def find_closest(peak, starts_plus, stops_minus, cds_dict, ann_dict, d_threshold, inside, if_bed):
center = int(peak.name)
distances = [('+', x[0], x[1]-center) for x in starts_plus]
distances.extend([('-', x[0], center-x[1]+1) for x in stops_minus]);
distances = [x for x in distances if x[2]>-1*inside]
strand, gene_name, mindistance = min(distances, key = lambda x: abs(x[2]))
#print(gene_name)
if(abs(mindistance) <= d_threshold):
#gene_name = "_".join(gene_name.split("_")[:-1])
cds = cds_dict[gene_name]
if(strand == '+'):
atg = cds.start - center
if(strand == '-'):
atg = center - cds.end + 1
gene_name = gene_name.split("-")[1]
ann = ann_dict.get(gene_name);
if(ann):
genesymbol = ann[0]
annotation = ann[1]
function = ann[2]
else:
genesymbol = gene_name
annotation = 'Unknown'
function = "Unknown"
if(if_bed):
return construct_gff_interval(peak.chrom, peak.start, peak.stop, 'consensus_region', score='0', strand=strand, source='un', frame='.', attrs=[("Name", peak.name), ("annotation", annotation), ("function", function), ("gene", gene_name), ("genesymbol", genesymbol), ("tss", mindistance), ("atg", abs(atg))])
else:
return construct_gff_interval(peak.chrom, peak.start, peak.stop, 'consensus_region', score='0', strand=strand, source='un', frame='.', attrs=[("Name", peak.attrs['Name']), ("maxcov", peak.attrs['maxcov']), ("zscores", peak.attrs['zscores']), ("peakpos", peak.attrs['peakpos']), ("annotation", annotation), ("function", function), ("gene", gene_name), ("genesymbol", genesymbol), ("tss", mindistance), ("atg", abs(atg))])
else:
return None
def print_compiled(compiled, size):
temp_d = dict(compiled)
compiled_processed = [temp_d.get(x, None) for x in range(size)]
area_coverage = ",".join(
[x.attrs['area_coverage'] if x else '0' for x in compiled_processed])
topcoverage = ",".join(
[x.attrs['topcoverage'] if x else '0' for x in compiled_processed])
compiled_processed = [x for x in compiled_processed if x]
compiled = [x[1] for x in sorted(compiled, key=lambda x: x[0])]
pos = int(
sum([
int(x.name) * float(x.attrs['topcoverage'])
for x in compiled_processed
]) / sum([float(x.attrs['topcoverage']) for x in compiled_processed]))
start = min([x.start for x in compiled_processed])
stop = min([x.stop for x in compiled_processed])
consensus = construct_gff_interval(compiled[0].chrom,
start,
stop,
'consensus',
score='0',
strand='.',
source='.',
frame='.',
attrs=[('Name', pos),
('topcoverage', topcoverage),
('area_coverage', area_coverage)
])
sys.stdout.write(str(consensus))
def annotate_position(peak, tr_dict, maxd, inside):
if(peak.chrom not in tr_dict):
return None
center = int(peak.name)
tr_plus, tr_minus = tr_dict[peak.chrom]
if(peak.strand == '+'):
distances = [(tr, center-tr.stop+1) for tr in tr_minus];
else:
distances = [(tr, tr.start-center) for tr in tr_plus]
distances = [x for x in distances if x[1]>-1*inside]
if(not distances):
return None
transcript, mindistance = min(distances, key = lambda x: abs(x[1]))
if(abs(mindistance) <= maxd):
gtype = 'upstream'
atg = mindistance + float(transcript.attrs['distance'])
old_attrs = peak.attrs
new_attrs = [("annotation", transcript.attrs['annotation']), ("function", transcript.attrs['function']), ("gene", transcript.name), ("genesymbol", transcript.attrs['genesymbol']), ("cg", transcript.attrs.get('cg', 'unknown')), ("tss", mindistance), ("atg", atg), ("gtype", gtype), ("anti", "1")]
for k, v in new_attrs:
old_attrs[k] = v;
