit_score_edge_ss = blast_2_best_hit_score_edge
if blast_2_max_target_seqs_ss is None:
blast_2_max_target_seqs_ss = blast_2_max_target_seqs
run_tblastn_on_assemblies(
ss, assemblies, aa_queries_files[ss], tblastn,
dir_prj_assmbl_blast_results, blast_2_evalue_ss,
blast_2_max_hsps_ss, blast_2_qcov_hsp_perc_ss,
blast_2_best_hit_overhang_ss, blast_2_best_hit_score_edge_ss,
blast_2_max_target_seqs_ss, THREADS, dir_cache_prj, dir_prj_ips)
# Prepare BLAST hits for analysis: find ORFs, translate ------------------
for ss in sss:
if stat(aa_queries_files[ss]).st_size == 0:
continue
min_target_orf_len_ss = sss[ss]['min_target_orf_length']
max_target_orf_len_ss = sss[ss]['max_target_orf_length']
organelle = sss[ss]['organelle']
blast_2_qcov_hsp_perc_ss = sss[ss]['blast_2_qcov_hsp_perc']
if blast_2_qcov_hsp_perc_ss is None:
blast_2_qcov_hsp_perc_ss = blast_2_qcov_hsp_perc
find_orfs_translate(ss, assemblies, dir_prj_transcripts, seqtk,
dir_temp, prepend_assmbl, min_target_orf_len_ss,
max_target_orf_len_ss, allow_non_aug,
allow_no_strt_cod, allow_no_stop_cod, tax,
tax_group, tax_ids_user, blast_2_qcov_hsp_perc_ss,
organelle)
# GFF3 files from kakapo results JSON files ------------------------------
# print()
for ss in sss:
if stat(aa_queries_files[ss]).st_size == 0:
continue
gff_from_json(ss, assemblies, dir_prj_ips,
dir_prj_transcripts_combined, prj_name)
# Run InterProScan 5 -----------------------------------------------------
if should_run_ipr is True:
print()
ss_names = tuple(sss.keys())
# Determine the length of printed strings, for better spacing --------
max_title_a_len = 0
max_run_id_len = 0
for a in assemblies:
for ss in ss_names:
if 'transcripts_aa_orf_fasta_file__' + ss not in a:
continue
aa_file = a['transcripts_aa_orf_fasta_file__' + ss]
if aa_file is None:
continue
assmbl_name = a['name']
run_id = ss + '_' + assmbl_name
max_run_id_len = max(len(run_id), max_run_id_len)
seqs = seq_records_to_dict(read_fasta(aa_file, SEQ_TYPE_AA))
# Filter all ORFs except the first one.
for seq_def in tuple(seqs.keys()):
seq_def_prefix = seq_def.split(' ')[0]
if seq_def_prefix.endswith('ORF001'):
max_title_a_len = max(len(seq_def_prefix),
max_title_a_len)
max_title_a_len += 2
max_run_id_len += 2
# --------------------------------------------------------------------
parallel_run_count = min(THREADS, len(ss_names))
def run_inter_pro_scan_parallel(ss):
if stat(aa_queries_files[ss]).st_size == 0:
return
run_inter_pro_scan(ss, assemblies, email, dir_prj_ips,
dir_cache_prj, parallel_run_count,
max_title_a_len, max_run_id_len)
# GFF3 files from kakapo and InterProScan 5 results JSON files
gff_from_json(ss, assemblies, dir_prj_ips,
dir_prj_transcripts_combined, prj_name)
Parallel(n_jobs=parallel_run_count, verbose=0,
require='sharedmem')(delayed(run_inter_pro_scan_parallel)(ss)
for ss in ss_names)
# Download CDS for NCBI protein queries ----------------------------------
print()
prot_cds_ncbi_files = OrderedDict()
def dnld_cds_for_ncbi_prot_acc_parallel(ss):
if stat(aa_queries_files[ss]).st_size == 0:
return
if ss not in prot_acc_user_filtered:
return
prot_cds_ncbi_files[ss] = opj(
dir_prj_transcripts_combined,
prj_name + '_ncbi_query_cds__' + ss + '.fasta')
if len(prot_acc_user_filtered[ss]) > 0:
dnld_cds_for_ncbi_prot_acc(ss, prot_acc_user_filtered[ss],
prot_cds_ncbi_files[ss], tax,
dir_cache_prj)
ss_names = tuple(sss.keys())
Parallel(n_jobs=2, verbose=0, require='sharedmem')(
delayed(dnld_cds_for_ncbi_prot_acc_parallel)(ss) for ss in ss_names)
# ------------------------------------------------------------------------
rmtree(dir_temp)
# ------------------------------------------------------------------------
rerun = input('\nRepeat ([y]/n)? ').lower().strip()
if rerun.startswith('y') or rerun == '':
print()
return False
else:
print('\nExiting...')
