def _run_all(out_dir, query, blastdb, num_iterations, num_threads, template,
flann_x, flann_y, flann_index, num_neighbors, score_out_name,
score_matrix, open_penalty, extend_penalty, alignment_out_name):
o_path = Path(out_dir).expanduser().absolute()
o_path.mkdir(exist_ok=True, parents=True)
q_path = Path(query).expanduser().absolute()
NcbipsiblastCommandline(query=q_path,
db=blastdb,
num_iterations=num_iterations,
num_threads=num_threads,
out_ascii_pssm=o_path / (q_path.stem + '.mtx'),
save_pssm_after_last_round=True)()
t_path = Path(template).expanduser().absolute()
NcbipsiblastCommandline(query=t_path,
db=blastdb,
num_iterations=num_iterations,
num_threads=num_threads,
out_ascii_pssm=o_path / (t_path.stem + '.mtx'),
save_pssm_after_last_round=True)()
machina.predict.predict_scores(o_path / (q_path.stem + '.mtx'),
o_path / (t_path.stem + '.mtx'),
flann_x=Path(flann_x),
flann_y=Path(flann_y),
flann_index=Path(flann_index),
num_neighbors=num_neighbors,
out_dir=o_path,
out_name=Path(score_out_name))
machina.generate_alignment.alignment_local_and_save(
o_path / score_matrix, o_path / (q_path.stem + '.mtx'),
o_path / (t_path.stem + '.mtx'), -open_penalty, -extend_penalty,
o_path, Path(alignment_out_name))
def generate_pairwise_alignment(self, query_id: str, target_id: str,
out_dir: str, pssm_dir: str):
Path(f'{out_dir}/{query_id}').mkdir(parents=True, exist_ok=True)
SeqIO.write(
SeqRecord(_pp(f'{pssm_dir}/{query_id[2:4]}/{query_id}.mtx'),
id=query_id), 'query.fasta', 'fasta')
SeqIO.write(
SeqRecord(_pp(f'{pssm_dir}/{target_id[2:4]}/{target_id}.mtx'),
id=target_id), 'subject.fasta', 'fasta')
if self.algo == 'psiblast':
if not Path(
f'{pssm_dir}/{query_id[2:4]}/{query_id}.pssm').exists():
NcbipsiblastCommandline(
db=f'{self.blast_db_dir}/uniref90',
num_iterations=3,
out_pssm=f'{pssm_dir}/{query_id[2:4]}/{query_id}.pssm',
query='query.fasta',
save_pssm_after_last_round=True,
num_threads=os.cpu_count())()
NcbipsiblastCommandline(
in_pssm=f'{pssm_dir}/{query_id[2:4]}/{query_id}.pssm',
evalue=99999,
subject='subject.fasta',
outfmt=5,
out=f'{out_dir}/{query_id}/{target_id}.xml')()
elif self.algo == 'deltablast':
NcbideltablastCommandline(
subject='subject.fasta',
rpsdb=f'{self.blast_db_dir}/cdd_delta',
evalue=99999,
outfmt=5,
out=f'{out_dir}/{query_id}/{target_id}.xml',
query='query.fasta')()
Path('query.fasta').unlink()
Path('subject.fasta').unlink()
def create_profile(query, template, blastdb, num_iterations, cmd, out_dir):
o_path = Path(out_dir).expanduser().absolute()
o_path.mkdir(exist_ok=True, parents=True)
q_path = Path(query).expanduser().absolute()
NcbipsiblastCommandline(query=q_path,
db=blastdb,
num_iterations=num_iterations,
out_ascii_pssm=o_path / (q_path.stem + '.mtx'),
save_pssm_after_last_round=True)()
q_path = Path(template).expanduser().absolute()
NcbipsiblastCommandline(query=q_path,
db=blastdb,
num_iterations=num_iterations,
out_ascii_pssm=o_path / (q_path.stem + '.mtx'),
save_pssm_after_last_round=True)()
def do_psiblast(dirpath, rec):
"""
Run a PSIBLAST query on the given sequence.
"""
# save the query to a fasta file
query_basepath = Path(dirpath, rec.id)
SeqIO.write(rec, str(query_basepath) + ".fasta", "fasta")
# build the query
query = str(query_basepath) + ".fasta"
db = "cpdb2_db"
evalue = 0.001
outfmt = 5
out = str(query_basepath) + "_blast.xml"
num_threads = 6
num_iterations = 3
# out_pssm=str(query_basepath)+"_blast.pssm"
# out_ascii_pssm=str(query_basepath)+"_blast.ascii_pssm"
# save_pssm_after_last_round=True
try:
psib_cline = NcbipsiblastCommandline(query=query,
db=db,
evalue=evalue,
outfmt=outfmt,
out=out,
num_threads=num_threads,
num_iterations=num_iterations)
#print(psib_cline)
stdout, stderr = psib_cline()
except:
print("Failed to run PSIBLAST on record %s" % rec.id)
return -1
return 1
def process_input_user(seq_file, dir):
processed_num = 0
if not os.path.exists(dir):
os.mkdir(dir)
index = 0
for seq_record in list(SeqIO.parse(seq_file, "fasta")):
processed_num += 1
print("in loop, processing" + str(processed_num) + "\n")
pssmfile = dir + str(index) + "_pssm.txt"
inputfile = dir + 'tempseq.fasta'
seql = len(seq_record)
if not os.path.exists(pssmfile):
if os.path.exists(inputfile):
os.remove(inputfile)
SeqIO.write(seq_record, inputfile, 'fasta')
try:
psiblast_cline = NcbipsiblastCommandline(
query=inputfile,
db='./db/swissprot/swissprot',
num_iterations=3,
evalue=0.001,
out_ascii_pssm=pssmfile,
num_threads=4)
stdout, stderr = psiblast_cline()
except:
print("invalid protein: " + seq_record)
index = index + 1
def run_psiblast(path, database, in_pssm, out_xml):
psiblast_cline = NcbipsiblastCommandline(db=f'{path}/{database}.db',
in_pssm=f'{path}/{in_pssm}',
out=f'{path}/{out_xml}',
max_target_seqs=10000,
outfmt=5)
psiblast_cline()
def psi_blaster(in_file, out_file):
"""Perform a PSI-BLAST via the refseq-protein.00
database available locally and then get XML output.
