def frames_by_scaffold(scaffold):
if scaffold == 'all':
return db_session.query(Backbone).all()
return db_session.query(Backbone).filter(
func.lower(Backbone.name) == scaffold.lower()
).all()
def offtarget_seed(fasta_string, with_references=False):
"""Function which calculates offtarget using seed sequence on 3'UTR database
Args:
fasta_string(str): Fasta sequence.
with_references(bool):
Returns:
Offtarget value(int).
"""
references = db_session.query(Utr.reference).filter(
Utr.sequence.like("%{}%".format(fasta_string[1:9].upper()))
)
return _count_offtarget(references, with_references=with_references)
def get_results(
transcript_name, minimum_CG, maximum_CG, maximum_offtarget,
scaffold, immunostimulatory
):
try:
stored_input = db_session.query(InputData).filter(
func.lower(InputData.transcript_name) == transcript_name.lower(),
InputData.minimum_CG == minimum_CG,
InputData.maximum_CG == maximum_CG,
InputData.maximum_offtarget == maximum_offtarget,
func.lower(InputData.scaffold) == scaffold.lower(),
func.lower(InputData.immunostimulatory) == immunostimulatory.lower()
).outerjoin(InputData.results).one()
except NoResultFound:
return None
return [result.as_json() for result in stored_input.results]
def shmir_from_sirna_score(input_str):
"""Main function takes string input and returns the best results depending
on scoring. Single result include sh-miR sequence,
score and link to 2D structure from mfold program
Args:
input_str(str): Input string contains one or two sequences.
Returns:
List of sh-miR(s) sorted by score.
"""
seq1, seq2, shift_left, shift_right = check_input(input_str)
if not seq2:
seq2 = reverse_complement(seq1)
original_frames = db_session.query(Backbone).all()
frames = get_frames(seq1, seq2,
shift_left, shift_right,
deepcopy(original_frames))
with allow_join_result():
frames_with_score = group(
fold_and_score.s(
seq1, seq2,
frame_tuple,
original,
score_from_sirna,
(seq1,)
).set(queue="subtasks")
for frame_tuple, original in zip(frames, original_frames)
).apply_async().get()
sorted_frames = [
elem[:-1] for elem in sorted(
frames_with_score, key=operator.itemgetter(0), reverse=True
) if elem[0] > 60
][:3]
return sorted_frames
def seed_initial_data():
backbones = [
Backbone(
name='miR-30a',
flanks3_s='TGCCTACTGCCTCGGACTTCAAGGGGCTACTTTAGGAGCA',
flanks3_a='TGCTCCTAAAGTAGCCCCTTGAAGTCCGAGGCAGTAGGCA',
flanks5_s='CTAAAGAAGGTATATTGCTGTTGACAGTGAGCGAC',
flanks5_a='GTCGCTCACTGTCAACAGCAATATACCTTCTTTAG',
loop_s='CTGTGAAGCCACAGATGGG',
loop_a='CCCATCTGTGGCTTCACAG',
miRNA_s='UGUAAACAUCCUCGACUGGAAG',
miRNA_a='CTTCCAGTCGAGGATGTTTGCAGC',
miRNA_length=22,
miRNA_min=19,
miRNA_max=25,
miRNA_end_5=-2,
miRNA_end_3=0,
structure='./data/structures/miR-30a',
homogeneity=4,
miRBase_link=('http://www.mirbase.org/cgi-bin/mirna_entry.pl'