return True
FORCE_DEPS = ARGS.FORCE_DEPS
INSTALL_DEPS = ARGS.INSTALL_DEPS
DNLD_KRAKEN_DBS = ARGS.DNLD_KRAKEN_DBS
PRINT_VERSION = ARGS.PRINT_VERSION
PRINT_HELP = ARGS.PRINT_HELP
if PRINT_HELP is True:
print(SCRIPT_INFO)
PARSER.print_help()
exit(0)
if PRINT_VERSION is True:
print(__script_name__ + ' v' + __version__)
exit(0)
if CLEAN_CONFIG_DIR is True and ope(DIR_CFG):
print(CONSRED + 'Removing configuration directory: ' + CONSDFL + DIR_CFG)
rmtree(DIR_CFG)
exit(0)
elif CLEAN_CONFIG_DIR is True:
print(CONSRED + 'Configuration directory does not exist. Nothing to do.' +
CONSDFL)
exit(0)
if CLEAN_CONFIG_DIR is False and CONFIG_FILE_PATH is not None:
if not ope(CONFIG_FILE_PATH):
print(CONSRED + 'Configuration file ' + CONFIG_FILE_PATH +
' does not exist.' + CONSDFL)
exit(0)
elif INSTALL_DEPS is True or DNLD_KRAKEN_DBS is True:
pass
def list_wavs_in_dir(dirname):
return glob.glob(opj(ope(dirname), '*.wav'))
def make_dirs(path):
path = abspath(expanduser(path))
if not ope(path):
makedirs(path)
return path
def run_inter_pro_scan(ss, assemblies, email, dir_prj_ips, dir_cache_prj,
parallel_run_count, max_title_a_len, max_run_id_len):
delay = 0.25
for a in assemblies:
if 'transcripts_aa_orf_fasta_file__' + ss not in a:
continue
aa_file = a['transcripts_aa_orf_fasta_file__' + ss]
if aa_file is None:
continue
assmbl_name = a['name']
json_dump_file_path = opj(dir_prj_ips,
assmbl_name + '_ann_ips__' + ss + '.json')
if ope(json_dump_file_path):
Log.inf('InterProScan results for assembly ' + assmbl_name + ', '
'search strategy ' + ss + ' have already been downloaded.')
continue
else:
Log.inf('Running InterProScan on translated ' + ss + ' from ' +
assmbl_name + '.')
seqs = seq_records_to_dict(read_fasta(aa_file, SEQ_TYPE_AA))
# Filter all ORFs except the first one.
for seq_def in tuple(seqs.keys()):
seq_def_prefix = seq_def.split(' ')[0]
if not seq_def_prefix.endswith('ORF001'):
del seqs[seq_def]
seqs = OrderedDict(
sorted(seqs.items(),
key=lambda x: x[0].split(' ')[1],
reverse=True))
run_id = ss + '_' + assmbl_name
_ = opj(dir_cache_prj, 'ips5_cache_done_' + run_id)
if ope(_):
with open(_, 'rb') as f:
jobs = pickle.load(f)
else:
jobs = job_runner(email=email,
dir_cache=dir_cache_prj,
seqs=seqs,
run_id=run_id,
parallel_run_count=parallel_run_count,
max_title_a_len=max_title_a_len,
max_run_id_len=max_run_id_len)
with open(_, 'wb') as f:
pickle.dump(jobs, f, protocol=PICKLE_PROTOCOL)
Log.inf('Downloading InterProScan results for ' + ss + ' in ' +
assmbl_name + '.')