Args:
in_file [str]: Path to the input file for BLAST search.
Returns:
out_psi.xml [file]: Tell the user operation is done
"""
in_file = str(in_file)
print("Initiating PSI-BLAST...")
cline = NcbipsiblastCommandline(query=in_file,
outfmt=5,
db="refseq_protein.00",
num_iterations=3,
out=out_file)
cmd = str(cline)
cmd = cmd.split(" ")
return subprocess.run(cmd, check=True)
def gen_pssm(fname, pssm_dir):
#fname = './data/secreted_pos_30_train_0'
seq_records = list(SeqIO.parse(fname, 'fasta'))
i = 0
for seq_record in seq_records:
inputfile = 'input.fasta'
if not os.path.exists(pssm_dir):
os.mkdir(pssm_dir)
pssmfile = pssm_dir + "/" + seq_record.id + "_pssm.txt"
# psi-blast output file
if not os.path.exists(pssmfile):
# psi-blast input file
if os.path.exists(inputfile):
os.remove(inputfile)
SeqIO.write(seq_record, inputfile, 'fasta')
# psi-blast
psiblast_cline = NcbipsiblastCommandline(query=inputfile,
db='Swissprot',
evalue=0.001,
num_iterations=3,
out_ascii_pssm=pssmfile)
stdout, stderr = psiblast_cline()
i = i + 1
print("{:.2f}% ====> {}/{} finished".format(
i / len(seq_records) * 100, i, len(seq_records)))
def psiblast(query, db, out_dir, out_file):
out_file = os.path.join(out_dir, "{}.pssm".format(out_file))
psi_cline = NcbipsiblastCommandline('psiblast', db = db,
query = query, evalue = 0.001,
num_iterations = 3,
save_pssm_after_last_round=True,
out_ascii_pssm = out_file )
psi_cline()
return out_file
def pssm_gen_ncbi(database, input_fasta, out_pssm, num_iter, num_thr):
from Bio.Blast.Applications import NcbipsiblastCommandline
psi_cline = NcbipsiblastCommandline('psiblast',
db=database,
query=inp_fasta,
num_threads=num_thr,
num_iterations=num_iter,
outfmt=5,
out_ascii_pssm=out_pssm)
psi_cline()
return
def getPSSMFiles(fastafile, outfileprefix='', dbName='swissprot'):
inputfile = 'input.fasta'
for seq_record in SeqIO.parse(fastafile, 'fasta'):
print('\r{} '.format(seq_record.id), end="")
# psi-blast input file
if os.path.exists(inputfile):
os.remove(inputfile)
SeqIO.write(seq_record, inputfile, 'fasta')
# psi-blast output file
pssmfile = "".join((outfileprefix, seq_record.id, '.txt'))
if os.path.exists(pssmfile):
os.remove(pssmfile)
# psi-blast
psiblast_cline = NcbipsiblastCommandline(query=inputfile,
db=dbName,
evalue=0.001,
num_iterations=3,
out_ascii_pssm=pssmfile)
stdout, stderr = psiblast_cline()
# If seq_record does not have pssm, generating it by blosum62 Matrix
if not os.path.exists(pssmfile):
print('\r{} does not have pssm'.format(seq_record.id))
with open(pssmfile, 'w') as pw:
pw.writelines(" \n")
pw.writelines(
"last position-specific scoring matrix computed, weighted \n"
)
pw.writelines(alphabet + '\n')
s = seq_record.seq
k = 1
for aa in s:
line = str(k) + ' ' + aa + ' '
k += 1
idx = alphabet.find(aa)
col = 0
for a in alphabet:
line = line + str(blosumMatrix[idx][col]) + ' '
col += 1
line = line + '\n'
pw.writelines(line)
def upSampling_pssm(num_upsamp, upsamp_file, fpos, vrate, pssmdir):
seq_records = list(SeqIO.parse(fpos, 'fasta'))
N = len(seq_records)
proteins = []
i = 0
inputfile = 'input.fasta'
if not os.path.exists(pssmdir):
os.mkdir(pssmdir)
while True:
k = np.random.randint(0, N)
seq_record = seq_records[k]
pssmfile = os.path.join(pssmdir, seq_record.id + "_pssm.txt")
# psi-blast output file
if not os.path.exists(pssmfile):
# psi-blast input file
if os.path.exists(inputfile):
os.remove(inputfile)
SeqIO.write(seq_record, inputfile, 'fasta')
# psi-blast
psiblast_cline = NcbipsiblastCommandline(query=inputfile,
db='Swissprot',
evalue=0.001,
num_iterations=3,
out_ascii_pssm=pssmfile)
stdout, stderr = psiblast_cline()
# If psi-blast didn't constructe pssm
if not os.path.exists(pssmfile):
continue
pssm = readPSSM(pssmfile)
prot = genSeq(str(seq_record.seq), pssm, vrate)
fake_seq = Seq(prot, IUPAC.ExtendedIUPACProtein)
fake_record = SeqRecord(fake_seq, id='fake' + str(i))
proteins.append(fake_record)
i = i + 1
print("{:.2f}% ====> {}/{} finished".format(i / num_upsamp * 100, i,
num_upsamp))
if i == num_upsamp:
break
SeqIO.write(proteins, upsamp_file, 'fasta')
def _psi_blast_pssm(input):
