def score_seq(known, guess, gapopen=10, gapextend=1):
cmd = 'needle -asequence %(cb)s -bsequence %(seq)s -aformat score -gapopen %(go)f -gapextend %(ge)s -outfile %(out)s'
with NamedTemporaryFile() as conb_handle:
fasta_writer(conb_handle, [('SeqA', known)])
conb_handle.flush()
os.fsync(conb_handle.fileno())
with NamedTemporaryFile() as seq_handle:
fasta_writer(seq_handle, [('Seq1', guess)])
seq_handle.flush()
os.fsync(seq_handle.fileno())
with NamedTemporaryFile() as out_handle:
param_dict = {
'cb':conb_handle.name,
'seq':seq_handle.name,
'out':out_handle.name,
'go':gapopen,
'ge':gapextend
}
cmd_list = shlex.split(cmd % param_dict)
check_call(cmd_list)
for line in out_handle:
parts = line.split()
if (len(parts) == 4):
return float(parts[-1][1:-2])
def map_seqs_to_ref(input_seqs, retry=0):
"""Maps a set of (name, seq) pairs to HXB2 using LANL"""
base_seqs = StringIO()
fasta_writer(base_seqs, input_seqs)
base_seqs.seek(0)
fasta_seqs = base_seqs.read()
br = build_browser()
br.open('http://www.hiv.lanl.gov/content/sequence/LOCATE/locate.html')
logging.debug('Opened Browser to LANL')
br.select_form(nr=1)
br.form['SEQ'] = fasta_seqs
resp = br.submit()
logging.info('Submitted Seqs to LANL')
try:
soup = BeautifulSoup(resp)
except IncompleteRead:
if retry > 5:
raise ValueError
return map_seqs_to_ref(input_seqs, retry=retry+1)
rows = []
count = 0
for name, seq, table in zip(yield_seq_names(soup), yield_query_seqs(soup), yield_seq_tables(soup)):
count += 1
for row in yield_row_vals(table, seq):
row['Name'] = name
rows.append(row)
logging.info('LANL returned %i regions for %i patients' % (len(rows), count))
return rows
def _write_seqs(self, X, handle):
seqs = []
for row in range(X.shape[0]):
seq = ''.join(X[row])
seqs.append(('Seq-%03i' % row, ''.join(l for l in seq if l.isalpha())))
fasta_writer(handle, seqs)
handle.flush()
os.fsync(handle.fileno())
def run_FastTree(seqs, alphabet=generic_protein, tmp_path=None, uniq_seqs=False):
if uniq_seqs:
trans_names = defaultdict(list)
norm_seq_names = {}
for num, (name, seq) in enumerate(seqs):
trans_names[seq].append(name)
new_name = 'Seq-%i' % num
norm_seq_names[seq] = new_name
uni_seqs = []
name_defs = {}
for seq, new_name in norm_seq_names.items():
uni_seqs.append((new_name, seq))
name_defs[new_name] = trans_names[seq]
out_tree = run_FastTree(uni_seqs, alphabet=alphabet, tmp_path=tmp_path)
tax_set = out_tree.taxon_set
for old_name, new_names in name_defs.items():
node = out_tree.find_node_with_taxon_label(old_name)
if node:
names = iter(new_names)
node.taxon.label = names.next()
parent = node.parent_node
edge_dist = node.edge.length
for name in names:
parent.new_child(taxon=tax_set.new_taxon(label=name),
edge_length=edge_dist)
return out_tree
else:
base_path = os.path.dirname(__file__)
with NTF(dir=tmp_path, suffix='.fasta') as handle:
fasta_writer(handle, seqs)
handle.flush()
os.fsync(handle)
tdict = {
'alpha': '-nt' if alphabet == generic_dna else '',
'path': handle.name
}
cmd = os.path.join(base_path,
'FastTree %(alpha)s -quiet %(path)s' % tdict)
cmd_list = shlex.split(cmd)
tree_str = check_output(cmd_list)
return dendropy.Tree(stream=StringIO(tree_str), schema='newick')
def testSeqTransformer_from_fasta():
handle = StringIO()
inseqs = [('Seq1', 'ATGTCG'),
('Seq2', 'ATGG'),
('Seq3', 'ATGTAHYTD')]
fasta_writer(handle, inseqs)
handle.seek(0)
outdata = np.array(['ATGTCG---',
'ATGG-----',
'ATGTAHYTD'])
names, out = HIVAlignTools.SeqTransformer.get_from_fasta_handle(handle)
ok_(np.all(out == outdata))
ok_(all(t == g for t, g in zip(names, ['Seq1', 'Seq2', 'Seq3'])))
def align_with_lastz(input_seqs, ref_seqs):
"""Aligns set of query sequences with a reference."""