all_ips_results = {}
# Nicer printing
for i, job in enumerate(jobs['finished']):
job_id = jobs['finished'][job]
titles_ab = split_seq_defn(job)
title_a = titles_ab[0]
progress = round(((i + 1) / len(jobs['finished'])) * 100)
progress_str = '{:3d}'.format(progress) + '%'
msg = (' ' * 12 + title_a.ljust(max_title_a_len) +
run_id.ljust(max_run_id_len) + progress_str.rjust(4) + ' ' +
job_id)
Log.msg(msg)
sleep(delay)
ips_json = result_json(job_id)
if ips_json is None:
continue
# ips_version = ips_json['interproscan-version']
ips_json = ips_json['results']
# These fields are set to 'EMBOSS_001' by default
# Delete them
del ips_json[0]['xref']
job_no_def = job.split(' ')[0]
all_ips_results[job_no_def] = ips_json
with open(json_dump_file_path, 'w') as f:
json.dump(all_ips_results, f, sort_keys=True, indent=4)
# Removes cached jobs file.
osremove(_)
def run_spades(se_fastq_files, pe_fastq_files, dir_spades_assemblies,
spades, dir_temp, ss, threads, ram):
if len(se_fastq_files) > 0 or len(pe_fastq_files) > 0:
if spades is None:
Log.err('SPAdes is not available. Cannot continue. Exiting.')
exit(0)
for se in se_fastq_files:
dir_results = opj(dir_spades_assemblies, se + '__' + ss)
fq_path = se_fastq_files[se]['vsearch_results_path' + '__' + ss]
se_fastq_files[se]['spades_assembly' + '__' + ss] = None
if ope(dir_results):
Log.msg('SPAdes assembly already exists:', se)
else:
make_dirs(dir_results)
Log.msg('Running SPAdes on:', se)
run_spades_se(spades,
out_dir=dir_results,
input_file=fq_path,
threads=threads,
memory=ram,
rna=True)
assmbl_path = opj(dir_results, 'transcripts.fasta')
if ope(assmbl_path):
count = len(read_fasta(assmbl_path, SEQ_TYPE_NT))
tr_str = ' transcripts.'
if count == 1:
tr_str = ' transcript.'
Log.msg('SPAdes produced ' + str(count) + tr_str, False)
se_fastq_files[se]['spades_assembly' + '__' + ss] = assmbl_path
else:
Log.wrn('SPAdes produced no transcripts.', False)
for pe in pe_fastq_files:
dir_results = opj(dir_spades_assemblies, pe + '__' + ss)
fq_paths = pe_fastq_files[pe]['vsearch_results_path' + '__' + ss]
pe_fastq_files[pe]['spades_assembly' + '__' + ss] = None
if ope(dir_results):
Log.msg('SPAdes assembly already exists:', pe)
else:
make_dirs(dir_results)
Log.msg('Running SPAdes on: ' + pe)
if osstat(fq_paths[0]).st_size > 0 and \
osstat(fq_paths[1]).st_size > 0:
run_spades_pe(spades,
out_dir=dir_results,
input_files=fq_paths,
threads=threads,
memory=ram,
rna=True)
else:
_ = opj(dir_temp, 'temp.fasta')
combine_text_files(fq_paths, _)
run_spades_se(spades,
out_dir=dir_results,
input_file=_,
threads=threads,
memory=ram,
rna=True)
osremove(_)
assmbl_path = opj(dir_results, 'transcripts.fasta')
if ope(assmbl_path):
count = len(read_fasta(assmbl_path, SEQ_TYPE_NT))
tr_str = ' transcripts.'
if count == 1:
tr_str = ' transcript.'