file, path, blast_db_path, evalue, num_iterations, sigmoid, tmp_fastas_path ,tmp_pssms_path = input[0], input[1], input[2], input[3], input[4], input[5], input[6], input[7]
# create and call command
blastx_cline = NcbipsiblastCommandline(
cmd='psiblast',
query=path+tmp_fastas_path+file,
db=blast_db_path,
evalue=evalue,
num_iterations=num_iterations,
outfmt=5,
num_threads=3,
out_ascii_pssm=path+tmp_pssms_path+file.split('.')[0])()
if os.path.isfile(path+tmp_pssms_path+file.split('.')[0]):
pssm = _parse_pssm_ascii(path+tmp_pssms_path+file.split('.')[0])
else:
# TODO: what to do in this case?! e.g. no sequences returned by psiblast due to too low evalue
return None
if sigmoid:
return _sigmoid(pssm)
else:
return pssm
def generate_sequence_profiles(structural_alignment_path,
pssm_dir,
blastdb='uniref90'):
results = []
seq_index = SeqIO.index(structural_alignment_path, 'fasta')
for record_id in seq_index:
results.append(record_id.split('&')[0])
results.append(record_id.split('&')[1])
for domain in tqdm(set(results)):
if Path(f'{pssm_dir}/{domain[2:4]}/{domain}.mtx').exists():
continue
Path(f'{pssm_dir}/{domain[2:4]}').mkdir(parents=True, exist_ok=True)
seq_record = seq_index[[
_ for _ in seq_index if _.startswith(f'{domain}&')
][0]]
NcbipsiblastCommandline(
db=blastdb,
num_threads=os.cpu_count(),
num_iterations=3,
out_ascii_pssm=Path(
f'{pssm_dir}/{domain[2:4]}/{domain}.mtx').as_posix(),
save_pssm_after_last_round=True)(stdin=str(seq_record.seq))
def BLAST_submission(task):
"""Run a single PSI-BLAST search
Parameters
----------
task : array
List/array which contains the query data and the parameters for the BLAST search
Saved Files and Figures
-----------------------
PROTEIN_NAME.blast_result.xml : xml file containing PSI-BLAST hits
(e.g. A2A2V5.blast_result.xml)
PROTIEN_NAME.blast_result.pssm : pssm file containing PSI-BLAST similiarity scoring matrix
(e.g. A2A2V5.blast_result.pssm)
"""
#Obtain query and parameters
protein_name, query, output_hit_file, output_pssm_file, evalue, hitsize, overwrite_results, database,\
remote_tag, compress_results = task
#IF overwrite existing results is FALSE
if not overwrite_results:
#check if compressed file exist and is not empty: IF yes return and don't execute PSI-BLAST
if os.path.exists(output_hit_file +
".gz") and os.path.getsize(output_hit_file +
".gz") > 0:
#IF compress results is TURE -> return and don't execute PSI-BLAST
if compress_results:
#logging.warning("Skipped PSI-BLAST search for protein:" + "\t" + protein_name)
return
#ELSE decompress file, return and don't execute PSI-BLAST
else:
with gzip.open(output_hit_file + ".gz",
'rb') as blast_result_in, open(
output_hit_file, 'wb') as blast_result_out:
blast_result_out.write(blast_result_in.read())
os.remove(output_hit_file + ".gz")
with gzip.open(output_pssm_file + ".gz",
'rb') as blast_result_in, open(
output_pssm_file, 'wb') as blast_result_out:
blast_result_out.write(blast_result_in.read())
os.remove(output_pssm_file + ".gz")
#logging.warning("Skipped PSI-BLAST search for protein:" + "\t" + protein_name)
return
#check if non compressed file exist and is not empty:
if os.path.exists(
output_hit_file) and os.path.getsize(output_hit_file) > 0:
#IF compress results is TURE -> compress the existing result file, return and don't execute PSI-BLAST
if compress_results:
with open(output_hit_file, "rb") as blast_result_in, gzip.open(
output_hit_file + ".gz", 'wb') as blast_result_out:
blast_result_out.writelines(blast_result_in)
os.remove(output_hit_file)
with open(output_pssm_file,
"rb") as blast_result_in, gzip.open(
output_pssm_file + ".gz",
'wb') as blast_result_out:
blast_result_out.writelines(blast_result_in)
os.remove(output_pssm_file)
#logging.warning("Skipped PSI-BLAST search for protein:" + "\t" + protein_name)
return
#ELSE: return and don't execute PSI-BLAST
else:
#logging.warning("Skipped PSI-BLAST search for protein:" + "\t" + protein_name)
return
#ELSE remove existing results
else:
#remove raw results
if os.path.exists(output_hit_file):
os.remove(output_file)
if os.path.exists(output_pssm_file):
os.remove(output_pssm_file)
#remove compressed results
