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minibar_og.py
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minibar_og.py
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#!/usr/bin/env python
from __future__ import division, print_function
import sys, errno, timeit
try:
import edlib
except:
print("\n edlib module not found, to install use:\n\n pip install edlib\n", file=sys.stderr)
exit(3)
def read_barcode_file(primer_filename, ops):
global fwd_indexes, rev_indexes
global len_first_index
global fwd_primer, len_fwd_primer, rev_primer, len_rev_primer
global indx2sample_map_fr, indx2sample_map_rf
# read the primer file contents into a list
def read_primer_list(primer_filename):
def header_line(ln, ops):
if ops.first_line_header == -1: # -1 means auto detect
lwln = ln.lower()
return lwln.find('sample') >= 0 or lwln.find('id') >= 0
else:
return ops.first_line_header != 0
primers = []
headers = []
with open(primer_filename) as primerFile:
ln1 = primerFile.readline().rstrip()
ln1_list = ln1.split("\t")
if header_line(ln1, ops):
headers = ln1_list
else:
primers.append(ln1_list)
if len(ln1_list) < 5:
print("Need at least 5 tab delimited columns in the barcode_file.\n"
"Here is the first line of '{}':\n{}\n".format(primer_filename, ln1), file=sys.stderr)
sys.exit(2)
for line in primerFile:
primers.append(line.rstrip().split("\t"))
return primers
def set_col_ix(primer_list, ops):
if ops.col_set_on_cmdline or len(primer_list) < 1:
return
# if user didn't set explicitly, set col pos based on number of fields
ops.sampleix = 0
num_cols = len(primer_list[0])
if num_cols == 5:
ops.fwix = 1; ops.fwprm = 2; ops.rvix = 3; ops.rvprm = 4
elif num_cols == 6: # presume 1 is a name
ops.fwix = 2; ops.fwprm = 3; ops.rvix = 4; ops.rvprm = 5
elif num_cols in [7,8,9]: # presume 1 and 4 are names
ops.fwix = 2; ops.fwprm = 3; ops.rvix = 5; ops.rvprm = 6
else: # leave as the 11 column defaults: SAMPLE = 0; FWIX = 2; FWPRM = 4; RVIX = 7; RVPRM = 9
return #ops.fwix = 2; ops.fwprm = 4; ops.rvix = 7; ops.rvprm = 9
# make 2 dict maps, keys are a combination of the two indexes that
# uniquely define the Sample. we use 2 maps so we don't care if the
# first index found is the forward or reverse index.
def make_sample_id_maps(primer_list, sampleid_col, fwix_col, rvix_col):
fwixmap = {}; rvixmap = {}
for p in primer_list:
id = p[sampleid_col].strip()
if id == "": continue
fwix = p[fwix_col].upper(); rvix = p[rvix_col].upper()