'?acc=MI0000088'),
active_strand=3
),
Backbone(
name='miR-155',
flanks3_s=('GTGTATGATGCCTGTTACTAGCATTCACATGGAACAAATTGCTGCTGCCGTGGG'
'AGGATGACAAAGA'),
flanks3_a=('TCTTTGTCATCCTCCCACGGCAGCAGCAATTTGTTCCATGTGAATGCTAGTAAC'
'AGGCATCATACAC'),
flanks5_s='AGGCTTGCTGTAGGCTGTATGCTG',
flanks5_a='CAGCATACCTACAGCAAGCCT',
loop_s='TTTTGCCTCCAACTGA',
loop_a='TCAGTTGGAGGCAAAA',
miRNA_s='UUAAUGCUAAUCGUGAUAGGGGU',
miRNA_a='CUCCUACAUAUUAGCAUUAACA',
miRNA_length=23,
miRNA_min=20,
miRNA_max=26,
miRNA_end_5=-2,
miRNA_end_3=1,
structure='./data/structures/miR-155',
homogeneity=5,
miRBase_link=('http://www.mirbase.org/cgi-bin/mirna_entry.pl'
'?acc=MIMAT0000646'),
active_strand=5
),
Backbone(
name='miR-21',
flanks3_s='CTGACATTTTGGTATCTTTCATCTGACCATCCATATCCAATGTTCTCATT',
flanks3_a='AATGAGAACATTGGATATGGATGGTCAGATGAAAGATACCAAAATGTCAG',
flanks5_s='TACCATCGTGACATCTCCATGGCTGTACCACCTTGTCGGG',
flanks5_a='CCCGACAAGGTGGTACAGCCATGGAGATGTCACGATGGTA',
loop_s='CTGTTGAATCTCATGG',
loop_a='CCATGAGATTCAACAG',
miRNA_s='UAGCUUAUCAGACUGAUGUUGA',
miRNA_a='CAACACCAGUCGAUGGGCUGU',
miRNA_length=22,
miRNA_min=19,
miRNA_max=24,
miRNA_end_5=-1,
miRNA_end_3=1,
structure='./data/structures/miR-21',
homogeneity=4,
miRBase_link=('http://www.mirbase.org/cgi-bin/mirna_entry.pl'
'?acc=MI0000077'),
active_strand=5
),
Backbone(
name='miR-122',
flanks3_s='GCTACTGCTAGGCAATCCTTCCCTCGATAAATGTCTTGGCATCGTTTGCTT',
flanks3_a='AAGCAAACGATGCCAAGACATTTATCGAGGGAAGGATTGCCTAGCAGTAGC',
flanks5_s='TGGAGGTGAAGTTAACACCTTCGTGGCTACAGAGTTTCCTTAGCAGAGCTG',
flanks5_a='CAGCTCTGCTAAGGAAACTCTGTAGCCACGAAGGTGTTAACTTCACCTCCA',
loop_s='TGTCTAAACTATCA',
loop_a='TGATAGTTTAGACA',
miRNA_s='UGGAGUGUGACAAUGGUGUUUG',
miRNA_a='AACGCCAUUAUCACACUAAAUA',
miRNA_length=22,
miRNA_min=21,
miRNA_max=23,
miRNA_end_5=-2,
miRNA_end_3=2,
structure='./data/structures/miR-122',
homogeneity=5,
miRBase_link=('http://www.mirbase.org/cgi-bin/mirna_entry.pl'
'?acc=MI0000442'),
active_strand=5
),
Backbone(
name='miR-31',
flanks3_s='CTTTCCTGTCTGACAGCAGCTTGGCTACCTCCGTCCTGTTCCTCCTTGTCTT',
flanks3_a='AAGACAAGGAGGAACAGGACGGAGGTAGCCAAGCTGCTGTCAGACAGGAAAG',
flanks5_s='CATAACAACGAAGAGGGATGGTATTGCTCCTGTAACTCGGAACTGGAGAGG',
flanks5_a='CCTCTCCAGTTCCGAGTTACAGGAGCAATACCATCCCTCTTCGTTGTTATG',
loop_s='GTTGAACTGGGAACC',
loop_a='GGTTCCCAGTTCAAC',
miRNA_s='AGGCAAGAUGCUGGCAUAGCU',
miRNA_a='UGCUAUGCCAACAUAUUGCCAU',
miRNA_length=21,
miRNA_min=19,
miRNA_max=23,
miRNA_end_5=-1,
miRNA_end_3=1,
structure='./data/structures/miR-31',
homogeneity=4,
miRBase_link=('http://www.mirbase.org/cgi-bin/mirna_entry.pl'
'?acc=MI0000089'),
active_strand=5
),
Backbone(
name='miR-26a',
flanks3_s='GGGACGC',
flanks3_a='GCGTCCC',
flanks5_s='GTGGCCTCG',
flanks5_a='CGAGGCCAC',
loop_s='GTGCAGGTCCCAATGGG',
loop_a='CCCATTGGGACCTGCAC',