if os.path.exists(output_hit_file + ".gz"):
os.remove(output_hit_file + ".gz")
if os.path.exists(output_pssm_file + ".gz"):
os.remove(output_pssm_file + ".gz")
#Run PSI-BLAST search
#logging.info("Run PSI-BLAST search for protein:" + "\t" + protein_name)
#TODO: Add -out parameter for the hits and out_ascii_pssm for the pssm file
#TODO: Both compressed if required
psiblast_cline = NcbipsiblastCommandline('psiblast',
db=database,
evalue=evalue,
max_target_seqs=hitsize,
outfmt=5,
out=output_hit_file,
out_ascii_pssm=output_pssm_file,
remote=remote_tag,
inclusion_ethresh=evalue,
num_iterations=3,
use_sw_tback=True,
seg="no")
out, err = psiblast_cline(stdin=query)
#Exception handling of BLAST execution
if out or err:
logging.warning(out)
logging.warning(err)
return
#IF compress results is TURE -> compress the blast xml result file
if compress_results:
if os.path.exists(output_hit_file):
with open(output_hit_file, "rb") as blast_result_in, gzip.open(
output_hit_file + ".gz", 'wb') as blast_result_out:
blast_result_out.writelines(blast_result_in)
os.remove(output_hit_file)
if os.path.exists(output_pssm_file):
with open(output_pssm_file, "rb") as blast_result_in, gzip.open(
output_pssm_file + ".gz", 'wb') as blast_result_out:
blast_result_out.writelines(blast_result_in)
os.remove(output_pssm_file)
selected.append(splited[0])
AC_BIN_dict = {}
for line in inf:
line = line.strip('\n')
splited = line.split('\t')
AC_BIN_dict[splited[0]] = splited[1]
outf = open(outf_name, "w")
os.chdir(sequences_dir)
print "Current directory changed into " + sequences_dir + "!"
percent_count = 0
for item in selected:
outf.write(item + "\t")
# cline = NcbipsiblastCommandline(query = item + ".fasta", db = "swissprot", max_hsps_per_subject = 1, num_threads = threads_count, outfmt = 0, out = "tmp.log")
cline = NcbipsiblastCommandline(query=item + ".fasta",
db="swissprot",
num_threads=threads_count,
outfmt=0,
out=item + ".log")
if percent_count % (len(selected) / 100) == 0:
print str(percent_count / (len(selected) / 100)) + "% finished!"
if percent_count == 0:
print "Time elasped: Unknown"
print "Time remaining: Unknown"
else:
m, s = divmod(time.time() - start_time, 60)
h, m = divmod(m, 60)
print "Time elasped: %d:%02d:%02d" % (h, m, s)
del h, m, s
m, s = divmod(
(float(len(selected)) / percent_count) *
(time.time() - start_time) - (time.time() - start_time), 60)
def get_pssm(self,
fasta_dir='fasta/',
outdir='pssm_raw/',
num_iterations=3,
run=True):
"""Compute the PSSM files
Args:
fasta_dir (str, optional): irectory where the fasta queries are stored
blast (str, optional): path to the psiblast executable
db (str, optional): path to the blast database
outdir (str, optional): output directory where to store the pssm files
num_iterations (int, optional): number of iterations for the blast calculations
"""
fasta_dir = os.path.join(self.caseID, fasta_dir)
outdir = os.path.join(self.caseID, outdir)
if not os.path.isdir(outdir):
os.mkdir(outdir)
out_fmt = '7 qseqid qgi qacc qaccver qlen sseqid sallseqid sgi sallgi ,\
sacc saccver sallacc slen qstart qend sstart send qseq sseq ,\
evalue bitscore score length pident nident mismatch positive ,\
gapopen gaps ppos frames qframe sframe btop staxids stitle ,\
salltitles sstrand qcovs qcovhsp qcovus'
for q in os.listdir(fasta_dir):
# get the fasta quey
query = os.path.join(fasta_dir, q)
name = os.path.splitext(os.path.basename(query))[0]
# set up the output names
out_ascii_pssm = os.path.join(outdir, name + '.pssm')
out_pssm = os.path.join(outdir, name + '.cptpssm')
out_xml = os.path.join(outdir, name + '.xml')
# get the parameters
blast_param = self._get_psiblast_parameters(query)
# set up the psiblast calculation
psi_cline = NcbipsiblastCommandline(
self.blast,
db=self.db,
query=query,
evalue=0.0001,
word_size=blast_param['wordSize'],
gapopen=blast_param['gapOpen'],
gapextend=blast_param['gapExtend'],
matrix=blast_param['scoringMatrix'],
outfmt=7, #out_fmt,
comp_based_stats='T',
max_target_seqs=2000,
save_each_pssm=True,
num_iterations=num_iterations,
save_pssm_after_last_round=True,
out_ascii_pssm=out_ascii_pssm,
out_pssm=out_pssm,
out=out_xml)