Log.msg('SPAdes produced ' + str(count) + tr_str, False)
pe_fastq_files[pe]['spades_assembly' + '__' + ss] = assmbl_path
else:
Log.wrn('SPAdes produced no transcripts.', False)
def run_tblastn_on_assemblies(ss, assemblies, aa_queries_file, tblastn,
dir_prj_assmbl_blast_results, blast_2_evalue,
blast_2_max_hsps, blast_2_qcov_hsp_perc,
blast_2_best_hit_overhang,
blast_2_best_hit_score_edge,
blast_2_max_target_seqs, threads, dir_cache_prj,
dir_prj_ips):
if len(assemblies) > 0:
print()
Log.inf('Running BLAST on assemblies:', ss)
if tblastn is None:
Log.err('tblastn is not available. Cannot continue. Exiting.')
exit(0)
else:
Log.wrn('There are no assemblies. Nothing to do, stopping.')
exit(0)
cache_file = opj(dir_cache_prj, 'blast_2_settings_cache__' + ss)
pickled = dict()
settings = {'blast_2_evalue': blast_2_evalue,
'blast_2_max_hsps': blast_2_max_hsps,
'blast_2_qcov_hsp_perc': blast_2_qcov_hsp_perc,
'blast_2_best_hit_overhang': blast_2_best_hit_overhang,
'blast_2_best_hit_score_edge': blast_2_best_hit_score_edge,
'blast_2_max_target_seqs': blast_2_max_target_seqs,
'queries': seq_records_to_dict(
read_fasta(aa_queries_file, SEQ_TYPE_AA))}
Log.msg('evalue:', str(blast_2_evalue))
Log.msg('max_hsps:', str(blast_2_max_hsps))
Log.msg('qcov_hsp_perc:', str(blast_2_qcov_hsp_perc))
Log.msg('best_hit_overhang:', str(blast_2_best_hit_overhang))
Log.msg('best_hit_score_edge:', str(blast_2_best_hit_score_edge))
Log.msg('max_target_seqs:', str(blast_2_max_target_seqs))
print()
for a in assemblies:
assmbl_src = a['src']
assmbl_name = a['name']
if assmbl_src != 'user_fasta':
if assmbl_name.endswith('__' + ss):
assmbl_name = assmbl_name.replace('__' + ss, '')
else:
continue
assmbl_blast_db_path = a['blast_db_path']
assmbl_genetic_code = a['gc_id']
ips_json_dump_path = opj(dir_prj_ips, assmbl_name + '_ann_ips__' + ss +
'.json')
_ = opj(dir_prj_assmbl_blast_results, assmbl_name + '__' + ss + '.tsv')
if ope(_) and ope(cache_file):
with open(cache_file, 'rb') as f:
pickled = pickle.load(f)
if ope(_) and pickled == settings:
# Log.msg('The provided BLAST settings and query sequences did '
# 'not change since the previous run.')
Log.msg('BLAST results already exist:', assmbl_name)
else:
Log.msg('Running tblastn on: ' + assmbl_name, ss)
if ope(ips_json_dump_path):
osremove(ips_json_dump_path)
run_blast(exec_file=tblastn,
task='tblastn',
threads=threads,
db_path=assmbl_blast_db_path,
queries_file=aa_queries_file,
out_file=_,
evalue=blast_2_evalue,
max_hsps=blast_2_max_hsps,
qcov_hsp_perc=blast_2_qcov_hsp_perc,
best_hit_overhang=blast_2_best_hit_overhang,
best_hit_score_edge=blast_2_best_hit_score_edge,
max_target_seqs=blast_2_max_target_seqs,
db_genetic_code=assmbl_genetic_code,
out_cols=BLST_RES_COLS_2)
a['blast_hits_aa__' + ss] = parse_blast_results_file(_, BLST_RES_COLS_2)
with open(cache_file, 'wb') as f:
pickle.dump(settings, f, protocol=PICKLE_PROTOCOL)
def run_bt2_fq(se_fastq_files, pe_fastq_files, dir_fq_filter_data, bowtie2,
bowtie2_build, threads, dir_temp, bt2_order, fpatt, taxonomy,
dir_cache_refseqs):
new_se_fastq_files = dict()
new_pe_fastq_files = dict()
msg_printed = False
# SE
for se in se_fastq_files:
taxid = se_fastq_files[se]['tax_id']
dbs = _should_run_bt2(taxid, taxonomy, bt2_order, bowtie2,
bowtie2_build)
in_f = se_fastq_files[se]['trim_path_fq']
in_f_orig = in_f
if len(dbs) == 0:
se_fastq_files[se]['filter_path_fq'] = in_f
continue
if msg_printed is False:
print()
Log.inf('Running Bowtie2.')