return construct_gff_interval( peak.chrom, peak.start, peak.stop, 'annotated', score=peak.score, strand=transcript.strand, source='annotate.py', frame='.', attrs=old_attrs.items())
else:
return None
def annotate_position(peak, tr_dict, maxd, inside):
center = int(peak.name)
if (peak.chrom not in tr_dict):
mindistance = float("NaN")
gtype = "intergenic"
transcript = STUB_TR
else:
center = int(peak.name)
tr_plus, tr_minus = tr_dict[peak.chrom]
distances = [(tr, tr.start - center) for tr in tr_plus]
distances.extend([(tr, center - tr.stop + 1) for tr in tr_minus])
distances = [x for x in distances if x[1] > -1 * inside]
transcript, mindistance = min(distances, key=lambda x: abs(x[1]))
if (abs(mindistance) <= maxd):
gtype = 'upstream'
else:
pairs = [(tr, center - tr.start, tr.stop - center - 1)
for tr in tr_plus + tr_minus]
pairs = [x for x in pairs if x[1] >= 0 and x[2] >= 0]
if (pairs):
gtype = 'gene'
transcript = pairs[0][0]
if (transcript.strand == '+'):
mindistance = -1 * pairs[0][1]
else:
mindistance = -1 * pairs[0][2]
else:
gtype = 'intergenic'
atg = mindistance + float(transcript.attrs['distance'])
if (str(atg) != "nan"):
atg = "%d" % atg
attrs = [("Name", peak.name),
("annotation", transcript.attrs['annotation']),
("function", transcript.attrs['function']),
("gene", transcript.name),
("genesymbol", transcript.attrs['genesymbol']),
("cg", transcript.attrs['cg']), ("tss", mindistance),
("atg", atg), ("gtype", gtype)]
if (if_bed):
return construct_gff_interval(peak.chrom,
peak.start,
peak.stop,
'annotated',
score=peak.score,
strand=transcript.strand,
source='annotate.py',
frame='.',
attrs=attrs)
else:
for attr_name, attr_value in attrs:
peak.attrs[attr_name] = str(attr_value)
return peak
def merge(intervals, name):
chrom = intervals[0].chrom
start = min([int(x.start) for x in intervals])
stop = max([int(x.stop) for x in intervals])
score = str(max([float(x.score) for x in intervals]))
strand = intervals[0].strand
gap = str(min([int(x.attrs['gap']) for x in intervals], key = abs))
unique_reads = sum([int(x.attrs.get('n_uniq', 1)) for x in intervals])
chscore = str(max([x.attrs['chscore'] for x in intervals]))
return construct_gff_interval(chrom, start, stop, 'interaction', score=score, strand=strand, source='chiflex', frame='.', attrs=[("ID", name), ('gap', gap), ('chscore', chscore), ('n_uniq', unique_reads)])
def find_closest(peak, starts_plus, stops_minus, pam_plus, pam_minus, d_threshold, inside):
center = int(peak.name)
distances = [('+', x[0], x[1]-center) for x in starts_plus]
distances.extend([('-', x[0], center-x[1]+1) for x in stops_minus]);
distances = [x for x in distances if x[2]>-1*inside]
strand, gene_name, mindistance = min(distances, key = lambda x: abs(x[2]))
if(abs(mindistance) <= d_threshold):
pam_sense, pam_antisense = get_pams_around_peak(center, strand, pam_plus, pam_minus)
pam_min = min([abs(x) for x in [pam_sense, pam_antisense]])
return construct_gff_interval( peak.chrom, peak.start, peak.stop, 'consensus_region', score=str(peak.score), strand=strand, source='un', frame='.', attrs=[("Name", peak.name),("tss", mindistance), ("gene", gene_name), ('pam_sense', pam_sense), ('pam_antisense', pam_antisense), ('pam_min', pam_min) ])
else:
return False
def area2interval(area, pflank, clear):
chrom = area[0][4]
start = area[0][0]
stop = area[-1][0]+1
score = str(max([x[3] for x in area]))
distance = int(abs((np.mean([x[2] for x in area]))))
peak = area[0][1]
if(distance<pflank):
_type = 'in'
elif(distance<clear):
_type = 'unk'
else:
_type = 'out'
