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generate_primer_pairs.py
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generate_primer_pairs.py
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'''
Created on 3/26/2020
Fast analyzer for designing primer pairs with 2D-stable structure
@author: ignacio
'''
import tempfile
from difflib import SequenceMatcher
from itertools import combinations
from itertools import product
import numpy as np
import pandas as pd
import utilities
from utilities import *
from collections import Counter
# Main script function
def run(pmin, pmax, gcmin, gcmax, tm, amplicon_min, amplicon_max, fasta_id, input_dir, output_dir,
linearfold_bin, check_others, **kwargs):
print('Reading MSA data (for mismatches mapping)')
# calculate iteratively an MSA by calling muscle
# msa_path = join(input_dir, 'lcl_mod_emb-LR757997.1 and 105 other sequences.aln')
msa_path = join(input_dir, 'covid_other_seqs_BIG.fa')
msa_full = FastaAnalyzer.get_fastas(msa_path)
msa_path_fast = join(input_dir, 'debug/covid_other_seqs_BIG_backtrail_to_gap500.fa')
debug = False
msa_dir = join(input_dir, 'msa')
if not exists(msa_dir):
mkdir(msa_dir)
msa_bkp = join(msa_dir, basename(msa_path).replace(".aln", ".fasta"))
if not exists(msa_bkp):
sequences = FastaAnalyzer.get_fastas(join(input_dir, "%s.fasta" % fasta_id))
for h, s in sequences:
next_fa = join(msa_dir, h.split(" ")[1][1:-1] + ".afa")
# print(exists(next_fa), next_fa)
if not exists(next_fa):
fa_in = join(msa_dir, h.split(" ")[1][1:-1] + ".fa")
FastaAnalyzer.write_fasta_from_sequences([[h, s]], fa_in)
muscle_cmd = 'muscle -profile -in1 %s -in2 \"%s\" -out %s' % (fa_in, msa_path_fast, next_fa)
print(muscle_cmd)
system(muscle_cmd)
# read msa info: This dictionary will be referred to during the last step
variants_by_h = {}
for f in listdir(msa_dir):
print(f)
if not f.endswith('.afa'):
continue
code = f.replace(".afa", '')
curr_position = 0
msa = FastaAnalyzer.get_fastas(join(msa_dir, f))
# print(len(msa), len(msa_full))
# the msa hits has to have the same size as the full MSA
assert len(msa[0][1]) == len(msa_full[0][1])
for si, nt in enumerate(msa_full[0][1]):
if si % 1000 == 0:
print('reading MSA variants by columns', si, 'out of', len(msa_full[0][1]))
if nt != '-':
if not code in variants_by_h:
variants_by_h[code] = {}
variants_by_h[code][curr_position] = Counter([s2[si] for h2, s2 in msa_full])
curr_position += 1
hits_by_h2 = {}
for h, s in sequences:
# print(h, len(s))
for h2, s2 in msa:
if s in s2.replace("-", ''):
# print(h, h2, 'found')
hits_by_h2[h2] = 1 if not h2 in hits_by_h2 else hits_by_h2[h2] + 1
print('Primer pairs generator + with background genome check (step 1) and RNA secondary structure check (step 2)')
sequences = FastaAnalyzer.get_fastas(join(input_dir, "%s.fasta" % fasta_id), as_dict=True)
tagseqs = FastaAnalyzer.get_fastas(kwargs.get('tags', 0))
overwrite1 = kwargs.get('overwrite1', 0)
overwrite2 = kwargs.get('overwrite2', 0)
overwrite3 = kwargs.get('overwrite3', 0)
overwrite4 = kwargs.get('overwrite4', 0)
# RULES 1-8 + VIRUSES COMPETITION
print('STEP 1')
bkp_path_df = join(output_dir, "%s.tsv.gz" % fasta_id)
if not exists(bkp_path_df) or overwrite1:
table = []
for h in sequences:
s = sequences[h]
for primer_len in range(pmin, pmax):
print('scanning %s for primers primer of len %i' % (fasta_id, primer_len))
for si in range(len(s) - primer_len + 1):
primer = s[si: si + primer_len]
for seq, direction in zip([primer, SequenceMethods.get_complementary_seq(primer)],
['+', '-']):
# GC content between gcmin and gcmax
gc = SequenceMethods.get_gc_content(seq)