msg_printed = True
for i, db in enumerate(dbs):
db_path = dbs[db]
dir_fq_bt_data_sample = opj(dir_fq_filter_data, se, db)
dir_fq_bt_data_sample_un = opj(dir_fq_filter_data, se)
new_se = se + '_' + db
out_f = opj(dir_fq_bt_data_sample, new_se + '.fastq')
out_f_un = opj(dir_temp, new_se + '_bt2_unaligned' + '.fastq')
sam_f = opj(dir_fq_bt_data_sample, new_se + '.sam')
new_se_fastq_files[new_se] = deepcopy(se_fastq_files[se])
new_se_fastq_files[new_se]['path'] = None
new_se_fastq_files[new_se]['cor_path_fq'] = None
new_se_fastq_files[new_se]['trim_path_fq'] = None
taxid = new_se_fastq_files[new_se]['tax_id']
gc = new_se_fastq_files[new_se]['gc_id']
if db == MT:
gc = taxonomy.mito_genetic_code_for_taxid(taxid)
new_se_fastq_files[new_se]['gc_id'] = gc
elif db == PT:
gc = taxonomy.plastid_genetic_code_for_taxid(taxid)
new_se_fastq_files[new_se]['gc_id'] = gc
new_se_fastq_files[new_se]['gc_tt'] = TranslationTable(gc)
new_se_fastq_files[new_se]['filter_path_fq'] = out_f
if ope(dir_fq_bt_data_sample):
Log.msg('Bowtie2 filtered FASTQ file already exists:', new_se)
in_f = opj(dir_fq_bt_data_sample_un, se + '.fastq')
else:
Log.msg('SE mode:', new_se)
make_dirs(dir_fq_bt_data_sample)
db_fasta_path = None
bt2_idx_path = None
if db_path in (MT, PT):
db_fasta_path = dnld_refseqs_for_taxid(taxid,
db,
taxonomy,
dir_cache_refseqs,
query='',
db='nuccore')
bt2_idx_path = splitext(db_fasta_path)[0]
else:
db_fasta_path = db_path
bt2_idx_path = opj(dir_cache_refseqs,
splitext(basename(db_fasta_path))[0])
if not ope(bt2_idx_path + '.1.bt2'):
build_bt2_index(bowtie2_build, [db_fasta_path],
bt2_idx_path, threads)
run_bowtie2_se(bowtie2=bowtie2,
input_file=in_f,
output_file=out_f,
output_file_un=out_f_un,
sam_output_file=sam_f,
index=bt2_idx_path,
threads=threads,
dir_temp=dir_temp)
if i > 0:
remove(in_f)
in_f = out_f_un
out_f_un = opj(dir_fq_bt_data_sample_un, se + '.fastq')
se_fastq_files[se]['filter_path_fq'] = out_f_un
if in_f != in_f_orig:
move(in_f, out_f_un)
se_fastq_files.update(new_se_fastq_files)
# PE
for pe in pe_fastq_files:
taxid = pe_fastq_files[pe]['tax_id']
dbs = _should_run_bt2(taxid, taxonomy, bt2_order, bowtie2,
bowtie2_build)
in_fs = pe_fastq_files[pe]['trim_path_fq']
in_fs_orig = tuple(in_fs)
if len(dbs) == 0:
pe_fastq_files[pe]['filter_path_fq'] = in_fs
continue
if msg_printed is False:
print()
Log.inf('Running Bowtie2.')