return construct_gff_interval(chrom, start, stop, 'long_at', score=score, strand='.', source='un', frame='.', attrs=[("peak", peak), ("distance", distance), ("type", _type), ("ID", str((stop+start)//2))])
def annotate_position(peak, tr_dict, maxd, inside):
#sys.stderr.write(str(peak))
center = int(peak.name)
tr_plus, tr_minus = tr_dict.get(peak.chrom, [None, None])
if (not tr_plus):
mindistance = float("NaN")
gtype = "intergenic"
transcript = STUB_TR
anti = get_anti(STUB_TR, 'NaN')
else:
distances_plus = [(tr, tr.start - center) for tr in tr_plus]
distances_minus = [(tr, center - tr.stop + 1) for tr in tr_minus]
distances_plus = [x for x in distances_plus if x[1] > -1 * inside]
distances_minus = [x for x in distances_minus if x[1] > -1 * inside]
if (distances_plus):
transcript_plus, mindistance_plus = min(distances_plus,
key=lambda x: abs(x[1]))
else:
transcript_plus, mindistance_plus = STUB_TR, float("inf")
if (distances_minus):
transcript_minus, mindistance_minus = min(distances_minus,
key=lambda x: abs(x[1]))
else:
transcript_minus, mindistance_minus = STUB_TR, float("inf")
if (mindistance_plus < mindistance_minus):
transcript, mindistance = transcript_plus, mindistance_plus
anti = get_anti(transcript_minus, mindistance_minus)
else:
transcript, mindistance = transcript_minus, mindistance_minus
anti = get_anti(transcript_plus, mindistance_plus)
if (abs(mindistance) <= maxd):
gtype = 'upstream'
else:
pairs = [(tr, center - tr.start, tr.stop - center - 1)
for tr in tr_plus + tr_minus]
pairs = [x for x in pairs if x[1] >= 0 and x[2] >= 0]
if (pairs):
gtype = 'gene'
transcript = pairs[0][0]
if (transcript.strand == '+'):
mindistance = -1 * pairs[0][1]
else:
mindistance = -1 * pairs[0][2]
else:
gtype = 'intergenic'
atg = mindistance + float(transcript.attrs['distance'])
if (str(atg) != "nan"):
atg = "%d" % atg
attrs = [("Name", peak.name),
("annotation", transcript.attrs['annotation']),
("function", transcript.attrs['function']),
("gene", transcript.name),
("genesymbol", transcript.attrs['genesymbol']),
("cg", transcript.attrs.get('cg', 'unknown')),
("tss", mindistance), ("atg", atg), ("gtype", gtype)] + anti
if (if_bed):
return construct_gff_interval(peak.chrom,
peak.start,
peak.stop,
'annotated',
score=peak.score,
strand=transcript.strand,
source='annotate.py',
frame='.',
attrs=attrs)
else:
for attr_name, attr_value in attrs:
peak.attrs[attr_name] = str(attr_value)
peak.strand = transcript.strand
return peak
nargs='?',
required=False,
type=str,
help="Path to the output plot directory")
args = parser.parse_args()
if (os.stat(args.path).st_size == 0):
sys.exit("###annotate\nInput file is empty, empty output is produced\n")
STUB_TR = construct_gff_interval("unknown",
0,
2,
'fake',
score='0',
strand="+",
source='ff',
frame='.',
attrs=[("Name", "fake"),
("annotation", "None"),
("function", "None"),
("genesymbol", "fake"),
("distance", "NaN")])
### Annotate genomically ###
def get_anti(tr, mind):
return [('anti_gene', tr.name),
('anti_genesymbol', tr.attrs['genesymbol']),
('anti_tss', str(mind))]
def clarify(i1, i2, breakpoint, antisense):
gap = int(i1.attrs['gap'])
#clarify breakpoint
#clarify strandness
if (antisense):
if (i1.strand == '+'):
cs1 = construct_gff_interval(chrom=i2.chrom,
start=i2.start + breakpoint,
stop=i2.stop,
feature='ch',
score=i2.score,
strand='-',
source='.',
frame='.',
attrs=i2.attrs.items())
cs2 = construct_gff_interval(chrom=i1.chrom,
start=i1.start,
stop=i1.stop + gap + breakpoint,
feature='ch',
score=i1.score,
strand='-',
source='.',
frame='.',
attrs=i1.attrs.items())
else:
cs1 = construct_gff_interval(chrom=i2.chrom,