rule1 = gc >= gcmin and gc <= gcmax
# GC content of first 6 nt
gc5prime = SequenceMethods.get_gc_content(seq[:6])
gc3prime = SequenceMethods.get_gc_content(seq[-6:])
# rule2
# Tm = 81.5 + 16.6(log10([Na+])) + .41*(%GC) - 600/length,
na_concentration = 0.1
primer_tm = 81.5 + 16.6*(np.log10(na_concentration)) + .41*(gc) - 600/len(seq)
rule2 = primer_tm > tm
# homopolymers of length 4
rule3 = SequenceMethods.has_homopolymer(seq, 4)
rule4 = seq[-1] == 'A'
if not rule1 or not rule2 or rule3 or not rule4:
continue
table.append([fasta_id, h, si, primer_len, seq, direction, gc, primer_tm,
rule1, rule2, rule3, rule4, gc5prime, gc3prime])
df = pd.DataFrame(table, columns=['fasta.id', 'fa.name', 'fasta.position', 'primer.len', 'seq', 'direction',
'GC', 'Tm', 'rule.1', 'rule.2', 'rule.3', 'rule.4', 'gc.5p6nt', 'gc.3p6n'])
df['k'] = df['fasta.id'] + "_" + df['fasta.position'].astype(str) + "_" + df['primer.len'].astype(str)
# check for MSA alternate variants within the primers
msa_flagged_primers = []
flag_details = []
for ri, r in df.iterrows():
flag = False
flag_details.append('')
k = r['fa.name'].split(" ")[1][1:-1]
for pi in range(r['fasta.position'], r['fasta.position'] + r['primer.len']):
n_variants = len({nt for nt in variants_by_h[k][pi].keys() if nt not in {'N'}})
if n_variants >= 2:
flag = True
flag_details[-1] += str(pi) + ":" + str(dict(variants_by_h[k][pi])) + ";"
msa_flagged_primers.append(flag)
df['msa.flagged.primers'] = msa_flagged_primers
df['msa.flagged.primers.desc'] = flag_details
df['n.msa.flagged.primers.desc'] = [len(x.split(";")) - 1 for x in df['msa.flagged.primers.desc']]
# scan whether primers intersect with other viruses
other_viruses_dir = join(input_dir, "other_viruses")
column_names_by_f = DataFrameAnalyzer.get_dict(DataFrameAnalyzer.read_tsv(join(input_dir, 'other_viruses',
'names.tsv')),
'FILENAME', 'VIRUS')
# check against all background viral genomes
if check_others:
for f in listdir(other_viruses_dir):
if f.endswith('.tsv'):
continue
print('Scanning against background viral genomes using file ...', f, column_names_by_f[f])
best_hits = []
fa = FastaAnalyzer.get_fastas(join(other_viruses_dir, f))
for ri, r in df.iterrows():
a = r['seq']
if ri % 200 == 0:
print(ri, 'primers out of', df.shape[0], 'matched against', column_names_by_f[f], a)
cmp_a = SequenceMethods.get_complementary_seq(a)
n_best_match = 0
for h, b in fa:
match_ab = SequenceMatcher(None, a, b,
autojunk=False).find_longest_match(0, len(a), 0, len(b))
match_cmpa_b = SequenceMatcher(None, cmp_a, b,
autojunk=False).find_longest_match(0, len(cmp_a), 0, len(b))
if match_ab.size == 0 and match_cmpa_b.size == 0:
print('problem with sequence matches. Please check')
assert 1 > 2
n_best_match = max(match_ab.size, match_cmpa_b.size, n_best_match)
best_hits.append(n_best_match)
df[f] = best_hits
df = df.rename(columns=column_names_by_f)
df['best.hit.others'] = df[[c for c in df if c in column_names_by_f.values()]].max(axis=1)
else:
df['best.hit.others'] = -1
print('Saving selected primers:')
DataFrameAnalyzer.to_tsv(df, join(output_dir, "%s.tsv.gz" % fasta_id))
df.to_excel(join(output_dir, "%s.xlsx" % fasta_id))
else:
print('skip STEP 1 (file exists and overwrite1=False')
df = DataFrameAnalyzer.read_tsv_gz(bkp_path_df)
best_hit_by_k = DataFrameAnalyzer.get_dict(df, 'k', 'best.hit.others')
# Analyze group primers by pairs and filter ones that are not good amplicon length min-max
# and Run LinearFold to get score estimates
print('STEP 2')
print('\nSelection of 1-2 primer pairs (amplicon length + RNA secondary structure)...')
