def pssm_search(request):
"""Scans the genome with the given motif."""
if request.method == 'POST':
form = BindingSiteSearchForm(request.POST)
if form.is_valid():
genome = form.cleaned_data['genome']
motif = bioutils.build_motif(form.cleaned_data['sites'])
# Find a threshold
dist = motif.pssm.distribution(precision=10**4)
threshold = dist.threshold_patser()
hits = bioutils.pssm_search(
motif.pssm, genome.seq, threshold=threshold)
matches = []
for pos, strand, score in hits:
seq = genome.seq[pos:pos+motif.length]
if strand == -1:
seq = bioutils.reverse_complement(seq)
matches.append({
'start': pos,
'end': pos + motif.length,
'strand': strand,
'seq': seq,
'score': score})
return render(
request,
'pssm_search_results.html',
{'matches': matches,
'weblogo': bioutils.weblogo_uri(form.cleaned_data['sites']),
'threshold': threshold})
else:
form = BindingSiteSearchForm()
return render(request, 'pssm_search.html', {'form': form})
def generate_motif_file():
"""Generates motif file containing all motifs for each TF-instance."""
export_file = os.path.join(settings.STATICFILES_DIRS[0],
'collectf_meme.cm')
with open(export_file, 'w') as f:
for TF_instance in models.TFInstance.objects.all():
print TF_instance
reports = motif_report.build_motif_reports(
models.Curation_SiteInstance.objects.filter(
curation__TF_instances=TF_instance))
if reports:
sites = reports[0].aligned_sites
if len(sites) < 10:
continue
motif = build_motif(sites)
f.write('>%s %s_%s\n' % (
dbxref.to_uniprot_dbxref(TF_instance.TF_instance_id),
reports[0].TF_name,
reports[0].short_species_name))
f.write('%s\n' % '\t'.join(map(str, motif.counts['A'])))
f.write('%s\n' % '\t'.join(map(str, motif.counts['C'])))
f.write('%s\n' % '\t'.join(map(str, motif.counts['G'])))
f.write('%s\n' % '\t'.join(map(str, motif.counts['T'])))
def export_PSFM(meta_sites, **kwargs):
"""Exports Position-Specific-Frequency-Matrix"""
format = kwargs['format']
rows = export_base(meta_sites)
aligned = lasagna.lasagna([m.delegate_site_instance for m in meta_sites])
motif = bioutils.build_motif(aligned)
consensus = motif.degenerate_consensus
TF_name= ','.join(set(row['TF_name'] for row in rows))
sp = ','.join(set('_'.join(row['organism'].split()) for row in rows))
lines = []
if format == 'JASPAR':
lines.append('> CollecTF_%s_%s' % (TF_name, sp))
lines.append('A [ %s ]' % (' '.join(map(str, motif.counts['A']))))
lines.append('C [ %s ]' % (' '.join(map(str, motif.counts['C']))))
lines.append('G [ %s ]' % (' '.join(map(str, motif.counts['G']))))
lines.append('T [ %s ]' % (' '.join(map(str, motif.counts['T']))))
elif format == 'TRANSFAC':
lines.append('ID %s' % TF_name)
lines.append('BF %s' % sp)
lines.append('PO\tA\tC\tG\tT')
lines.extend('%02d\t%d\t%d\t%d\t%d\t%s' % (po+1, motif.counts['A'][po],
motif.counts['C'][po],
motif.counts['G'][po],
motif.counts['T'][po],
consensus[po])
for po in range(motif.length))
lines.append('XX')
elif format == 'raw_fasta':
lines.append('>CollecTF_%s_%s' % (TF_name, sp))
lines.extend('%d\t%d\t%d\t%d' % (motif.counts['A'][po],
motif.counts['C'][po],
motif.counts['G'][po],
motif.counts['T'][po])
for po in range(motif.length))
return '\n'.join(lines)
def pssm_search(request):
"""Scans the genome with the given motif."""
if request.method == 'POST':
form = BindingSiteSearchForm(request.POST)
if form.is_valid():
genome = form.cleaned_data['genome']
motif = bioutils.build_motif(form.cleaned_data['sites'])
# Find a threshold
dist = motif.pssm.distribution(precision=10**4)
threshold = dist.threshold_patser()
hits = bioutils.pssm_search(motif.pssm,
genome.seq,
threshold=threshold)
matches = []
for pos, strand, score in hits:
seq = genome.seq[pos:pos + motif.length]
if strand == -1:
seq = bioutils.reverse_complement(seq)
matches.append({
'start': pos,
'end': pos + motif.length,
'strand': strand,
'seq': seq,
'score': score
})
return render(
request, 'pssm_search_results.html', {
'matches': matches,
'weblogo': bioutils.weblogo_uri(
form.cleaned_data['sites']),
'threshold': threshold
})
else:
form = BindingSiteSearchForm()
return render(request, 'pssm_search.html', {'form': form})
def motif_pwm_columns(sites):
"""Returns the columns of teh PWM of the motif."""
motif = bioutils.build_motif(sites)
motif.pseudocounts = 1.0
return [{let: motif.pwm[let][i] for let in 'ACGT'}
for i in xrange(len(motif))]
def motif_pwm_columns(sites):
"""Returns the columns of teh PWM of the motif."""
motif = bioutils.build_motif(sites)
motif.pseudocounts = 1.0
return [{let: motif.pwm[let][i]
for let in 'ACGT'} for i in xrange(len(motif))]