miRNA_s='UUCAAGUAAUCCAGGAUAGGCU',
miRNA_a='CCUAUUCUUGGUUACUUGCACG',
miRNA_length=22,
miRNA_min=21,
miRNA_max=23,
miRNA_end_5=-2,
miRNA_end_3=2,
structure='./data/structures/miR-26a',
homogeneity=4,
miRBase_link=('http://www.mirbase.org/cgi-bin/mirna_entry.pl'
'?acc=MI0000083'),
active_strand=5
),
Backbone(
name='miR-106b',
flanks3_s='TCCAGCAGG',
flanks3_a='CCTGCTGGA',
flanks5_s='CCTGCCGGGGC',
flanks5_a='GCCCCGGCAGG',
loop_s='AGTGGTCCTCTCCGTGCTA',
loop_a='TAGCACGGAGAGGACCACT',
miRNA_s='UAAAGUGCUGACAGUGCAGAU',
miRNA_a='CCGCACUGUGGGUACUUGCUGC',
miRNA_length=21,
miRNA_min=20,
miRNA_max=22,
miRNA_end_5=-3,
miRNA_end_3=2,
structure='./data/structures/miR-106b',
homogeneity=2,
miRBase_link=('http://www.mirbase.org/cgi-bin/mirna_entry.pl'
'?acc=MI0000734'),
active_strand=0
)
]
immunos = [
Immuno(
sequence='UGUGU',
receptor='TLR7 and TLR8',
link='http://www.ncbi.nlm.nih.gov/pubmed/16609928'
),
Immuno(
sequence='GUCCUUCAA',
receptor='TLR7 and TLR8',
link='http://www.ncbi.nlm.nih.gov/pubmed/15723075'
),
Immuno(
sequence='GU',
receptor='TLR7 and TLR8',
link='http://www.ncbi.nlm.nih.gov/pubmed/16609928'
),
Immuno(
sequence='AU',
receptor='TLR8',
link='http://www.ncbi.nlm.nih.gov/pubmed/18322178'
),
Immuno(
sequence='UGGC',
receptor='',
link='http://www.ncbi.nlm.nih.gov/pubmed/16682561'
),
Immuno(
sequence='UUUUU',
receptor='',
link='http://www.ncbi.nlm.nih.gov/pubmed/15778705'
)
]
if db_session.query(Utr).count() == 0:
filename = download.download_utr_database()
for sequence, reference in parse_utr_database(filename):
db_session.add(
Utr(
sequence=sequence,
reference=reference
)
)
if db_session.query(HumanmRNA).count() == 0:
filename = download.download_human_all_database()
for sequence, reference in parse_mRNA_database(filename):
db_session.add(
HumanmRNA(
sequence=sequence,
reference=reference
)
)
if db_session.query(Backbone).count() == 0:
db_session.add_all(backbones)
if db_session.query(Immuno).count() == 0:
db_session.add_all(immunos)
db_session.commit()
def blast_offtarget(fasta_string, with_references=False):
references = db_session.query(HumanmRNA.reference).filter(
HumanmRNA.sequence.like("%{}%".format(fasta_string.upper()))
)
return _count_offtarget(references, with_references=with_references)
def shmir_from_transcript_sequence(
transcript_name, minimum_CG, maximum_CG, maximum_offtarget, scaffold,
stimulatory_sequences
):
"""Generating function of shmir from transcript sequence.
Args:
transcript_name(str): Name of transcipt.
minimum_CG(int): Minimum number of 'C' and 'G' nucleotide in sequence.
maximum_CG(int): Maximum number of 'C' and 'G' nucleotide in sequence.
maximum_offtarget(int): Maximum offtarget.
scaffold(str): Name of frame of miRNA or 'all'.
stimulatory_sequences(str): One of 'yes', 'no', 'no_difference'.