bkp_path_df2 = join(output_dir, "%s_pairs.tsv.gz" % fasta_id)
if not exists(bkp_path_df2) or overwrite2:
iloc_by_idx = {idx: df.iloc[idx] for idx in df.index}
faname_by_idx = {idx: df.iloc[idx]['fa.name'] for idx in df.index}
direction_by_idx = {idx: df.iloc[idx]['direction'] for idx in df.index}
print('Generating primer pairs (within ORFs). This is the slowest step (please wait 3-5 in all ORFs...)')
accepted_pairs = []
for orf, grp in df.groupby('fa.name'):
ntest = kwargs.get('ntest')
print('Generating primer pairs for ORF:', orf[:100], "... # primers=%i" % grp.shape[0])
accepted_pairs += [[a, b] for a, b in combinations(grp.head(ntest if ntest is not None else grp.shape[0]).index, 2) if
(abs(iloc_by_idx[b]['fasta.position'] - iloc_by_idx[a]['fasta.position'] + 1) >= amplicon_min) and
(abs(iloc_by_idx[b]['fasta.position'] - iloc_by_idx[a]['fasta.position'] + 1) <= amplicon_max) and
(direction_by_idx[a] == '+') and (direction_by_idx[b] == '-')]
df2 = pd.DataFrame([[a, iloc_by_idx[a]['k'], b, iloc_by_idx[b]['k']] for a, b in accepted_pairs], columns=['i', 'vi', 'j', 'vj'])
for symbol in ['i', 'j']:
df2['seq.%s' % symbol] = [iloc_by_idx[r[symbol]]['seq'] for ri, r in df2.iterrows()]
df2['gc.%s' % symbol] = df2['seq.%s' % symbol].apply(SequenceMethods.get_gc_content)
df2['direction.%s' % symbol] = [direction_by_idx[r[symbol]] for ri, r in df2.iterrows()]
for tag_h, tag_s in tagseqs:
df2['%s.seq.%s' % (tag_h, symbol)] = tag_s + df2['seq.%s' % symbol]
assert sum(df2['i'].map(faname_by_idx) != df2['j'].map(faname_by_idx)) == 0
df2['cds'] = df2['i'].map(faname_by_idx)
df2['amplicon.len.idx'] = df2['vj'].str.split("_").str[-2].astype(int) - \
df2['vi'].str.split("_").str[-2].astype(int) + 1
df2 = df2[df2['amplicon.len.idx'] > 0]
# find the strongest local match between primer pairs with and without tags
names = ['seq.i', 'seq.j']
names += [((tag_h + ".") if tag_h is not None else + '') + c for tag_h, tag_s in tagseqs for c in names]
for ca, cb in product(names, repeat=2):
print(ca, cb)
longest_local_match = []
for ri, r in df2.iterrows():
if ri % 100 == 0:
print("# Scanning for local primer pair hits", ri, 'out of', df2.shape[0])
a, b = r[ca], r[cb]
cmp_b = SequenceMethods.get_complementary_seq(b)
match_a_cmpb = [a, cmp_b, SequenceMatcher(None, a, cmp_b, autojunk=False).find_longest_match(0, len(a), 0, len(b))]
longest_local_match.append(a[match_a_cmpb[-1].a: match_a_cmpb[-1].a + match_a_cmpb[-1].size] + "/" + \
SequenceMethods.get_complementary_seq(cmp_b[match_a_cmpb[-1].b: match_a_cmpb[-1].b +
match_a_cmpb[-1].size]))
df2['match.%s.%s' % (ca, cb)] = longest_local_match
df2['match.len.%s.%s' % (ca, cb)] = df2['match.%s.%s' % (ca, cb)].str.split("/").str[0].str.len()
# map the best hit with other viruses
df2['best.hit.others'] = [max(a, b) for a, b in zip(list(df2['vi'].map(best_hit_by_k)),
list(df2['vj'].map(best_hit_by_k)))]