fwixmap[fwix + '|' + rvix] = id
rvixmap[rvix + '|' + fwix] = id
return fwixmap, rvixmap
# (fwd and rv) index will occur multiple times in the primer_list
# this will make a dictionary entry for each unique index found at ix_col.
# each entry lists the indices in primer_list where this index occurs
# also UPPERCASE the index since sequence will be in uppercase
def make_index_list(primer_list, ix_col):
index_map = {}
for i, p in enumerate(primer_list):
index = p[ix_col].upper()
if index in index_map:
index_map[index].append(i)
else: # first time seeing this index
index_map[index] = [i]
return index_map
# 02Dec2018 JBH v0.21 use of same index in fwd and reverse won't work for Method 2
def check_dup_fwd_rev_index(fwd_indexes, rev_indexes):
dup_index = []
for f in fwd_indexes:
for r in rev_indexes:
if f.upper() == r.upper():
dup_index.append(f)
if ops.search_method != 1 and len(dup_index) > 0:
errstr = "Using same index in forward and reverse lists: " + " ".join(dup_index)
report_duplicates = error if ops.search_method == 2 else warning
if ops.search_method == 2:
errstr = "Not valid for Method 2: " + errstr
report_duplicates(errstr)
# make sure ids and index/pairs not blank and not duplicates; and number of cols is the same for each line
def check_empty_and_dups(primer_list, sampleid_col, fwix_col, rvix_col):
sample_map = {}; index_pair_map = {}; col_count_map = {}; col_count_first_line = {}
missing_samples = []; dup_samples = []; dup_indexpairs = []
for i, p in enumerate(primer_list):
id = p[sampleid_col].strip()
if id == "":
missing_samples.append(i)
elif id in sample_map:
dup_samples.append([id, i])
else:
sample_map[id] = i
if fwix_col >= len(p) or rvix_col >= len(p): # not enough tabbed fields in line
error("Not enough tabbed fields in entry {}: {}".format(i+1,p))
fwix = p[fwix_col].upper(); rvix = p[rvix_col].upper()
ix_pair = fwix + ' | ' + rvix
if ix_pair in index_pair_map:
dup_indexpairs.append([ix_pair, i])
else:
index_pair_map[ix_pair] = id
col_count = len(p)
if not col_count in col_count_map:
col_count_map[col_count] = 0
col_count_first_line[col_count] = "\t".join(p)
col_count_map[col_count] += 1
# report any errors
errstr = ""
if len(missing_samples) > 0:
errstr = "No sample names on items: {}\n ".format(", ".join(map(str, missing_samples)))
for dup in dup_samples:
errstr += "{} sample name duplicated on item {}\n ".format(dup[0], dup[1])
for dup in dup_indexpairs:
errstr += "{} index pairs duplicated on item {}\n ".format(dup[0], dup[1])
if len(col_count_map) > 1:
expected = len(primer_list[0]) # number of columns we expected in each line
errstr += "Some lines in the barcode file have other than {} columns:\n".format(expected)
example_str = " {} line(s) with {} columns, e.g: {}\n"
for c in col_count_map:
if c != expected:
errstr += example_str.format(col_count_map[c], c, col_count_first_line[c])
if errstr != "":
report_duplicates = error if ops.error_on_duplicate_samples else warning
report_duplicates(errstr)
# read the tsv data from the primer and index file
primers = read_primer_list(primer_filename)
# define column indices for the Index and the Primer both Forward and Backward
set_col_ix(primers, ops) # set based on number of columns if user didn't specifically tell us what to use
FWIX = ops.fwix; RVIX = ops.rvix; FWPRM = ops.fwprm; RVPRM = ops.rvprm; SAMPLE = ops.sampleix
# do some basic error checking
check_empty_and_dups(primers, SAMPLE, FWIX, RVIX)
# gather unique set of fwd and rev indexes
fwd_indexes = make_index_list(primers, FWIX)
rev_indexes = make_index_list(primers, RVIX)
first_index = primers[0][FWIX]
len_first_index = len(first_index)
# 02Dec2018 JBH check to see if same index/barcode used in fwd_indexes and rev_indexes, issue warning
check_dup_fwd_rev_index(fwd_indexes, rev_indexes)
# set fwd and rev primer vars
fwd_primer = primers[0][FWPRM]
len_fwd_primer = len(fwd_primer)
rev_primer = primers[0][RVPRM]
