def main():
args = docopt(__doc__)
#print(args)
bam_f = args['--bam']
include_f = args['--include']
exclude_f = args['--exclude']
out_prefix = args['--out']
read_format = args['--read_format']
if not read_format in set(['fq', 'fa']):
sys.exit("[X] Read format must be fq or fa!")
noninterleaved = args['--noninterleaved']
include_unmapped = True
if args['--exclude_unmapped']:
include_unmapped = False
out_f = BtIO.getOutFile(bam_f, out_prefix, None)
if include_f and exclude_f:
print(BtLog.error('43'))
elif include_f:
sequence_list = BtIO.parseList(include_f)
BtIO.parseBamForFilter(bam_f, include_unmapped, noninterleaved, out_f, sequence_list, None, read_format)
elif exclude_f:
sequence_list = BtIO.parseList(exclude_f)
BtIO.parseBamForFilter(bam_f, include_unmapped, noninterleaved, out_f, None, sequence_list, read_format)
else:
BtIO.parseBamForFilter(bam_f, include_unmapped, noninterleaved, out_f, None, None, read_format)
def mapping():
out_f, hit_f, map_f, taxid_d = None, None, None, {}
hit_f = megablast_output #hit file: BLAST similarity search result (TSV format)
map_f = "/home/nancy/assembly_app/blobtools/blobtools-master/taxon_n" #mapping file (TSV format), in which one column lists a sequence ID (of a subject) and another the NCBI TaxID
map_col_sseqid = "0" #column of mapping file containing sequence IDs (of the subject)
map_col_taxid = "2" #column of mapping file containing the TaxID of the subject
hit_col_qseqid = "0" #column of the hit file containing query ID
hit_col_sseqid = "1" #column of the hit file containing subject ID
hit_col_score = "11" #column of the hit file containing (bit)score
try:
hit_col_qseqid = int(hit_col_qseqid)
hit_col_sseqid = int(hit_col_sseqid)
hit_col_score = int(hit_col_score)
except ValueError:
BtLog.error('41' % (
"--hit_column_qseqid, --hit_column_sseqid and --hit_column_score"
))
if map_f:
if map_col_sseqid and map_col_taxid:
try:
map_col_sseqid = int(map_col_sseqid)
map_col_taxid = int(map_col_taxid)
except ValueError:
BtLog.error('44')
print BtLog.status_d['1'] % ("Mapping file", map_f)
taxid_d = BtIO.parseDict(map_f, map_col_sseqid, map_col_taxid)
out_f = BtIO.getOutFile("taxified", hit_f, "out")
else:
BtLog.error('44')
else:
BtLog.error('41')
output = []
print BtLog.status_d['1'] % ("similarity search result", hit_f)
with open(hit_f) as fh:
for idx, line in enumerate(fh):
col = line.rstrip("\n").split()
qseqid = col[hit_col_qseqid]
sseqid = col[hit_col_sseqid]
score = col[hit_col_score]
tax_id = None
if sseqid not in taxid_d:
BtLog.warn_d['12'] % (sseqid, map_f)
tax_id = taxid_d.get(sseqid, "N/A")
output.append("%s\t%s\t%s\t%s" %
(qseqid, tax_id, score, sseqid))
if output:
with open(out_f, "w") as fh:
print BtLog.status_d['24'] % out_f
fh.write("\n".join(output) + "\n")
def main():
args = docopt(__doc__)
bam_f = args['--bam']
include_f = args['--include']
exclude_f = args['--exclude']
out_prefix = args['--out']
include_unmapped = args['--include_unmapped']
gzip = None
do_sort = args['--sort']
keep_sorted = args['--keep']
sort_threads = int(args['--threads'])
out_f = BtIO.getOutFile(bam_f, out_prefix, None)
if include_f and exclude_f:
print BtLog.error('43')
elif include_f:
sequence_list = BtIO.parseList(include_f)
BtIO.parseBamForFilter(bam_f, include_unmapped, out_f, sequence_list, None, gzip, do_sort, keep_sorted, sort_threads)