# check that it's correct
psi_cline._validate()
if run:
# run the blast query
psi_cline()
# copyt the final pssm to its final name
shutil.copy2(out_ascii_pssm + '.%d' % num_iterations,
out_ascii_pssm)
# remove all the other files
for filename in glob.glob(out_pssm + '.*'):
os.remove(filename)
for filename in glob.glob(out_ascii_pssm + '.*'):
os.remove(filename)
os.remove(out_xml)
from Bio.Blast.Applications import NcbipsiblastCommandline
import re
feature_num = 20
threads_count = 4
input_filename = "input.fasta"
output_filename = "test_features.npy"
inf_name = "../AC_BIN.dat"
inf = open(inf_name, "r")
AC_BIN_dict = {}
for line in inf:
line = line.strip('\n')
splited = line.split('\t')
AC_BIN_dict[splited[0]] = splited[1]
cline = NcbipsiblastCommandline(query=input_filename,
db="swissprot",
num_threads=threads_count,
outfmt=0,
out="temp.log")
stdout, stderr = cline()
blastf = open("temp.log", "r")
outf = open(output_filename, "w")
AC = ""
expect = ""
count = 0
for line in blastf:
line = line.strip("\n")
matched = re.match(
r'^sp\|(\w+)\.\w+\|\w+?\s+RecName:\s+.+?\s+[0-9]+(\.[0-9])?\s+([0-9]+.[0-9]+|[0-9]+e-[0-9]+)\s*$',
line)
if matched:
if count < feature_num:
def searchPSIBLAST(self, id, psiblast):
"Psi-Blast over a local database or over the internet"
if psiblast == "local":
threads = LP(self.parameterfile, "psiblast_threading")
evalue = LP(self.parameterfile, "psiblast_evalue")
reference_protein = "refseq_protein"
in_sequence = self.dirname + id + ".fa"
output = self.dirname + id + ".xml"
if threads == False:
psiblast = NcbipsiblastCommandline(query=in_sequence,
db=reference_protein,
outfmt=5,
threshold=evalue,
out=output)
psiblast()
else:
try:
threads = int(threads)
psiblast = NcbipsiblastCommandline(query=in_sequence,
db=reference_protein,
outfmt=5,
threshold=evalue,
out=output,
num_threads=threads)
psiblast()
except:
psiblast = NcbipsiblastCommandline(query=in_sequence,
db=reference_protein,
outfmt=5,
threshold=evalue,
out=output)
psiblast()
try:
open(self.dirname + id + ".fasta")
open.close()
remove(self.dirname + id + ".fa")
except:
move(self.dirname + id + ".fa", self.dirname + id + ".fasta")
else:
evalue = LP(self.parameterfile, "psiblast_evalue")
reference_protein = "refseq_protein"
in_sequence = self.dirname + id + ".fa"
for seq_record in SeqIO.parse(in_sequence,
"fasta", IUPAC.protein):
sequence = seq_record.seq
psiblast = NCBIWWW.qblast("blastp",
reference_protein,
sequence,
service="psi",
expect=evalue,
hitlist_size=500)
psiblast
try:
open(self.dirname + id + ".fasta")
open.close()
remove(self.dirname + id + ".fa")
except:
move(self.dirname + id + ".fa", self.dirname + id + ".fasta")
output = self.dirname + id + ".xml"
saveblast = open(output, "w")
saveblast.write(psiblast.read())
saveblast.close()
psiblast.close()
import pandas as pd
import sys
#~~~~~~~~~~~~~~~~~~~~~~~~~~
# DEFINING SOME VARIABLES:
#~~~~~~~~~~~~~~~~~~~~~~~~~~
query = sys.argv[1]
pssm = query + '-pssm.asn1'
output = 'psiblast-' + query + '.tsv'
out_format = " '6 qaccver saccver bitscore evalue qlen slen length qcovs pident staxids sscinames' " # blastp output cols
out_columns = out_format[
4:-2] # gets only the output format specifiers in out_format
database = '~/datalocal/nr_db/nr'
e_cut = 1e-3
#~~~~~~~~~~~~~~~~~~~~~
# RUN 1 BLASTP ROUND:
#~~~~~~~~~~~~~~~~~~~~~
print('about to run')
# Run BLASTP in command line, according to above settings \
psiblast_cline = NcbipsiblastCommandline(in_pssm=pssm,
num_iterations='1',
out=output,
outfmt=out_format,
db=database,
evalue=e_cut)
print(psiblast_cline)
print('psiblast done')
import os, sys, glob, subprocess, shutil
from os.path import dirname, abspath, isdir
import pandas as pd
from Bio.Blast.Applications import NcbipsiblastCommandline
from Bio import SeqIO
#~~~~~~~~~~~~~~~~~~~~~~~~~~
# DEFINE NAMES OF FILES :
#~~~~~~~~~~~~~~~~~~~~~~~~~~
query = sys.argv[1] # what the query sequence was ('PflA' or 'PflB')
parentdir = dirname(dirname(abspath(__file__)))
query_fa = parentdir + '/1_bidirectional-best-hits/Cj' + query + '.faa'
blastdb = query + '-db/' + query + '-hits'
pssm = query + '-pssm.asn1'
