def test_arguments(self):
seq = SeqIO.read('tests/data/jackhmmer_seq.fasta', 'fasta',
alphabet=Alphabet.generic_protein)
seqdb = SeqIO.read('tests/data/matchtarget.fasta', 'fasta',
alphabet=Alphabet.generic_protein)
j = HMMER.jackhmmer(seq, seqdb)
args = j.getArgs(max=True, E='something')
self.assertEqual(args, ['--max', '-E', 'something'])
def update_models():
"""Recalculate the HMM models"""
print "Extracting C-Termini..."
ct = extract.get_c_terminus(extract.extract(localization=None))
print "Done. Got {} tails".format(len(ct))
(E, Ep, DYW) = utils.get_tail_consensus()
print "E"
j = HMMER.jackhmmer(E,ct)
j.hmms[-1].name = 'E'
hmmfile.write(j.hmms[-1], os.path.join(utils.HMMDir, 'E.hmm'))
print "E+"
j = HMMER.jackhmmer(Ep,ct)
j.hmms[-1].name = 'E+'
hmmfile.write(j.hmms[-1], os.path.join(utils.HMMDir, 'E+.hmm'))
print "DYW"
j = HMMER.jackhmmer(DYW,ct)
j.hmms[-1].name = 'DYW'
hmmfile.write(j.hmms[-1], os.path.join(utils.HMMDir, 'DYW.hmm'))
def test_jackhmmer(self):
seq = SeqIO.read('tests/data/jackhmmer_seq.fasta', 'fasta',
alphabet=Alphabet.generic_protein)
seqdb = SeqIO.read('tests/data/matchtarget.fasta', 'fasta',
alphabet=Alphabet.generic_protein)
j = HMMER.jackhmmer(seq, seqdb)
#load the expected output
seq_ = HMMER.wrap_seqrecords([seq,])
seqdb_ = HMMER.wrap_seqrecords([seqdb,])
m = matchfile.load('tests/data/jack_out', seq_, seqdb_)
self.assertEqual(m, j.matches)
def test_jackhmmer_dna(self):
seq = SeqIO.read('tests/data/jackhmmer_seq.fasta', 'fasta',
alphabet=Alphabet.generic_protein)
seqdb = SeqIO.read('tests/data/dna_target.fasta', 'fasta',
alphabet=Alphabet.generic_dna)
j = HMMER.jackhmmer(seq, seqdb)
#load the expected output
seqdb_prot = SeqIO.read('tests/data/matchtarget.fasta', 'fasta',
alphabet=Alphabet.generic_protein)
seq_ = HMMER.wrap_seqrecords([seq,])
seqdb_ = HMMER.wrap_seqrecords([seqdb_prot,])
matches = matchfile.load('tests/data/jack_out', seq_, seqdb_)
#scale the matches' locations to match the protein search
for m in matches:
m.scale(3)
self.assertEqual([str(m) for m in matches], [str(m) for m in j.matches])
def find_homologs():
"""Predict homologs of PPRs in other genomes based on footprints"""
pprs = load_pprs()
plastids = load_plastids(exclude=[
"Arabidopsis thaliana",
])
known_binding = SeqIO.read("output/ARA_annotated.gb", "gb")
exact_features = [
f for f in known_binding.features if "exact" in f.type.lower()
]
ara_genes = [f for f in known_binding.features if f.type.lower() == "gene"]
ara_genes.sort(key=lambda g: g.location.start)
print "Loaded {} pprs and {} plastids".format(len(pprs), len(plastids))
for k, ppr in enumerate(pprs):
print "Searching for homologs of \'{}\' ({}/{})".format(
ppr.name, k + 1, len(pprs))
footprints = [
f for f in exact_features
if f.type.lower() == "{}_exact".format(ppr.name.lower())
]
ppr.genes = [get_closest_gene(f, ara_genes) for f in footprints]
print "\tFound {} original genes, {}".format(len(
ppr.genes), [g.qualifiers['gene'] for g in ppr.genes])
ppr.potentialHomologs = {}
for i, plastid in enumerate(plastids):
if plastid.name != "Alsophila spinulosa":
continue
print "\t\tSearch {}/{}".format(i + 1, len(plastids))
#search for homologs of each gene
homologs = []
for gene in ppr.genes:
g = SeqRecord(gene.extract(known_binding.seq).translate())
search = HMMER.jackhmmer(g, plastid)
print "{} -> {} homologs".format(gene.qualifiers['gene'],
len(search.matches))
homologs += search.getFeatures(
type="{}_hl".format(gene.qualifiers['gene']))
#extract the sequence surrounding each homolog
for h in homologs:
h.location = FeatureLocation(
max(0, h.location.start - 500),
min(len(plastid), h.location.end + 500))
homologs = [SeqRecord(h.extract(plastid.seq)) for h in homologs]
#find exact or close to exact binding domains for each and add to the
#list of potential homologs for the PPR
ph = []
for h in homologs:
domains = []
for exact in ppr.exact:
try:
domains += binding.get_domains(exact,
h,
percentile=100.0,
gaps=0)
except KeyError:
continue
if domains:
domains.sort(key=lambda d: -d.qualifiers['odds'])
seq = str(domains[0].extract(h).seq)
similarity = max([