len_rev_primer = len(rev_primer)
# map index pairs to sample_id. create 2 dicts one with FWIX|RVIX key other with RVIX|FWIX key
indx2sample_map_fr, indx2sample_map_rf = make_sample_id_maps(primers, SAMPLE, FWIX, RVIX)
return primers
def display_barcode_file_inf(ops):
if len(ops.barcode_file_info) < 1 or not ops.barcode_file_info[0] in 'frpcba':
error('"{}" is an invalid argument for -info'.format(ops.barcode_file_info))
def display_col_info(primer_list, ops):
first = primer_list[0]
cols = "Sample name in col {}:\t{}\n".format(1+ops.sampleix, first[ops.sampleix])
cols += "Forward Barcode in col {}:\t{}\n".format(1+ops.fwix, first[ops.fwix])
cols += "Forward Primer in col {}:\t{}\n".format(1+ops.fwprm, first[ops.fwprm])
cols += "Reverse barcode in col {}:\t{}\n".format(1+ops.rvix, first[ops.rvix])
cols += "Reverse Primer in col {}:\t{}\n".format(1+ops.rvprm, first[ops.rvprm])
print(cols)
def display_index_editdistances(indexes):
shortest = len_first_index+1; ixlen = len_first_index
ln = 'Indexes '
for i in indexes: # header with each index in a column
ln += '\t' + i
print(ln)
for i in indexes:
ln = i
for j in indexes:
result = edlib.align(i,j, task='distance')
dist = result['editDistance']
if dist > 0 and dist < shortest:
shortest = dist
ixlen = len(j)
ln += '\t' + str(dist)
print(ln)
print('\nClosest has edit distance of {} with index lengths of {}, {:.2%} alike'.format(shortest, ixlen, 1-(shortest/ixlen)))
primer_list = read_barcode_file(ops.primerfile, ops)
info = ops.barcode_file_info[0]
if info == 'p' or info == 'a':
result = edlib.align(fwd_primer, rev_primer, task='path', additionalEqualities=YR_maps)
dist = result['editDistance']
alike = 1-(dist/len_fwd_primer)
msg = 'Primers {} {} edit distance {} with lengths of {}, {:.2%} alike'
print(msg.format(fwd_primer, rev_primer, dist, len_rev_primer, alike))
elif info == 'c': # show cols values for first line 14Jun2018
display_col_info(primer_list, ops)
if info in 'frba':
if info != 'b' and info != 'a':
indexes = fwd_indexes if info != 'r' else rev_indexes
else: # we want 'b'oth indexes combined to see what we have
indexes = fwd_indexes.copy()
indexes.update(rev_indexes)
display_index_editdistances(indexes)
def open_sequence_file(fqname): # won't allow stdin with this method
global fa_headerln
if fqname[-3:] == ".gz": # open as a gzipped file
import gzip
fh = gzip.open(fqname, 'rt')
else: # use regular open
fh = open(fqname)
firstln = fh.readline().rstrip()
if firstln[0] == '@': # it's a fastq file
fh.seek(0) # rewind file to beginning
isfastq = True
else:
isfastq = False
if firstln[0] == '>': # it's a fasta file either
fa_headerln = firstln
else:
print("{} is not a Fastq or a Fasta file.".format(fqname))
return None, False
global load_seqrecs
load_seqrecs = load_fq_seqrecs if isfastq else load_fa_seqrecs
return fh, isfastq
def load_fq_seqrecs(fh_seq, maxseqs):
global rec_seqs, rec_hdrs, rec_quals # appends to these (caller is responsible for emptying the lists)
RECSIZE = 4
rec_ln = 0
for ln in fh_seq:
ln = ln.rstrip()
rec_ln += 1
if rec_ln == 1: # header line
rec_hdrs.append(ln)
elif rec_ln == 2:
rec_seqs.append(ln)
elif rec_ln == 4:
rec_quals.append(ln)
if rec_ln == RECSIZE:
rec_ln = 0
if len(rec_seqs) >= maxseqs:
break
return len(rec_seqs)
def load_fa_seqrecs(fh_seq, maxseqs):
global rec_seqs, rec_hdrs, rec_quals # appends to these (caller is responsible for emptying the lists)
global fa_headerln # header line for next fasta record
faseq = ''
for ln in fh_seq:
ln = ln.rstrip()
if ln[0] != '>':
faseq += ln
else: # got to header of next fasta record
rec_hdrs.append(fa_headerln)
rec_seqs.append(faseq)
rec_quals.append('') # placeholder for fasta, not used
fa_headerln = ln
faseq = ''
if len(rec_seqs) >= maxseqs:
break
if faseq != '': # presume it is because we reached end of file, not maxseqs
rec_hdrs.append(fa_headerln)
rec_seqs.append(faseq)
rec_quals.append('') # placeholder for fasta, not used
return len(rec_seqs)