elif exclude_f:
sequence_list = BtIO.parseList(exclude_f)
BtIO.parseBamForFilter(bam_f, include_unmapped, out_f, None, sequence_list, gzip, do_sort, keep_sorted, sort_threads)
else:
BtIO.parseBamForFilter(bam_f, include_unmapped, out_f, None, None, gzip, do_sort, keep_sorted, sort_threads)
def main():
args = docopt(__doc__)
fasta_f = args['--infile']
list_f = args['--list']
invert = args['--invert']
prefix = args['--out']
output = []
out_f = BtIO.getOutFile(fasta_f, prefix, "filtered.fna")
print BtLog.status_d['1'] % ("list", list_f)
items = BtIO.parseSet(list_f)
items_count = len(items)
print BtLog.status_d['22'] % fasta_f
items_parsed = []
sequences = 0
for header, sequence in BtIO.readFasta(fasta_f):
sequences += 1
if header in items:
if not (invert):
items_parsed.append(header)
output.append(">%s\n%s\n" % (header, sequence))
else:
if (invert):
items_parsed.append(header)
output.append(">%s\n%s\n" % (header, sequence))
BtLog.progress(len(output), 10, items_count, no_limit=True)
BtLog.progress(items_count, 10, items_count)
items_parsed_count = len(items_parsed)
print BtLog.status_d['23'] % ('{:.2%}'.format(items_parsed_count/sequences), "{:,}".format(items_count), "{:,}".format(items_parsed_count), "{:,}".format(sequences))
items_parsed_count_unique = len(set(items_parsed))
if not items_parsed_count == items_parsed_count_unique:
print BtLog.warn_d['8'] % "\n\t\t\t".join(list(set([x for x in items_parsed if items_parsed.count(x) > 1])))
with open(out_f, "w") as fh:
print BtLog.status_d['24'] % out_f
fh.write("".join(output))
def main():
args = docopt(__doc__)
fasta_f = args['--infile']
list_f = args['--list']
invert = args['--invert']
prefix = args['--out']
output = []
out_f = BtIO.getOutFile(fasta_f, prefix, "filtered.fna")
print(BtLog.status_d['1'] % ("list", list_f))
items = BtIO.parseSet(list_f)
items_count = len(items)
print(BtLog.status_d['22'] % fasta_f)
items_parsed = []
with tqdm(total=items_count, desc="[%] ", ncols=200, unit_scale=True) as pbar:
for header, sequence in BtIO.readFasta(fasta_f):
if header in items:
if not (invert):
items_parsed.append(header)
output.append(">%s\n%s\n" % (header, sequence))
else:
if (invert):
items_parsed.append(header)
output.append(">%s\n%s\n" % (header, sequence))
pbar.update()
items_parsed_count = len(items_parsed)
items_parsed_count_unique = len(set(items_parsed))
if not items_parsed_count == items_parsed_count_unique:
print(BtLog.warn_d['8'] % "\n\t\t\t".join(list(set([x for x in items_parsed if items_parsed.count(x) > 1]))))
with open(out_f, "w") as fh:
print(BtLog.status_d['24'] % out_f)
fh.write("".join(output))
def main():
#print(data_dir)
args = docopt(__doc__)
blobdb_f = args['--input']
prefix = args['--out']
ranks = args['--rank']
taxrule = args['--taxrule']
hits_flag = args['--hits']
seq_list_f = args['--list']
concoct = args['--concoct']
cov = args['--cov']
notable = args['--notable']
experimental = args['--experimental']
# Does blobdb_f exist ?
if not isfile(blobdb_f):
BtLog.error('0', blobdb_f)
out_f = BtIO.getOutFile(blobdb_f, prefix, None)
# Are ranks sane ?
if 'all' in ranks:
temp_ranks = RANKS[0:-1]
ranks = temp_ranks[::-1]
else:
for rank in ranks:
if rank not in RANKS:
BtLog.error('9', rank)