sequence_similarity(original, seq)
for original in ppr.footprints
])
print " {} -> \'{}\'".format(h.type, seq)
ph.append((similarity, seq))
ph.sort(key=lambda p: -p[0])
ppr.potentialHomologs[plastid.name] = ph
#try and avoid running out of RAM
gc.collect()
for ppr in pprs:
print "\'{}\' footprints = {}".format(ppr.name, ppr.footprints)
print "potential homologs"
for key, value in ppr.potentialHomologs.iteritems():
print "{}: {}".format(key, value)
return
stats = []
for plastid in plastids:
length = 0
similarity = 0.0
for ppr in pprs:
length += len(ppr.potentialHomologs[plastid.name])
similarity += sum(
[p[0] for p in ppr.potentialHomologs[plastid.name]])
try:
stats.append({
'name': plastid.name,
'avg_similarity': similarity / float(length),
'avg_homologs': length / len(pprs),
})
except ZeroDivisionError:
stats.append({
'name': plastid.name,
'avg_similarity': 0.0,
'avg_homologs': 0,
})
stats.sort(key=lambda s: -s['avg_similarity'])
f = open("tmp", "w")
for s in stats[0:50]:
f.write("{name}, {avg_similarity}, {avg_homologs}\n".format(**s))
f.close()
def find_homologs():
"""Predict homologs of PPRs in other genomes based on footprints"""
pprs = load_pprs()
plastids = load_plastids(exclude=["Arabidopsis thaliana",])
known_binding = SeqIO.read("output/ARA_annotated.gb", "gb")
exact_features = [f for f in known_binding.features if
"exact" in f.type.lower()]
ara_genes = [f for f in known_binding.features if f.type.lower() == "gene"]
ara_genes.sort(key=lambda g: g.location.start)
print "Loaded {} pprs and {} plastids".format(len(pprs), len(plastids))
for k,ppr in enumerate(pprs):
print "Searching for homologs of \'{}\' ({}/{})".format(
ppr.name,k+1,len(pprs))
footprints = [f for f in exact_features if
f.type.lower() == "{}_exact".format(ppr.name.lower())]
ppr.genes = [get_closest_gene(f, ara_genes) for f in footprints]
print "\tFound {} original genes, {}".format(len(ppr.genes),
[g.qualifiers['gene'] for g in ppr.genes])
ppr.potentialHomologs = {}
for i,plastid in enumerate(plastids):
if plastid.name != "Alsophila spinulosa":
continue
print "\t\tSearch {}/{}".format(i+1, len(plastids))
#search for homologs of each gene
homologs = []
for gene in ppr.genes:
g = SeqRecord(gene.extract(known_binding.seq).translate())
search = HMMER.jackhmmer(g, plastid)
print "{} -> {} homologs".format(gene.qualifiers['gene'],
len(search.matches))
homologs += search.getFeatures(type="{}_hl".format(gene.qualifiers['gene']))
#extract the sequence surrounding each homolog
for h in homologs:
h.location = FeatureLocation(
max(0,h.location.start - 500),
min(len(plastid), h.location.end+500))
homologs = [SeqRecord(h.extract(plastid.seq)) for h in homologs]
#find exact or close to exact binding domains for each and add to the
#list of potential homologs for the PPR
ph = []
for h in homologs:
domains = []
for exact in ppr.exact:
try:
domains += binding.get_domains(exact, h, percentile=100.0, gaps=0)
except KeyError:
continue
if domains:
domains.sort(key=lambda d: -d.qualifiers['odds'])
seq = str(domains[0].extract(h).seq)
similarity = max([sequence_similarity(original, seq) for
original in ppr.footprints])
print " {} -> \'{}\'".format(h.type, seq)
ph.append((similarity, seq))
ph.sort(key=lambda p: -p[0])
ppr.potentialHomologs[plastid.name] = ph
#try and avoid running out of RAM
gc.collect()
for ppr in pprs:
print "\'{}\' footprints = {}".format(ppr.name, ppr.footprints)
print "potential homologs"
for key,value in ppr.potentialHomologs.iteritems():
print "{}: {}".format(key, value)
return
stats = []
for plastid in plastids:
length = 0
similarity = 0.0
for ppr in pprs:
length += len(ppr.potentialHomologs[plastid.name])
similarity += sum([p[0] for p in ppr.potentialHomologs[plastid.name]])
try:
stats.append({'name': plastid.name,
'avg_similarity': similarity / float(length),
'avg_homologs': length / len(pprs),})
except ZeroDivisionError:
stats.append({'name': plastid.name,
'avg_similarity': 0.0,
'avg_homologs': 0,})
stats.sort(key=lambda s: -s['avg_similarity'])
f = open("tmp", "w")
for s in stats[0:50]:
f.write("{name}, {avg_similarity}, {avg_homologs}\n".format(**s))
f.close()