# df2 = df2[df2['longest.local.match.len'].abs() <= 5]
df2['amplicon.fwd'] = [sequences[r['cds']][int(r['vi'].split("_")[-2]):int(r['vj'].split("_")[-2]) +
int(r['vj'].split("_")[-1])]
for ri, r in df2.iterrows()]
df2['amplicon.rev'] = df2['amplicon.fwd'].apply(SequenceMethods.get_complementary_seq)
for tag_h, tag_s in tagseqs:
df2['%s.amplicon.fwd' % tag_h] = tag_s + df2['amplicon.fwd']
df2['%s.amplicon.rev' % tag_h] = tag_s + df2['amplicon.rev']
df2['amplicon.len.str'] = df2['vj'].str.split("_").str[-2].astype(int) -\
df2['vi'].str.split("_").str[-2].astype(int) + \
df2['vi'].str.split("_").str[-1].astype(int) + 1
tmppath = tempfile.mkstemp()[1]
inpath = tmppath
queries_linearfold = ['seq.i', 'seq.j', 'amplicon.fwd', 'amplicon.rev']
tag_queries = []
for tag_h, tag_s in tagseqs:
tag_queries += [tag_h + "." + q for q in queries_linearfold]
queries_linearfold += tag_queries
linearfold = '%s -V' % linearfold_bin
inpath = tmppath + ".in"
# SLOWEST STEP. RUN ONCE PER SEQUENCE SET
for qi, label in enumerate(queries_linearfold):
scores_col = 'dG.LFold.%s' % label
if scores_col in df2:
continue
print(qi, label)
DataFrameAnalyzer.write_list(df2[label].replace("T", "U"), inpath)
print('running LinearFold with columns %s (# queries=%i)' % (label, df2.shape[0]))
out_linearfold = os.popen('cat %s | %s' % (inpath, linearfold)).read()
scores = [float(r.split(" ")[1].replace("(", '').replace(")", ''))
for r in out_linearfold.split("\n") if '.' in r]
df2['dG.LFold.%s' % label] = scores
# calculate Z-scores based on mean by length
# do this twice (i) only for tag+primers and (ii) for tag+primer+amplicon
# mean is estimated by linear model
# more negative Z-scores = less reliable primers/amplicons due to unexpected stability
for sub_queries in [q for q in queries_linearfold if 'seq' in q],\
[q for q in queries_linearfold if 'amplicon' in q]:
z_df = []
for qi, q in enumerate(sub_queries):
print(q, q in df2)
if not "dG.LFold.%s" % q in df2:
continue
sel = df2[[q, "dG.LFold.%s" % q]]
sel.columns = ['len', 'dG']
sel['len'] = sel['len'].str.len()
print(sel.head())
z_df.append(sel)
z_df = pd.concat(z_df).reset_index(drop=True)
from sklearn.linear_model import LinearRegression
# calculate a coefficient to estimate expected dG by len
print('generate Z-score using linear model')
lm = LinearRegression().fit(np.array(z_df[['len']]), np.array(z_df['dG']))
sigma = float(z_df.groupby('len').std().mean())
for qi, q in enumerate(sub_queries):
df2['z.score.dG.%s' % q] = (df2['dG.LFold.%s' % q] - lm.predict(np.array(df2[q].str.len()).reshape(-1, 1))) / sigma
DataFrameAnalyzer.to_tsv_gz(df2, join(output_dir, "%s_pairs_test.tsv.gz" % fasta_id))
df2.to_excel(join(output_dir, "%s_pairs_test.xlsx" % fasta_id), index=None)
else:
print('skip STEP 2 (file exists and overwrite2=False)')
if overwrite3:
# step 3: add mismatches with others viruses, precalculated from first table ( If not in path2 already )
print('# STEP 3: final verify on best.hit.others')
df2 = DataFrameAnalyzer.read_tsv_gz(join(output_dir, "%s_pairs.tsv.gz" % fasta_id))