# Does seq_list file exist?
seqs = []
if (seq_list_f):
if isfile(seq_list_f):
seqs = BtIO.parseList(seq_list_f)
else:
BtLog.error('0', seq_list_f)
# Load BlobDb
blobDb = BtCore.BlobDb('new')
print(BtLog.status_d['9'] % (blobdb_f))
blobDb.load(blobdb_f)
blobDb.version = interface.__version__
# Is taxrule sane and was it computed?
if (blobDb.hitLibs) and taxrule not in blobDb.taxrules:
BtLog.error('11', taxrule, blobDb.taxrules)
# view(s)
viewObjs = []
print(BtLog.status_d['14'])
if not (notable):
tableView = None
if len(blobDb.hitLibs) > 1:
tableView = BtCore.ViewObj(name="table",
out_f=out_f,
suffix="%s.table.txt" % (taxrule),
body=[])
else:
tableView = BtCore.ViewObj(name="table",
out_f=out_f,
suffix="table.txt",
body=[])
viewObjs.append(tableView)
if not experimental == 'False':
meta = {}
if isfile(experimental):
meta = BtIO.readYaml(experimental)
experimentalView = BtCore.ExperimentalViewObj(name="experimental",
view_dir=out_f,
blobDb=blobDb,
meta=meta)
viewObjs.append(experimentalView)
if (concoct):
concoctTaxView = None
concoctCovView = None
if len(blobDb.hitLibs) > 1:
concoctTaxView = BtCore.ViewObj(
name="concoct_tax",
out_f=out_f,
suffix="%s.concoct_taxonomy_info.csv" % (taxrule),
body=dict())
concoctCovView = BtCore.ViewObj(
name="concoct_cov",
out_f=out_f,
suffix="%s.concoct_coverage_info.tsv" % (taxrule),
body=[])
else:
concoctTaxView = BtCore.ViewObj(name="concoct_tax",
out_f=out_f,
suffix="concoct_taxonomy_info.csv",
body=dict())
concoctCovView = BtCore.ViewObj(name="concoct_cov",
out_f=out_f,
suffix="concoct_coverage_info.tsv",
body=[])
viewObjs.append(concoctTaxView)
viewObjs.append(concoctCovView)
if (cov):
for cov_lib_name, covLibDict in blobDb.covLibs.items():
out_f = BtIO.getOutFile(covLibDict['f'], prefix, None)
covView = BtCore.ViewObj(name="covlib",
out_f=out_f,
suffix="cov",
body=[])
blobDb.view(viewObjs=[covView],
ranks=None,
taxrule=None,
hits_flag=None,
seqs=None,
cov_libs=[cov_lib_name],
progressbar=True)
if (viewObjs):
#for viewObj in viewObjs:
# print(viewObj.name)
blobDb.view(viewObjs=viewObjs,
ranks=ranks,
taxrule=taxrule,
hits_flag=hits_flag,
seqs=seqs,
cov_libs=[],
progressbar=True)
print(BtLog.status_d['19'])
def main():
args = docopt(__doc__)
args = BtPlot.check_input(args)
blobdb_f = args['--infile']
rank = args['--rank']
min_length = int(args['--length'])
max_group_plot = int(args['--plotgroups'])
hide_nohits = args['--nohit']
taxrule = args['--taxrule']
c_index = args['--cindex']
exclude_groups = args['--exclude']
labels = args['--label']
colour_f = args['--colours']
refcov_f = args['--refcov']
catcolour_f = args['--catcolour']
multiplot = args['--multiplot']
out_prefix = args['--out']
sort_order = args['--sort']
sort_first = args['--sort_first']
hist_type = args['--hist']
no_title = args['--notitle']
ignore_contig_length = args['--noscale']
format_plot = args['--format']
no_plot_blobs = args['--noblobs']
no_plot_reads = args['--noreads']
legend_flag = args['--legend']
cumulative_flag = args['--cumulative']
cov_lib_selection = args['--lib']
filelabel = args['--filelabel']
exclude_groups = BtIO.parseCmdlist(exclude_groups)