start=i2.start,
stop=i2.stop - breakpoint,
feature='ch',
score=i2.score,
strand='+',
source='.',
frame='.',
attrs=i2.attrs.items())
cs2 = construct_gff_interval(chrom=i1.chrom,
start=i1.start - gap - breakpoint,
stop=i1.stop,
feature='ch',
score=i1.score,
strand='+',
source='.',
frame='.',
attrs=i1.attrs.items())
else:
if (i1.strand == '+'):
cs1 = construct_gff_interval(chrom=i1.chrom,
start=i1.start,
stop=i1.stop + gap + breakpoint,
feature='ch',
score=i1.score,
strand=i1.strand,
source='.',
frame='.',
attrs=i1.attrs.items())
cs2 = construct_gff_interval(chrom=i2.chrom,
start=i2.start + breakpoint,
stop=i2.stop,
feature='ch',
score=i2.score,
strand=i2.strand,
source='.',
frame='.',
attrs=i2.attrs.items())
else:
cs1 = construct_gff_interval(chrom=i1.chrom,
start=i1.start - gap - breakpoint,
stop=i1.stop,
feature='ch',
score=i1.score,
strand=i1.strand,
source='.',
frame='.',
attrs=i1.attrs.items())
cs2 = construct_gff_interval(chrom=i2.chrom,
start=i2.start,
stop=i2.stop - breakpoint,
feature='ch',
score=i2.score,
strand=i2.strand,
source='.',
frame='.',
attrs=i2.attrs.items())
return cs1, cs2
for (chrom, geneid), features in gene_id_dict.items():
fd = dict([ (int(x.type == 'gene'), x) for x in features])
transcript = fd[0]
gene = fd[1]
genesymbol = gene.qualifiers.get('gene', [geneid])[0]
annotation = transcript.qualifiers['product'][0].replace(";", ' ')
function = transcript.qualifiers.get('note', [''])[0].replace(";", ' ')
strand = strand_dict[feature.location.strand]
cds = "%d:%d" % (transcript.location.start, transcript.location.end)
if(strand == '+'):
distance = transcript.location.start - gene.location.start
else:
distance = gene.location.end - transcript.location.end
newint = construct_gff_interval(chrom, gene.location.start, gene.location.end, transcript.type, score='0', strand=strand, attrs=[('ID', geneid), ('genesymbol', genesymbol), ('annotation', annotation), ('function', function), ('cds', cds), ('distance', distance), ('tss_variants', '1') ])
sys.stdout.write(str(newint))
#if('CDS' in [x.type for x in v]):
#print()
#print("-"*140)
#print()
#for el in v:
#print(el)
#print(len(name2gene))
for ga in gene_annotation_list:
parent_name = ga.attrs.get('Parent')
if (parent_name):
parent = name2gene.get(ga.attrs['Parent'])
if (parent):
if (parent.strand == '+'):
distance = ga.start - parent.start
else:
distance = parent.stop - ga.stop
distance = 0
aint = construct_gff_interval(
parent.chrom,
parent.start,
parent.stop,
'gene',
score='0',
strand=parent.strand,
source='ncbi_af',
frame='.',
attrs=[('ID', parent.attrs['ID']),
('genesymbol', parent.attrs.get('gene', 'None')),
('annotation', ga.attrs.get('Note', 'None')),
('product', ga.attrs.get('product', 'None')),
('cds', "%d:%d" % (ga.start + 1, ga.stop)),
('tss_variants', '1'), ('distance', str(distance))])
sys.stdout.write(str(aint))
required=True,
type=str,
help="Path to the genome, fasta format")
parser.add_argument('--gff',
nargs='?',
default=False,
const=True,
type=bool,
help="If set, output is in gff format")
args = parser.parse_args()
genome = next(SeqIO.parse(args.genome, 'fasta')).seq
for interval in BedTool(args.path):
seq = str(genome[interval.start:interval.end].upper())
if (args.gff):
sys.stdout.write(
str(
construct_gff_interval(interval.chrom,
interval.start,
interval.end,
'un',
score=interval.score,
strand=interval.strand,
source='un',
frame='.',
attrs=[('Name', interval.name),
('seq', seq)])))
else:
print("\t".join([str(x) for x in list(interval) + [seq]]))