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# CONVERT TO PSSM BY A SINGLE PSI-BLAST ITERATION:
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
psiblast_cline = NcbipsiblastCommandline(
query=query_fa, db=blastdb, num_iterations='2', out_pssm=pssm
) # do one iteration of psiblast, take the original query (CjPflA or CjPflB) as the 'subject', and save the pssm it creates
stdout, stderr = psiblast_cline(
) # make the PSSM with the original query and dummy database
def blast(algo):
seqindex = SeqIO.index('data/astral-scopdom-seqres-gd-sel-gs-bib-40-1.75.fa',
'fasta', key_function=lambda x: x.split()[0])
hie = pickle.load(Path('data/train/scop40_1fold_hie.pkl').open('rb'))
scop = Scop(dir_path='data/scop', version='1.75')
tmpdir = Path(f'.{algo}')
tmpdir.mkdir(exist_ok=True)
auc_result = {}
for sf in tqdm(hie):
px_list = hie[sf]
if len(px_list) < 1:
continue
sid = random.sample(px_list, 1)[0]
record = seqindex[sid]
f_fasta = tmpdir/f'{sid}.fasta'
f_xml = tmpdir/f'{sid}.xml'
SeqIO.write(record, f_fasta.as_posix(), 'fasta')
try:
if algo == 'psiblast':
NcbipsiblastCommandline(query=f_fasta.as_posix(),
db='astral-scopdom-seqres-gd-sel-gs-bib-40-1.75',
num_threads=int(os.cpu_count()),
num_iterations=3,
evalue=999999,
outfmt=5,
out=f_xml.as_posix())()
elif algo == 'deltablast':
NcbideltablastCommandline(query=f_fasta.as_posix(),
db='astral-scopdom-seqres-gd-sel-gs-bib-40-1.75',
num_threads=int(os.cpu_count()),
num_iterations=3,
evalue=999999,
outfmt=5,
out=f_xml.as_posix())()
else:
raise ValueError(f'Invalid algorithm ({algo})')
except ApplicationError as e:
logging.error(e)
f_xml.unlink()
continue
finally:
f_fasta.unlink()
results = SearchIO.parse(f_xml.as_posix(), 'blast-xml')
results = list(results)[-1]
results = list(results)[:500]
sf_sccs = scop.getNodeBySunid(sf).sccs
roc_score = []
roc_label = []
for result in results:
result_sf_sccs = result.description.split(' ')[0][:-2]
roc_score.append(-result.hsps[0].evalue)
if result_sf_sccs == sf_sccs:
roc_label.append(1)
else:
roc_label.append(0)
if np.all(np.array(roc_label) == 1):
auc = 1.0
elif np.all(np.array(roc_label) == 0):
auc = 0.0
else:
auc = metrics.roc_auc_score(roc_label, roc_score)
auc_result[sf_sccs] = {'auc': auc, 'sample': sid, 'num': len(results)}
f_xml.unlink()
now = int(time.time())
pickle.dump(auc_result, Path(f'auc_result_{algo}_{now}.pkl').open('wb'))
def searchPSIBLAST(self, id, psiblast):
"Psi-Blast over a local database or over the internet"
if psiblast == "local":
# edit psiblast_evalue at Parameters.py
threads = psiblast_threading
evalue = psiblast_evalue
reference_protein = "refseq_protein"
in_sequence = "./Data/" + id + ".fa"
output = "./Data/"+ id + ".xml"
if threads == False:
psiblast = NcbipsiblastCommandline(query=in_sequence,
db=reference_protein,
outfmt=5,
threshold=evalue,
out=output)
psiblast()
else:
try:
threads = int(threads)
psiblast = NcbipsiblastCommandline(query=in_sequence,
db=reference_protein,
outfmt=5,
threshold=evalue,
out=output,
num_threads=threads)
psiblast()
except:
psiblast = NcbipsiblastCommandline(query=in_sequence,
db=reference_protein,
outfmt=5,
threshold=evalue,
out=output)
psiblast()
try:
open("./Data/" + id + ".fasta")
open.close()
remove("./Data/" + id + ".fa")
except:
move("./Data/" + id + ".fa", "./Data/" + id + ".fasta")
else:
# edit psiblast_evalue at Parameters.py
evalue = psiblast_evalue
reference_protein = "refseq_protein"
in_sequence = "./Data/" + id + ".fa"
for seq_record in SeqIO.parse(in_sequence,
"fasta",IUPAC.protein):
sequence = seq_record.seq
psiblast = NCBIWWW.qblast("blastp",
reference_protein,
sequence,
service="psi",
expect=evalue,
hitlist_size=500)
psiblast
try:
open("./Data/" + id + ".fasta")
open.close()
remove("./Data/" + id + ".fa")
except:
move("./Data/" + id + ".fa", "./Data/" + id + ".fasta")
output = "./Data/"+ id + ".xml"
saveblast = open(output, "w")
saveblast.write(psiblast.read())
saveblast.close()
psiblast.close()
def generate_profiles(in_dataframe, out_path):
"""Rather complicated and quite honetly ugly looking function used
for generating the profiles from a given set of sequences. Intended to be used internally.