refcov_dict = BtIO.parseReferenceCov(refcov_f)
user_labels = BtIO.parseCmdLabels(labels)
catcolour_dict = BtIO.parseCatColour(catcolour_f)
colour_dict = BtIO.parseColours(colour_f)
# Load BlobDb
print BtLog.status_d['9'] % blobdb_f
blobDb = BtCore.BlobDb('blobplot')
blobDb.version = blobtools.__version__
blobDb.load(blobdb_f)
# Generate plot data
print BtLog.status_d['18']
data_dict, min_cov, max_cov, cov_lib_dict = blobDb.getPlotData(
rank, min_length, hide_nohits, taxrule, c_index, catcolour_dict)
plotObj = BtPlot.PlotObj(data_dict, cov_lib_dict, cov_lib_selection,
'blobplot', sort_first)
plotObj.exclude_groups = exclude_groups
plotObj.version = blobDb.version
plotObj.format = format_plot
plotObj.max_cov = max_cov
plotObj.min_cov = min_cov
plotObj.no_title = no_title
plotObj.multiplot = multiplot
plotObj.hist_type = hist_type
plotObj.ignore_contig_length = ignore_contig_length
plotObj.max_group_plot = max_group_plot
plotObj.legend_flag = legend_flag
plotObj.cumulative_flag = cumulative_flag
# order by which to plot (should know about user label)
plotObj.group_order = BtPlot.getSortedGroups(data_dict, sort_order,
sort_first)
# labels for each level of stats
plotObj.labels.update(plotObj.group_order)
# plotObj.group_labels is dict that contains labels for each group : all/other/user_label
if (user_labels):
for group, label in user_labels.items():
plotObj.labels.add(label)
plotObj.group_labels = {group: set() for group in plotObj.group_order}
plotObj.relabel_and_colour(colour_dict, user_labels)
plotObj.compute_stats()
plotObj.refcov_dict = refcov_dict
# Plotting
info_flag = 1
out_f = ''
for cov_lib in plotObj.cov_libs:
plotObj.ylabel = "Coverage"
plotObj.xlabel = "GC proportion"
if (filelabel):
plotObj.ylabel = basename(cov_lib_dict[cov_lib]['f'])
out_f = "%s.%s.%s.p%s.%s.%s" % (blobDb.title, taxrule, rank,
max_group_plot, hist_type, min_length)
if catcolour_dict:
out_f = "%s.%s" % (out_f, "catcolour")
if ignore_contig_length:
out_f = "%s.%s" % (out_f, "noscale")
if c_index:
out_f = "%s.%s" % (out_f, "c_index")
if exclude_groups:
out_f = "%s.%s" % (out_f, "exclude_" + "_".join(exclude_groups))
if labels:
out_f = "%s.%s" % (out_f, "userlabel_" + "_".join(
set([name for name in user_labels.values()])))
out_f = "%s.%s" % (out_f, "blobplot")
if (plotObj.cumulative_flag):
out_f = "%s.%s" % (out_f, "cumulative")
if (plotObj.multiplot):
out_f = "%s.%s" % (out_f, "multiplot")
out_f = BtIO.getOutFile(out_f, out_prefix, None)
if not (no_plot_blobs):
plotObj.plotScatter(cov_lib, info_flag, out_f)
info_flag = 0
if not (no_plot_reads) and (
plotObj.cov_libs_total_reads_dict[cov_lib]):
# prevent plotting if --noreads or total_reads == 0
plotObj.plotBar(cov_lib, out_f)
plotObj.write_stats(out_f)
def parseCoverage(self, **kwargs):
# arguments
covLibObjs = kwargs['covLibObjs']
no_base_cov = kwargs['no_base_cov']
for covLib in covLibObjs:
self.addCovLib(covLib)
print BtLog.status_d['1'] % (covLib.name, covLib.f)
if covLib.fmt == 'bam' or covLib.fmt == 'sam':
base_cov_dict = {}
if covLib.fmt == 'bam':