df = DataFrameAnalyzer.read_tsv_gz(bkp_path_df)
best_hit_by_k = DataFrameAnalyzer.get_dict(df, 'k', 'best.hit.others')
df2 = DataFrameAnalyzer.read_tsv_gz(join(output_dir, "%s_pairs.tsv.gz" % fasta_id))
df2.to_excel(join(output_dir, "%s_pairs.xlsx" % fasta_id), index=None)
# map the best hit with other viruses
df2['best.hit.others.vi'] = [a for a in list(df2['vi'].map(best_hit_by_k))]
df2['best.hit.others.vj'] = [a for a in list(df2['vj'].map(best_hit_by_k))]
df2['best.hit.others'] = df2[['best.hit.others.vi', 'best.hit.others.vi']].max(axis=1)
DataFrameAnalyzer.to_tsv_gz(df2, join(output_dir, "%s_pairs.tsv.gz" % fasta_id))
df2.to_excel(join(output_dir, "%s_pairs.xlsx" % fasta_id), index=None)
if overwrite4:
print('# STEP 4: Sequence variation')
df2 = DataFrameAnalyzer.read_tsv_gz(join(output_dir, "%s_pairs.tsv.gz" % fasta_id))
# check for MSA alternate variants within the primers
msa_flagged_primers = []
variants_by_h_compressed = {}
for k in variants_by_h:
print(k)
variants_by_h_compressed[k] = {}
for pi in sorted(variants_by_h[k]):
if pi % 1000 == 0:
print(pi, k)
variants_by_h_compressed[k][pi] = Counter([nt.upper() for nt in variants_by_h[k][pi]
for n in range(variants_by_h[k][pi][nt]) if nt.upper() in {'A', 'C', 'G', 'T'}])
flag_details = []
for ri, r in df2.iterrows():
if ri % 100 == 0:
print(ri, 'out of', df2.shape[0])
flag = False
flag_details.append('')
k = r['cds'].split(" ")[1][1:-1]
start, end, primer_len = int(r["vi"].split("_")[-2]), int(r["vj"].split("_")[-2]), int(r["vi"].split("_")[-1])
for pi in range(start, min(end + primer_len, len(variants_by_h_compressed[k]))):
n_variants = len({nt.upper() for nt in variants_by_h_compressed[k][pi].keys() if nt.upper() in 'ACGT'})
if n_variants >= 2:
flag = True
flag_details[-1] += str(pi) + ":" + str(dict(variants_by_h_compressed[k][pi])) + ";"
msa_flagged_primers.append(flag)
df2['msa.flagged.primers'] = msa_flagged_primers
df2['msa.flagged.primers.desc'] = flag_details
df2['n.msa.flagged.primers.desc'] = [len(x.split(";")) - 1 for x in df2['msa.flagged.primers.desc']]
DataFrameAnalyzer.to_tsv_gz(df2, join(output_dir, "%s_pairs.tsv.gz" % fasta_id))
df2.to_excel(join(output_dir, "%s_pairs.xlsx" % fasta_id), index=None)
df = DataFrameAnalyzer.read_tsv_gz(join(output_dir, "%s.tsv.gz" % fasta_id))
# check for MSA alternate variants within the primers
msa_flagged_primers = []
flag_details = []
for ri, r in df.iterrows():
if ri % 100 == 0:
print(ri, 'out of', df2.shape[0])
flag = False
flag_details.append('')
k = r['fa.name'].split(" ")[1][1:-1]
for pi in range(r['fasta.position'], r['fasta.position'] + r['primer.len']):
n_variants = len({nt for nt in variants_by_h_compressed[k][pi].keys() if nt not in {'N'}})
if n_variants >= 2:
flag = True
flag_details[-1] += str(pi) + ":" + str(dict(variants_by_h_compressed[k][pi])) + ";"
msa_flagged_primers.append(flag)
df['msa.flagged.primers'] = msa_flagged_primers