msg_printed = True
for i, db in enumerate(dbs):
db_path = dbs[db]
dir_fq_bt_data_sample = opj(dir_fq_filter_data, pe, db)
dir_fq_bt_data_sample_un = opj(dir_fq_filter_data, pe)
new_pe = pe + '_' + db
out_fs = [x.replace('@D@', dir_fq_bt_data_sample) for x in fpatt]
out_fs = [x.replace('@N@', new_pe) for x in out_fs]
out_fs_un = [x.replace('@D@', dir_temp) for x in fpatt]
out_fs_un = [
x.replace('@N@', new_pe + '_bt2_unaligned') for x in out_fs_un
]
sam_f = opj(dir_fq_bt_data_sample, new_pe + '.sam')
new_pe_fastq_files[new_pe] = deepcopy(pe_fastq_files[pe])
new_pe_fastq_files[new_pe]['path'] = None
new_pe_fastq_files[new_pe]['cor_path_fq'] = None
new_pe_fastq_files[new_pe]['trim_path_fq'] = None
taxid = new_pe_fastq_files[new_pe]['tax_id']
gc = new_pe_fastq_files[new_pe]['gc_id']
if db == MT:
gc = taxonomy.mito_genetic_code_for_taxid(taxid)
new_pe_fastq_files[new_pe]['gc_id'] = gc
elif db == PT:
gc = taxonomy.plastid_genetic_code_for_taxid(taxid)
new_pe_fastq_files[new_pe]['gc_id'] = gc
new_pe_fastq_files[new_pe]['gc_tt'] = TranslationTable(gc)
new_pe_fastq_files[new_pe]['filter_path_fq'] = out_fs
if ope(dir_fq_bt_data_sample):
Log.msg('Bowtie2 filtered FASTQ files already exist:', new_pe)
in_fs = [
x.replace('@D@', dir_fq_bt_data_sample_un) for x in fpatt
]
in_fs = [x.replace('@N@', pe) for x in in_fs]
else:
Log.msg('PE mode:', new_pe)
make_dirs(dir_fq_bt_data_sample)
db_fasta_path = None
bt2_idx_path = None
if db_path in (MT, PT):
db_fasta_path = dnld_refseqs_for_taxid(taxid,
db,
taxonomy,
dir_cache_refseqs,
query='',
db='nuccore')
bt2_idx_path = splitext(db_fasta_path)[0]
else:
db_fasta_path = db_path
bt2_idx_path = opj(dir_cache_refseqs,
splitext(basename(db_fasta_path))[0])
if not ope(bt2_idx_path + '.1.bt2'):
build_bt2_index(bowtie2_build, [db_fasta_path],
bt2_idx_path, threads)
paired_out_pattern = out_fs[0].replace('_paired_1.fastq',
'_paired_%.fastq')
paired_out_pattern_un = out_fs_un[0].replace(
'_paired_1.fastq', '_paired_%.fastq')
run_bowtie2_pe(bowtie2=bowtie2,
input_files=in_fs,
paired_out_pattern=paired_out_pattern,
paired_out_pattern_un=paired_out_pattern_un,
unpaired_out_1=out_fs[2],
unpaired_out_2=out_fs[3],
unpaired_out_1_un=out_fs_un[2],
unpaired_out_2_un=out_fs_un[3],
sam_output_file=sam_f,
index=bt2_idx_path,
threads=threads,
dir_temp=dir_temp)
if i > 0:
remove(in_fs[0])
remove(in_fs[1])
remove(in_fs[2])
remove(in_fs[3])
in_fs = out_fs_un
out_fs_un = [x.replace('@D@', dir_fq_bt_data_sample_un) for x in fpatt]
out_fs_un = [x.replace('@N@', pe) for x in out_fs_un]
pe_fastq_files[pe]['filter_path_fq'] = out_fs_un
if tuple(in_fs) != in_fs_orig:
move(in_fs[0], out_fs_un[0])
move(in_fs[1], out_fs_un[1])
move(in_fs[2], out_fs_un[2])
move(in_fs[3], out_fs_un[3])
pe_fastq_files.update(new_pe_fastq_files)