base_cov_dict, covLib.reads_total, covLib.reads_mapped, read_cov_dict = BtIO.parseBam(
covLib.f, set(self.dict_of_blobs), no_base_cov)
else:
base_cov_dict, covLib.reads_total, covLib.reads_mapped, read_cov_dict = BtIO.parseSam(
covLib.f, set(self.dict_of_blobs), no_base_cov)
if covLib.reads_total == 0:
print BtLog.warn_d['4'] % covLib.f
for name, base_cov in base_cov_dict.items():
cov = base_cov / self.dict_of_blobs[name].agct_count
covLib.cov_sum += cov
self.dict_of_blobs[name].addCov(covLib.name, cov)
self.dict_of_blobs[name].addReadCov(
covLib.name, read_cov_dict[name])
# Create COV file for future use
out_f = BtIO.getOutFile(covLib.f, kwargs.get('prefix', None),
None)
covView = ViewObj(name="covlib",
out_f=out_f,
suffix="cov",
header="",
body=[])
self.view(viewObjs=[covView],
ranks=None,
taxrule=None,
hits_flag=None,
seqs=None,
cov_libs=[covLib.name],
progressbar=False)
elif covLib.fmt == 'cas':
cov_dict, covLib.reads_total, covLib.reads_mapped, read_cov_dict = BtIO.parseCas(
covLib.f, self.order_of_blobs)
if covLib.reads_total == 0:
print BtLog.warn_d['4'] % covLib.f
for name, cov in cov_dict.items():
covLib.cov_sum += cov
self.dict_of_blobs[name].addCov(covLib.name, cov)
self.dict_of_blobs[name].addReadCov(
covLib.name, read_cov_dict[name])
out_f = BtIO.getOutFile(covLib.f, kwargs.get('prefix', None),
None)
covView = ViewObj(name="covlib",
out_f=out_f,
suffix="cov",
header="",
body=[])
self.view(viewObjs=[covView],
ranks=None,
taxrule=None,
hits_flag=None,
seqs=None,
cov_libs=[covLib.name],
progressbar=False)
elif covLib.fmt == 'cov':
base_cov_dict, covLib.reads_total, covLib.reads_mapped, covLib.reads_unmapped, read_cov_dict = BtIO.parseCov(
covLib.f, set(self.dict_of_blobs))
#cov_dict = BtIO.readCov(covLib.f, set(self.dict_of_blobs))
if not len(base_cov_dict) == self.seqs:
print BtLog.warn_d['4'] % covLib.f
for name, cov in base_cov_dict.items():
covLib.cov_sum += cov
self.dict_of_blobs[name].addCov(covLib.name, cov)
if name in read_cov_dict:
self.dict_of_blobs[name].addReadCov(
covLib.name, read_cov_dict[name])
else:
pass
covLib.mean_cov = covLib.cov_sum / self.seqs
if covLib.cov_sum == 0.0:
print BtLog.warn_d['6'] % (covLib.name)
self.covLibs[covLib.name] = covLib
def main():
args = docopt(__doc__)
out_f, hit_f, map_f, taxid_d = None, None, None, {}
hit_f = args['--hit_file']
hit_col_qseqid = args['--hit_column_qseqid']
hit_col_sseqid = args['--hit_column_sseqid']
hit_col_score = args['--hit_column_score']
map_f = args['--taxid_mapping_file']
map_col_sseqid = args['--map_col_sseqid']
map_col_taxid = args['--map_col_taxid']
custom_f = args['--custom']
custom_taxid = args['--custom_taxid']
custom_score = args['--custom_score']
prefix = args['--out']
try:
hit_col_qseqid = int(hit_col_qseqid)
hit_col_sseqid = int(hit_col_sseqid)
hit_col_score = int(hit_col_score)
except ValueError:
BtLog.error('41' % (
"--hit_column_qseqid, --hit_column_sseqid and --hit_column_score"))
if custom_taxid:
try:
custom_taxid = int(custom_taxid)
except TypeError:
BtLog.error('26')
out_f = BtIO.getOutFile(hit_f, prefix, "taxID_%s.out" % custom_taxid)
taxid_d = defaultdict(lambda: custom_taxid)