bacteria2prophages[prophage.bacteria].append(prophage)
for bname, bannotation, blength in bacterial_annotation:
for prophage in sorted(bacteria2prophages[bname], key=lambda x: x.start):
scale = 1000 / blength
host_start = round(prophage.start * scale)
host_end = round(prophage.end * scale) + 1
interval = construct_gff_interval(
bname,
prophage.start,
prophage.end,
'prophage',
score='0',
strand=prophage.strand,
source='ovidiu',
frame='.',
attrs=[('host_length', str(blength)), ('host_family', bannotation),
('Name', "_".join([
str(x) for x in (bname, prophage.start, prophage.end)
])),
('rlength',
"%1.4f" % ((prophage.end - prophage.start) / blength)),
('integrase', str(prophage.integrase)),
('host_start', "%d" % host_start),
('host_end', "%d" % host_end)])
if (host_start > 0 and host_end < 1000):
sys.stdout.write(str(interval))
else:
sys.stderr.write(
"\nProphage region is outside bacterial borders\n%s" %
interval)
parser = argparse.ArgumentParser(description='Converts transcripts raw file into ncbi format');
parser.add_argument('--transcripts', nargs = '?', required=True, type = str, help = "Path to the transcripts, raw format");
parser.add_argument('--genome', nargs = '?', required=True, type = str, help = "Path to the genome, fasta format");
args = parser.parse_args();
with open(args.genome) as f:
chrom = next(f).strip()[1:]
with open(args.transcripts) as f:
next(f);
for l in f:
a = [x.strip().replace('"', '').replace(';', '') for x in l.strip().split("\t")]
aint = construct_gff_interval( chrom, int(a[2]), int(a[3]), 'gene', score='0', strand=a[4], source='ncbi_af', frame='.', attrs=[ ('ID', 'gene-%s' % a[0]), ('gene_biotype', 'protein_coding'), ('Name', 'gene-%s' % a[0]), ('product', a[1]) ] );
sys.stdout.write(str(aint))
#453-Cg-phage-CL31_S7_L001_R1_001_contig_1 un gene 2 2422 0 + . ID=gene-CL_1_Hyp.; gene_biotype=protein_coding; Name=gene-CL_1_Hyp.; product=Protein (A. faecalis)
# ['CL_53', 'Hyp. Protein (Corynebacterium)', '44425', '44796', '-']
#aint = construct_gff_interval(parent.chrom, parent.start, parent.stop, 'gene', score='0', strand=parent.strand, source='ncbi_af', frame='.', attrs=[('ID', parent.attrs['ID']), ('genesymbol', parent.attrs.get('gene', 'None')), ('annotation', ga.attrs.get('Note', 'None')), ('product', ga.attrs.get('product', 'None')), ('cds', "%d:%d" % (ga.start+1, ga.stop)), ('tss_variants', '1'), ('distance', str(distance)) ] );
help="adfsa")
args = parser.parse_args()
if (os.stat(args.path).st_size != 0):
coverage_dict = coverage2dict(args.coverage)
convolution = list(coverage2dict(args.convolution).values())[0]
for peak in BedTool(args.path):
top = int(peak.name)
topcoverage = coverage_dict[peak.chrom][top]
total_coverage = coverage_dict[peak.chrom][peak.start:peak.stop]
newint = construct_gff_interval(peak.chrom,
peak.start,
peak.stop,
'binding_peak',
score=peak.score,
strand=peak.strand,
source='un',
frame='.',
attrs=[("Name", peak.name),
("topcoverage", topcoverage),
("total_coverage",
total_coverage)])
if (max(total_coverage) >= 1.1 * topcoverage):
fontsize = 24
fig, ax1 = plt.subplots(figsize=(16, 9))
plt.tight_layout(rect=[0.1, 0.1, 0.95, 0.95])
ax1.plot(total_coverage, 'b-')
ax1.plot(top - peak.start, topcoverage, 'r*', linewidth=5)
ax1.set_xlabel("position (nt)", fontsize=fontsize)
ax1.set_ylabel('coverage', color='b', fontsize=fontsize)
ax1.tick_params('y', colors='b')
else:
d = d_plus
s_strand = '+'
cs = cs_plus
peak2genestarts[interval.name] = start2genes[cs], d, s_strand
for interval in peaks:
topcoverage, ldrop, rdrop, start, end = peak2cov[interval.name]