Returns:
list of sh-miR(s).
"""
# check if results are in database
try:
stored_input = db_session.query(InputData).filter(
func.lower(InputData.transcript_name) == transcript_name.lower(),
InputData.minimum_CG == minimum_CG,
InputData.maximum_CG == maximum_CG,
InputData.maximum_offtarget == maximum_offtarget,
func.lower(InputData.scaffold) == scaffold.lower(),
func.lower(
InputData.stimulatory_sequences
) == stimulatory_sequences.lower()
).outerjoin(InputData.results).one()
except NoResultFound:
pass
else:
return [result.as_json() for result in stored_input.results]
# create path string
path = "_".join(
map(
str,
[transcript_name, minimum_CG, maximum_CG, maximum_offtarget,
scaffold, stimulatory_sequences]
)
)
mRNA = ncbi_api.get_mRNA(transcript_name)
if scaffold == 'all':
original_frames = db_session.query(Backbone).all()
else:
original_frames = db_session.query(Backbone).filter(
func.lower(Backbone.name) == scaffold.lower()
).all()
frames_by_name = {frame.name: frame for frame in original_frames}
patterns = {
frame.name: OrderedDict(
sorted(
json.loads(frame.regexp).items(),
reverse=True
)
) for frame in original_frames
}
best_sequences = defaultdict(list)
for name, patterns_dict in patterns.iteritems():
for regexp_type, sequences in find_by_patterns(patterns_dict, mRNA).iteritems():
with allow_join_result():
is_empty, sequences = generator_is_empty(sequences)
if not is_empty:
best_sequences[name] = remove_none(
group(
validate_and_offtarget.s(
sequence,
maximum_offtarget,
minimum_CG,
maximum_CG,
stimulatory_sequences,
int(regexp_type)
).set(queue="blast")
for sequence in sequences
).apply_async().get()
)
results = []
for name, seq_dict in unpack_dict_to_list(best_sequences):
if len(results) == 20:
break
with allow_join_result():
shmir_result = shmir_from_fasta_string.s(
seq_dict['sequence'],
[frames_by_name[name]],
seq_dict['offtarget'],
seq_dict['regexp'],
path
).set(queue="score").apply_async().get()
if shmir_result:
results.extend(shmir_result)
if not results:
best_sequences = []
sequences = all_possible_sequences(mRNA, 19, 21)
with allow_join_result():
is_empty, sequences = generator_is_empty(sequences)
if not is_empty:
best_sequences = remove_none(
group(
validate_and_offtarget.s(
sequence,
maximum_offtarget,
minimum_CG,
maximum_CG,
stimulatory_sequences,
0
).set(queue="blast")
for sequence in sequences
).apply_async().get()
)
if best_sequences:
with allow_join_result():
results = chain(*remove_none(
group(
shmir_from_fasta_string.s(
seq_dict['sequence'], original_frames,
seq_dict['offtarget'], seq_dict['regexp'], path
).set(queue="score")
for seq_dict in best_sequences
).apply_async().get()
))
sorted_results = sorted(
results,
key=operator.itemgetter(0),
reverse=True
)[:10]
db_results = [Result(
score=score,
sh_mir=shmir,
pdf=path_id,
backbone=frames_by_name[frame_name].id,
sequence=found_sequences[0],
) for score, shmir, frame_name, path_id, found_sequences in sorted_results]
remove_bad_foldings(path, (result.get_task_id() for result in db_results))
db_input = InputData(
transcript_name=transcript_name,
minimum_CG=minimum_CG,
maximum_CG=maximum_CG,
maximum_offtarget=maximum_offtarget,
scaffold=scaffold,
stimulatory_sequences=stimulatory_sequences,
results=db_results
)
db_session.add(db_input)
db_session.add_all(db_results)
db_session.commit()
return [result.as_json() for result in db_results]