with tmp_directory() as tmp_dir:
seq_file = os.path.join(tmp_dir, "query.fasta")
ref_file = os.path.join(tmp_dir, "ref.fasta")
out_file = os.path.join(tmp_dir, "res.fasta")
with open(seq_file, "w") as handle:
fasta_writer(handle, input_seqs)
with open(ref_file, "w") as handle:
fasta_writer(handle, ref_seqs)
call_lastz(seq_file, ref_file, out_file)
with open(out_file) as handle:
return list(SAMreader(handle))
def blast_all_v_all(seqsA, seqsB, block_size=20):
dpath = '/home/will/tmpstuf/haptest/tmpseqs/'
with NTF(suffix='.fa', dir=dpath, delete=False) as db_handle:
fasta_writer(db_handle, seqsA)
db_handle.flush()
os.fsync(db_handle.fileno())
cmd = 'makeblastdb -in %s -dbtype nucl' % db_handle.name
cmd_list = shlex.split(cmd)
check_call(cmd_list)
align_func = partial(check_seqs, db_handle.name)
check_iterable = islice(yield_blocks(iter(seqsB), 200), 20)
with ProcessPoolExecutor(max_workers=5) as pool:
res_iter = pool.map(align_func, check_iterable)
for num, block in enumerate(res_iter):
print num, len(block)
def check_seqs(db_path, seqs):
cmd = "blastn -db %(db)s -query %(q)s -outfmt '10 qseqid sseqid pident nident' -num_threads 20 -max_target_seqs 1"
fields = ['SeqA', 'SeqB', 'pident', 'nident']
dpath = '/home/will/tmpstuf/haptest/tmpseqs/'
with NTF(suffix='.fa', dir=dpath, delete=False) as check_handle:
fasta_writer(check_handle, seqs)
check_handle.flush()
os.fsync(check_handle.fileno())
tdict = {
'db':db_path,
'q':check_handle.name
}
cmd_list = shlex.split(cmd % tdict)
out = check_output(cmd_list)
reader = csv.DictReader(StringIO(out), fieldnames=fields)
return list(reader)
sys.path.append('/home/will/PySeqUtils/')
# <codecell>
from GeneralSeqTools import fasta_reader, fasta_writer, WebPSSM_V3_series
import glob
# <codecell>
files = [('x4_seqs.fasta.old', 'x4_seqs.fasta'),
('r5_seqs.fasta.old', 'r5_seqs.fasta')]
for ifile, ofile in files:
with open(ifile) as handle:
with open(ofile, 'w') as ohandle:
for name, seq in fasta_reader(handle):
fasta_writer(ohandle, [(name, seq[1:-1])])
# <codecell>
subtype_files = glob.glob('/home/will/WLAHDB_data/SubtypeGuess/*.gb')
subtypes = []
for f in subtype_files:
gb = f.rsplit(os.sep, 1)[-1].split('.')[0]
with open(f) as handle:
subtype = handle.next().strip()
if subtype != 'Unk':
subtypes.append((int(gb), subtype))
subtype_df = pd.DataFrame(subtypes, columns = ['GI', 'Subtype'])
subtype_ser = subtype_df.groupby('GI')['Subtype'].first()
'Tat-1-seq-align', 'Tat-2-seq-align', 'LTR-seq-align']
four = wanted_data[fourkb_cols].dropna()
wseqs = set()
with open('/home/will/Dropbox/HIVseqs/BensTropismLabels.csv') as handle:
for row in csv.DictReader(handle, delimiter=','):
wseqs.add(row['Patient ID'])
for col in four.columns:
found = set()
prot = col.rsplit('-', 2)[0]
fname = 'AlignForBenj/fourKB_%s.fasta' % prot
with open(fname, 'w') as handle:
for seq, name in zip(four[col], four.index):
if name in wseqs and name not in found:
fasta_writer(handle, [(name+'-'+trop_dict[name], ''.join(seq))])
found.add(name)
print prot, len(found)
# <codecell>
foukb_lanl = ['AB078005', 'AB221126', 'AB253432', 'AB286955',
'AB287365', 'AB287367', 'AB287368', 'AB287369',
'AB480695', 'AB485642', 'AB565479', 'AB565496',
'AB565497', 'AB565499', 'AB565500', 'AB565502',
'AB604946', 'AB604948', 'AB604950', 'AB604951',
'AB641836', 'AF003887', 'AF003888', 'AF004394',
'AF042100', 'AF042101', 'AF538302', 'AF538303',
'AF538307', 'AJ271445', 'AY173953', 'AY352275',
'AY835748', 'AY835754', 'AY835759', 'AY835762',
'AY835766', 'AY835769', 'AY835770', 'AY835774',
out_seqs.append({
'Accession':name,
'PSSM':tdata.ix[name]['PSSMScore'],
'Seq':seq
})
out_df = pd.DataFrame(out_seqs)
# <codecell>
with open('extra_brain.fasta', 'w') as handle:
found = set()
for name, seq in aa_seq_list:
if name in wanted_acc:
if seq not in found:
fasta_writer(handle, [(name+'NEWSEQS!!!!!!', seq)])
found.add(seq)
# <codecell>
out_df.to_csv('brain_x4.tsv', sep='\t')
# <codecell>
ax = trim_lanl.boxplot(column='PSSMScore', by = 'STissue', vert=False, figsize=(10,10))
ax.set_ylim([-1, ax.get_ylim()[1]])
order = ['R5-E', 'R5-1', 'R5-2', 'R5-3', 'R5-P', 'X4-P', 'X4']
for line, name in zip(pssm_bins, order):
ax.annotate(name, (line-1.5, -0.5), fontsize=10)
ax.annotate('X4', (-1.5, -0.5), fontsize=10)
from Bio.SeqIO.AbiIO import AbiIterator
files = glob.glob('../Wigdahl Trace files/2:11:11/*.ab1')
seqs = []
for f in files:
rec = AbiIterator(open(f, mode = 'rb'), trim = True).next()
seqs.append( (rec.id, rec.seq.tostring()) )
# <codecell>
!/home/will/staden-2.0.0b9.x86_64/bin/convert_trace --help
# <codecell>
res = call_muscle(seqs)
with open('align_data.fasta', 'w') as handle:
fasta_writer(handle, res)
# <codecell>
from HIVTransTool import process_seqs
results = list(process_seqs(seqs[:50], extract_regions = True, known_names = 50))
# <codecell>
for row in results:
if row['RegionName'] == 'LTR5':
print row['Name'], row['QueryNuc']
# <codecell>
stop = -1
path = 'HIV1_ALL_2012_env_PRO.fasta'
outpath = 'HIV1_ALL_2012_gp41_PRO.fasta'
with open(path) as handle:
for name, seq in islice(fasta_reader(handle), 20):
tseq = seq[start:stop]
print tseq[:5], tseq[-5:]
# <codecell>
seqs = []
with open(path) as handle:
for name, seq in fasta_reader(handle):
seqs.append((name, seq[start:stop]))
with open(outpath, 'w') as handle:
fasta_writer(handle, seqs)
# <codecell>
from Bio import Entrez
from Bio import SeqIO
ids = '544451412,544451410,544451408,544451406,544451404,544451402,544451400,544451398,544451396'
fetch_handle = Entrez.efetch(db="nucleotide", rettype="gb", retmode="text",
id=ids)
records = list(SeqIO.parse(fetch_handle, "gb"))
# <codecell>
rec = records[0]