lg = peak2genes[interval.name]
sg, dg, s_strand = peak2genestarts[interval.name]
anint = construct_gff_interval(interval.chrom,
start,
end,
'peak',
score=interval.score,
strand=interval.strand,
source='af_peak_detection',
frame='.',
attrs=[('Name', interval.name),
('topcoverage',
"%1.3f" % topcoverage),
('ldrop', "%1.3f" % ldrop),
('rdrop', "%1.3f" % rdrop),
('genes', ",".join(lg)),
('start_genes', ",".join(sg)),
('start_gene_distance', dg),
('start_gene_strand', s_strand)])
sys.stdout.write(str(anint))
#construct_gff_interval(chrom, start, stop, feature, score='0', strand='.', source='un', frame='.', attrs=[]);
parser.add_argument('path',
metavar='N',
nargs='?',
type=str,
help="Path to the genbank file")
args = parser.parse_args()
strand_dict = {1: '+', -1: '-'}
#NC_003450.3 RefSeq gene 4766 5302 . + . ID=gene-NCgl0004;Dbxref=GeneID:1021292;Name=NCgl0004;gbkey=Gene;gene_biotype=protein_coding;gene_synonym=Cgl0005;locus_tag=NCgl0004
for seqrecord in SeqIO.parse(args.path, 'genbank'):
chrom = seqrecord.name
chrom = '453-Cg-phage-CL31_S7_L001_R1_001_contig_1'
for feature in seqrecord.features:
if (feature.type == 'CDS'):
temp = feature.qualifiers['label'][0].replace(";", ":")
a = temp.split(" ")
geneid = "gene-%s" % a[0]
product = " ".join(a[1:])
newint = construct_gff_interval(
chrom,
feature.location.start,
feature.location.end,
'gene',
score='0',
strand=strand_dict[feature.location.strand],
attrs=[('ID', geneid), ('gene_biotype', 'protein_coding'),
('Name', geneid), ('product', product)])
sys.stdout.write(str(newint))
#NC_003888.3 RefSeq gene 14044 14454 . - . ID=gene-SCO0012;Dbxref=GeneID:1095452;Name=SCO0012;gbkey=Gene;gene_biotype=protein_coding;gene_synonym=SCJ30.07c;locus_tag=SCO0012
#NC_003888.3 RefSeq CDS 14044 14454 . - 0 ID=cds-NP_624373.1;Parent=gene-SCO0012;Dbxref=Genbank:NP_624373.1,GeneID:1095452;Name=NP_624373.1;Note=SCJ30.07c%2C unknown%2C len: 136 aa%3B predicted by GC Frameplot%2C Hidden Markov model and amino acid usage.;gbkey=CDS;locus_tag=SCO0012;product=hypothetical protein;protein_id=NP_624373.1;transl_table=11
# phiSco2_Bielefeld Prodigal:2.6 CDS 1400 2647 . - 0 ID=phiSco2_00003;Parent=phiSco2_00003_gene;inference=ab initio prediction:Prodigal:2.6,similar to AA sequence:Viruses2.aa:YP_009208303.1;locus_tag=phiSco2_00003;product=hypothetical protein SEA_AMELA_25
for interval in BedTool(args.path):
if (interval[2] == 'CDS'):
name = 'gene-' + interval.attrs['ID']
gene = construct_gff_interval(interval.chrom,
interval.start,
interval.stop,
'gene',
score='0',
strand=interval.strand,
source='custom',
frame='.',
attrs=[('ID', name),
('gene_biotype', 'protein_coding')
])
sys.stdout.write(str(gene))
cds = construct_gff_interval(
interval.chrom,
interval.start,
interval.stop,
'CDS',
score='0',
strand=interval.strand,
source='custom',
frame='.',
nargs='?',
type=str,
help="Path to the ncbi annotation file, gff format")
args = parser.parse_args()
curgene = None
cds_list = []
for interval in BedTool(args.path):
if (interval[2] == 'gene'):
curgene = interval
if (interval[2] == 'CDS'):
aint = construct_gff_interval(
interval.chrom,
interval.start,
interval.stop,
'cds',
score='0',
strand=interval.strand,
source='.',
frame='.',
attrs=[('ID', interval.attrs['Parent']),
('genesymbol', curgene.attrs['Name']),
('annotation', interval.attrs.get('Note', 'None')),
('product', interval.attrs.get('product', 'None'))])
cds_list.append(aint)
cds_list.sort(key=lambda x: x.start)
for aint in cds_list:
sys.stdout.write(str(aint))