"""
out_path = Path(out_path)
dataset = in_dataframe
s = Sultan()
print('Unpacking and generating Uniprot DB.')
s.gunzip('-fk ../data/swiss-prot/uniprot_sprot.fasta.gz').run()
cmd = NcbimakeblastdbCommandline(
input_file='../data/swiss-prot/uniprot_sprot.fasta', dbtype='prot')
cmd()
if not (out_path / 'profile').exists():
s.mkdir(out_path / 'profile').run()
with TemporaryDirectory() as psi_temp:
for _, sample in tqdm(dataset.iterrows(),
total=len(dataset),
desc='Generating profiles'):
with NamedTemporaryFile(mode='w') as blast_in:
if isinstance(sample.name, tuple):
sample_id, chain = sample.name[0], sample.name[1]
out_name = f'{sample_id}_{chain}'
dump_path = out_path / 'full_test_summary.joblib'
else:
sample_id = sample.name
out_name = sample_id
dump_path = out_path / 'jpred_summary.joblib'
sequence, structure = sample[['Sequence', 'Structure']]
structure = ' ' + structure
print(f'>{out_name}', file=blast_in)
print(sequence, file=blast_in)
blast_in.seek(0)
cmd = NcbipsiblastCommandline(
query=blast_in.name,
db='../data/swiss-prot/uniprot_sprot.fasta',
evalue=0.01,
num_iterations=3,
out_ascii_pssm=f'{psi_temp}/{out_name}.pssm',
num_descriptions=10000,
num_alignments=10000,
# out=f'{psi_temp}{out_name}.alns.blast',
num_threads=8)
cmd()
if not os.path.exists(
os.path.join(psi_temp, out_name + '.pssm')):
tqdm.write(
f'Unable to generate profile for {out_name}. No hits in the database.'
)
dataset.drop(index=sample.name, inplace=True)
continue
with open(f'{psi_temp}/{out_name}.pssm', 'r') as pssm_file:
pssm_file.readline()
pssm_file.readline()
profile = []
offset = False
position = 0
for line in pssm_file:
line = line.rstrip()
if not line:
break
line = line.split()
line.append(structure[position])
position += 1
if not offset:
for i in range(2):
line.insert(0, '')
offset = True
profile.append(line)
profile = pd.DataFrame(profile)
profile.drop(
(profile.columns[col] for col in range(2, 22)),
axis=1,
inplace=True)
profile.drop((profile.columns[-3:-1]),
axis=1,
inplace=True)
profile.drop((profile.columns[0]), axis=1, inplace=True)
profile.columns = profile.iloc[0]
profile = profile[1:]
profile.rename(columns={profile.columns[0]: "Sequence"},
inplace=True)
profile.rename(columns={profile.columns[-1]: "Structure"},
inplace=True)
profile = profile[
['Structure'] +
[col for col in profile.columns if col != 'Structure']]
profile.loc[:, 'A':'V'] = profile.loc[:, 'A':'V'].astype(
float).divide(100)
profile.to_csv(out_path / 'profile' /
(out_name + '.profile'),
sep='\t',
index=False)
print(
f'Dumping clean test to {dump_path}. Profiles are generated in {out_path}/profile'
)
dump(dataset, dump_path)
def blast(query, algo, iter, key_name, roc_top, result_dict_cover,
blast_ranking):
tmpdir = Path(f'.{algo}_iter{iter}')
tmpdir.mkdir(exist_ok=True)
f_fasta = Path(f'data/test/{query}.fasta')
f_xml = tmpdir / f'{query}.xml'
if not f_xml.exists():
if algo == 'psiblast':
NcbipsiblastCommandline(
query=f_fasta.as_posix(),
db=
'data/blastdb/astral-scopdom-seqres-gd-sel-gs-bib-40-1.75_train_only',
num_threads=int(os.cpu_count()),
num_iterations=iter,
max_target_seqs=500,
evalue=999999,
outfmt=5,
out=f_xml.as_posix())()
elif algo == 'deltablast':
NcbideltablastCommandline(
query=f_fasta.as_posix(),
db=
'data/blastdb/astral-scopdom-seqres-gd-sel-gs-bib-40-1.75_train_only',
rpsdb='data/blastdb/cdd_delta',
num_threads=int(os.cpu_count()),
num_iterations=iter,
max_target_seqs=500,
evalue=999999,
outfmt=5,
out=f_xml.as_posix())()
elif algo == 'psiblast_pssm':
f_pssm = Path(f'data/test/{query}.pssm')
NcbipsiblastCommandline(
in_pssm=f_pssm.as_posix(),
db=
'data/blastdb/astral-scopdom-seqres-gd-sel-gs-bib-40-1.75_train_only',
num_threads=int(os.cpu_count()),
num_iterations=iter,
max_target_seqs=500,
evalue=999999,
outfmt=5,
out=f_xml.as_posix())()
else:
raise ValueError(f'Invalid algorithm ({algo})')
results = SearchIO.parse(f_xml.as_posix(), 'blast-xml')
results = list(results)[-1] # final iteration
results = list(results)
sf_sccs = scop100.getDomainBySid(query).getAscendent('sf').sccs
all_tp_count = len(
hie[scop100.getDomainBySid(query).getAscendent('sf').sunid])
roc_score, roc_label = [], []
assert len(results) >= roc_top
blast_ranking[key_name][query] = []
for result in results:
blast_ranking[key_name][query].append(
{result.id: ('', '', result.hsps[0].evalue)})
for result in results:
result_sf_sccs = '.'.join(
result.description.split(' ')[0].split('.')[:-1])
roc_score.append(-result.hsps[0].evalue)
label = 1 if result_sf_sccs == sf_sccs else 0
roc_label.append(label)
if label == 1:
result_dict_cover[key_name].append(
len(result.hsps[0].query.seq.ungap('-')) /
len(SeqIO.read(f_fasta.as_posix(), 'fasta').seq))
if roc_label.count(0) == roc_top:
break
fpc, tpc = [0], [0]
for i in range(0, roc_top):
fpc.append(i)
tpc.append(roc_label[:i + 1].count(1))
auc = metrics.auc(fpc, tpc) / roc_top / all_tp_count
return fpc, tpc, auc
2 # -*- coding: utf-8 -*-
"""
Spyder Editor
This is a temporary script file.