elif map_f:
if map_col_sseqid and map_col_taxid:
try:
map_col_sseqid = int(map_col_sseqid)
map_col_taxid = int(map_col_taxid)
except ValueError:
BtLog.error('44')
print BtLog.status_d['1'] % ("Mapping file", map_f)
taxid_d = BtIO.parseDict(map_f, map_col_sseqid, map_col_taxid)
out_f = BtIO.getOutFile(hit_f, prefix, "taxified.out")
else:
BtLog.error('44')
else:
BtLog.error('41')
output = []
print BtLog.status_d['1'] % ("similarity search result", hit_f)
with open(hit_f) as fh:
for idx, line in enumerate(fh):
col = line.rstrip("\n").split()
qseqid = col[hit_col_qseqid]
sseqid = col[hit_col_sseqid]
score = col[hit_col_score]
tax_id = None
if custom_taxid:
tax_id = taxid_d[sseqid]
else:
if sseqid not in taxid_d:
BtLog.warn_d['12'] % (sseqid, map_f)
tax_id = taxid_d.get(sseqid, "N/A")
output.append("%s\t%s\t%s\t%s" % (qseqid, tax_id, score, sseqid))
if output:
with open(out_f, "w") as fh:
print BtLog.status_d['24'] % out_f
fh.write("\n".join(output) + "\n")
def main():
#main_dir = dirname(__file__)
args = docopt(__doc__)
fasta_f = args['--infile']
fasta_type = args['--type']
bam_fs = args['--bam']
cov_fs = args['--cov']
cas_fs = args['--cas']
hit_fs = args['--hitsfile']
prefix = args['--out']
nodesDB_f = args['--db']
names_f = args['--names']
estimate_cov_flag = True if not args['--calculate_cov'] else False
nodes_f = args['--nodes']
taxrules = args['--taxrule']
try:
min_bitscore_diff = float(args['--min_diff'])
min_score = float(args['--min_score'])
except ValueError():
BtLog.error('45')
tax_collision_random = args['--tax_collision_random']
title = args['--title']
# outfile
out_f = BtIO.getOutFile("blobDB", prefix, "json")
if not (title):
title = out_f
# coverage
if not (fasta_type) and not bam_fs and not cov_fs and not cas_fs:
BtLog.error('1')
cov_libs = [BtCore.CovLibObj('bam' + str(idx), 'bam', lib_f) for idx, lib_f in enumerate(bam_fs)] + \
[BtCore.CovLibObj('cas' + str(idx), 'cas', lib_f) for idx, lib_f in enumerate(cas_fs)] + \
[BtCore.CovLibObj('cov' + str(idx), 'cov', lib_f) for idx, lib_f in enumerate(cov_fs)]
# taxonomy
hit_libs = [
BtCore.HitLibObj('tax' + str(idx), 'tax', lib_f)
for idx, lib_f in enumerate(hit_fs)
]
# Create BlobDB object
blobDb = BtCore.BlobDb(title)
blobDb.version = interface.__version__
# Parse FASTA
blobDb.parseFasta(fasta_f, fasta_type)
# Parse nodesDB OR names.dmp, nodes.dmp
nodesDB_default = join(dirname(abspath(__file__)), "../data/nodesDB.txt")
nodesDB, nodesDB_f = BtIO.parseNodesDB(nodes=nodes_f,
names=names_f,
nodesDB=nodesDB_f,
nodesDBdefault=nodesDB_default)
blobDb.nodesDB_f = nodesDB_f
# Parse similarity hits
if (hit_libs):
blobDb.parseHits(hit_libs)
if not taxrules:
if len(hit_libs) > 1:
taxrules = ['bestsum', 'bestsumorder']
else:
taxrules = ['bestsum']
blobDb.computeTaxonomy(taxrules, nodesDB, min_score, min_bitscore_diff,
tax_collision_random)
else:
print(BtLog.warn_d['0'])
# Parse coverage
blobDb.parseCoverage(covLibObjs=cov_libs,
estimate_cov=estimate_cov_flag,
prefix=prefix)
# Generating BlobDB and writing to file
print(BtLog.status_d['7'] % out_f)
BtIO.writeJson(blobDb.dump(), out_f)