df['msa.flagged.primers.desc'] = flag_details
df['n.msa.flagged.primers.desc'] = [len(x.split(";")) - 1 for x in df['msa.flagged.primers.desc']]
print('Saving selected primers:')
DataFrameAnalyzer.to_tsv(df, join(output_dir, "%s.tsv.gz" % fasta_id))
df.to_excel(join(output_dir, "%s.xlsx" % fasta_id))
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--pmin", type=int, default=20, help='minimum primer length (def. 20)')
parser.add_argument("--pmax", type=int, default=24, help='minimum primer length (def. 24)')
parser.add_argument("--gcmin", type=float, default=40,
help='minimum GC content for primers (def. 40 Celsius).')
parser.add_argument("--gcmax", type=float, default=60,
help='maximum GC content for primers (def. 60 Celsius).')
parser.add_argument("--tmmin", type=float, default=41,
help='minimum melting temperature (def. 41 Celsius)')
parser.add_argument("--ampliconmin", type=int, default=120, help='minimum amplicon length (def. 120)')
parser.add_argument("--ampliconmax", type=int, default=240, help='minimum amplicon length (def. 240)')
# Use the T7 sequence as a default tag
parser.add_argument('--tags', type=str, help='Multi fasta file receiving primers for analysis',
default='./input/tags/tags.fa')
parser.add_argument("--ntest", type=int, default=None, help='for load tests. Default is None (--ntest 10 = test for 10 primer pairs and finish')
parser.add_argument("--overwrite1", action='store_true', help='Force repeat single primer generation and background viruses scanning step', default=0)
parser.add_argument("--overwrite2", action='store_true', help='Force repeat 1-2 primer pairs and secondary structure asssessment', default=0)
parser.add_argument("--overwrite3", action='store_true', help='Force adding best hit with other viruses from table 1 in table 2', default=0)
parser.add_argument("--overwrite4", action='store_true', help='Force adding nucleotide variation from original table', default=0)
parser.add_argument("-p", "--progressbar", action='store_true', default=False,
help='Show progress bar (not implemented in deployed version).')
parser.add_argument("--checkothers", action='store_true', default=False)
parser.add_argument('--fastaid', type=str, default='GCF_009858895.2_CDS', help='fastaid to use from input dir (def. GCF_009858895.2_CDS)')
parser.add_argument('--linearfold', type=str, default='linearfold', help='path to linearfold if not declared in $PATH')
parser.add_argument('--inputdir', type=str, default="input", help='input directory (def. ./input)')
parser.add_argument('--outputdir', type=str, default="output", help='output directory (def. ./output)')
opts = parser.parse_args()
run(opts.pmin, opts.pmax, opts.gcmin, opts.gcmax, opts.tmmin, opts.ampliconmin, opts.ampliconmax,
opts.fastaid, opts.inputdir, opts.outputdir, opts.linearfold, opts.checkothers, ntest=opts.ntest, tags=opts.tags,
overwrite1=opts.overwrite1, overwrite2=opts.overwrite2, overwrite3=opts.overwrite3, overwrite4=opts.overwrite4)