"""
from Bio.Blast.Applications import NcbipsiblastCommandline
from Bio import SeqIO
import os
inputfile = 'input.fasta'
for seq_record in SeqIO.parse('PDNA-224.fasta', 'fasta'):
print('{} is calculating pssm'.format(seq_record.id))
if os.path.exists(inputfile):
os.remove(inputfile)
pssmfile = "".join(('pssm', '_', seq_record.id, '.txt'))
SeqIO.write(seq_record, inputfile, 'fasta')
psiblast_cline = NcbipsiblastCommandline(query=inputfile,
db='swissprot',
evalue=0.001,
num_iterations=3,
out_ascii_pssm=pssmfile)
stdout, stderr = psiblast_cline()
if REMOTE:
db = BLAST_CONFIG['default_db']
kwargs = {}
else:
db = config['paths']['blast']['dbs'][
BLAST_CONFIG['default_db']]
kwargs = {
'num_iterations': BLAST_CONFIG['num_iterations'],
'num_threads': BLAST_CONFIG['num_threads']
}
cline = NcbipsiblastCommandline(
cmd=os.path.join(BLAST_PATH, 'bin', 'psiblast'),
query=in_file.name,
db=db,
num_alignments=1,
out_ascii_pssm=out_file_name,
remote=REMOTE,
**kwargs)
print(cline)
stdout, stderr = cline()
# Note: It's possible that out_file_name doesn't get generated at all, if there are no hits found.
# In this case, generate an empty .pssm file so we don't try to regenerate this file the next time this
# gene/protein is encountered. We'll also have to check for 0 sized files to make this work of course.
open(out_file_name, 'a').close(
) # no-op if file exists, creates an empty file if it doesn't
# Copy all 'valid' .pssm files (with size>0) to the output folder for downstream processing.
if os.path.getsize(out_file_name) > 0:
shutil.copyfile(out_file_name,
def __generate_sequence_profiles_old():
mtx_dir_name = 'pssm_deltablast'
DB_INDEX = SeqIO.index('data/scop40_structural_alignment.fasta', 'fasta')
records = {}
for i in DB_INDEX:
domkey = i.split('&')[0]
records[domkey] = SeqRecord(DB_INDEX[i].seq.ungap('-'),
id=domkey,
name='',
description='')
with Path('data/scop40_scopdom_pdbatom_seq.fasta').open('w') as f:
SeqIO.write(records.values(), f, 'fasta')
DB_INDEX = SeqIO.index('data/scop40_scopdom_pdbatom_seq.fasta', 'fasta')
for sid in tqdm(list(DB_INDEX)):
mtx_dir = Path(f'data/{mtx_dir_name}/{sid[2:4]}')
mtx_dir.mkdir(exist_ok=True, parents=True)
mtx_file = mtx_dir / f'{sid}.mtx'
if mtx_file.exists():
logging.debug(f'PSSM already exists: {mtx_file}')
continue
try:
SeqIO.write(DB_INDEX[sid], f'{sid}.fasta', 'fasta')
NcbipsiblastCommandline(query=f'{sid}.fasta',
db='uniref90',
num_threads=int(os.cpu_count()),
num_iterations=3,
out_ascii_pssm=mtx_file.as_posix(),
save_pssm_after_last_round=True)()
except Exception as e:
logging.exception(e)
continue
finally:
if Path(f'{sid}.fasta').exists():
Path(f'{sid}.fasta').unlink()
logging.info('')
for sid in tqdm(
pickle.load(Path('data/one_domain_superfamily.pkl').open('rb'))):
mtx_dir = Path(f'data/{mtx_dir_name}/{sid[2:4]}')
mtx_dir.mkdir(exist_ok=True, parents=True)
mtx_file = mtx_dir / f'{sid}.mtx'
if mtx_file.exists():
logging.debug(f'PSSM already exists: {mtx_file}')
continue
try:
tmalign = TMalignCommandLine(f'data/scop_e/{sid[2:4]}/{sid}.ent',
f'data/scop_e/{sid[2:4]}/{sid}.ent')
tmalign.run()
assert str(tmalign.alignment[0].seq).find('-') == -1
SeqIO.write(tmalign.alignment[0], f'{sid}.fasta', 'fasta')
NcbipsiblastCommandline(query=f'{sid}.fasta',
db='uniref90',
num_threads=int(os.cpu_count()),
num_iterations=3,
out_ascii_pssm=mtx_file.as_posix(),
save_pssm_after_last_round=True)()
except Exception as e:
logging.error(f'sid={sid}')
logging.exception(e)
continue
finally:
if Path(f'{sid}.fasta').exists():
Path(f'{sid}.fasta').unlink()