def rev_comp_py2(nt_str):
import string
trans_tbl = string.maketrans('AaTtGgCcNn', 'TtAaCcGgNn')
return nt_str.translate(trans_tbl)[::-1]
def rev_comp_py3(nt_str):
trans_tbl = str.maketrans('AaTtGgCcNn', 'TtAaCcGgNn')
return nt_str.translate(trans_tbl)[::-1]
# v 0.20 20Nov2018 add 3rd arg to determine which set (reverse or forward) to check first
# this will support using same index pairs but flipped from forward to reverse to id a sample
def sample_id_from_indexes(index1, index2, check_rev_first=None):
key = index1 + '|' + index2
map_one = indx2sample_map_fr
map_two = indx2sample_map_rf
if check_rev_first:
map_one, map_two = map_two, map_one
if key in map_one:
return map_one[key]
elif key in map_two:
return map_two[key]
return ''
# if key in indx2sample_map_fr:
# return indx2sample_map_fr[key]
# elif key in indx2sample_map_rf:
# return indx2sample_map_rf[key]
# return ''
def ids_from_index_matches(ind_match_1, ind_match_2):
id_list = []; id_dict = {}
for i1 in ind_match_1:
for i2 in ind_match_2:
id = sample_id_from_indexes(i1[0], i2[0])
if id != '' and not id in id_dict:
id_list.append(id)
id_dict[id] = True
return id_list
YR_maps = [("Y", "C"), ("Y", "T"), ("R", "A"), ("R", "G")]
def primer_positions(seq, primer):
rs = edlib.align(primer, seq, 'HW', 'locations', max_dist_primer, additionalEqualities=YR_maps)
return rs['editDistance'], rs['locations']
# this uses edlib.align()
max_dist_index = 4
max_search_area = 80
def search_seq_for_indexes(seq, indexes):
rslts = []
seq_prefix = seq[:max_search_area]
for query in indexes:
rs = edlib.align(query, seq_prefix, 'HW', 'locations', max_dist_index)
dist = rs['editDistance']
if dist > -1:
rslts.append([query, dist, rs['locations']])
return rslts
# index_matches are zero, one or more matches of an index query
# (if more than 1, they will have have the same dist score).
# primer_matches first element is the dist score, then a list of
# matches with that same score, each entry being a tuple of beg/end pos,eg (47, 73)
#
# best case is an index match with a good primer match right after it.
# return is a tuple of best match and primer best match, if any.
# good hit ex: (1, 'CGCTCTGCCAAAGAT', (33, 46), 3, (47, 73))
# where we have index dist score, index, pos, primer dist score, primer pos
# -1, '', () for index no-match; -1, () for primer no match
def choose_best_index(index_matches, primer_matches):
CLOSE_ENOUGH = 5
best_score = 999; best_im = None; best_loc = None; primer_loc = None
no_result = (-1, '', (), -1, ())
if len(index_matches) == 0 or len(primer_matches) == 0:
return no_result
primer_score = primer_matches[0]
if primer_score > -1:
# loop over primers first, since there is usually 1 or none rather than more, unlike index close matches
for pm in primer_matches[1]: # each pm is a tuple of the primer beg, end position
pm_beg = pm[0]; closest = 999
for im in index_matches:
score = im[1]
for loc in im[2]:
close = abs(abs(pm_beg - loc[1])-1)
if close < CLOSE_ENOUGH: # good primer match right after good index match
if score < best_score or (score==best_score and close < closest): # better score or same score and closer
best_im = im
best_loc = loc
primer_loc = pm
closest = close
best_score = score
return (best_im[1], best_im[0], best_loc, primer_score, primer_loc) if best_im else no_result
def find_best_index(seq, fwd=True):
global ind_matches, prm_matches
indexes = fwd_indexes if fwd else rev_indexes
primer = fwd_primer if fwd else rev_primer
ind_matches = search_seq_for_indexes(seq, indexes)
prm_matches = primer_positions(seq, primer)
return choose_best_index(ind_matches, prm_matches)
def search_for_best_index(seq, skip_fwdchk=False):
global best_index
strand = '?'; best = [-1]
if not skip_fwdchk:
best = find_best_index(seq, True) # true means use fwd indexes
if best[0] == -1: # try rev indexes and primer
best = find_best_index(seq, False) # False means use rev indexes
if best[0] != -1:
best_index = best[1]
strand = '-'
else: # found it with fwd indexes
best_index = best[1]
strand = '+'
return (strand,) + best
# for each sequence in the rec_seqs this will output results for the sample ID based
# on finding a good fwd_index/fwd_primer or rev_index/rev_primer pair at the beginning
# of the sequence. the complementary pair at the other end is best find, but if that
# is not there then we use complementary matched indexes to determine sample IDs
def search_sequence_list(seq_number, rec_seqs, rec_hdrs, rec_quals, ops, fh_map):
global H, HH, Hh, hh, samps, mult_ids
USE_FWD_INDEXES = True; USE_REV_INDEXES = False
HH_method = 1; hh_method = 2; Hh_method = 3
unknown_id = 'unk'
outtype = ops.output
search_method = ops.search_method
assert(search_method in [HH_method, Hh_method, hh_method])
show_color = ops.show_color
seq_ix = -1
for fseq in rec_seqs:
seq_ix += 1; seq_number += 1
ID_matches = 0; all_ids = ''; strength = ' '
ix2 = ''
ix1_loc = prm1_loc = ix2_loc = prm2_loc = rslt = None
strand = '?' # easy way to shoehorn in new method without deeper indentation
if search_method != hh_method:
rslt = search_for_best_index(fseq) # looking for a good match of an index close to a primer
strand = rslt[0]
if strand != '?': # strong hit for beginning index & primer, check ending index
H += 1
ix1 = best_index
ixed1 = rslt[1]; prmed1=rslt[4]; ed1='('+str(ixed1)+','+str(prmed1)+')'
ix1_loc = rslt[3]; prm1_loc = rslt[5]
rc_fseq = rev_comp(fseq)
if strand == '+': # found fwd indexes at beginning of seq, search revcomp of seq for rev indexes
strand2 = '-'
best = find_best_index(rc_fseq, USE_REV_INDEXES)
else: # found rev indexes at beginning of seq, search revcomp of seq for fwd indexes
assert strand == '-'
strand2 = '+'
best = find_best_index(rc_fseq, USE_FWD_INDEXES)
if best[0] != -1: # strong hit for ending index too
rcrslt = (strand2,) + best
strength = 'HH'
HH += 1
ix2 = best[1]
ix2_loc = rcrslt[3]; prm2_loc = rcrslt[5]
ed2 = '('+str(best[0])+','+str(best[3])+')'
else: # no primer hit, see if there are any index matches
rcrslt = (strand2,) + tuple(ind_matches)
if len(ind_matches) == 0:
strength = 'Hx'
ed2 = '(-1,-1)'
else:
Hh += 1
# loop through matches and choose those that create a sample id
best_score = 999; best_im = None; ID_matches = 0
for im in ind_matches:
score = im[1]
if score <= best_score: # if none yet, or none that matches a sample, choose this im
best_score = score
if not best_im:
best_im = im
id = sample_id_from_indexes(ix1, im[0], strand == '-')
if id != '':
ID_matches += 1
all_ids += id + ' '
if ID_matches == 1 or score < best_score:
# all_ids = id
best_im = im # take first or higher scoring match as best
ix2 = best_im[0] # sequence
ix2_loc = best_im[2][0]
strength = 'Hh'
ed2 = '({},-1)'.format(best_im[1]) # score
sample_id = sample_id_from_indexes(ix1, ix2, strand == '-') if ID_matches <= 1 else all_ids.strip()
if sample_id != '': samps += 1
else: sample_id = unknown_id
if ID_matches > 1: mult_ids += 1
elif search_method == hh_method or search_method == Hh_method:
strand2 = '?'
ed1 = '(-1)'; ed2 = '(-1)'
rcrslt = (-1, '', (), -1, ())
rc_fseq = rev_comp(fseq)
beg_fwd = search_seq_for_indexes(fseq, fwd_indexes)
end_rev = search_seq_for_indexes(rc_fseq, rev_indexes)
fr_ids = ids_from_index_matches(beg_fwd, end_rev)
if len(fr_ids) > 0:
strand = '+'
strand2 = '-'
rslt = (strand,) + tuple(beg_fwd)
rcrslt = (strand2,) + tuple(end_rev)
strength = 'hh'
ed1 = '({})'.format(beg_fwd[0][1])
ed2 = '({})'.format(end_rev[0][1])
beg_rev = search_seq_for_indexes(fseq, rev_indexes)
end_fwd = search_seq_for_indexes(rc_fseq, fwd_indexes)
rf_ids = ids_from_index_matches(beg_rev, end_fwd)
if len(rf_ids) > 0 and strand == '?':
strand = '-'
strand2 = '+'
rslt = (strand,) + tuple(beg_rev)
rcrslt = (strand2,) + tuple(end_fwd)
strength = 'hh'
ed1 = '({})'.format(beg_rev[0][1])
ed2 = '({})'.format(end_fwd[0][1])
all_id_list = fr_ids + rf_ids
if len(all_id_list) > 0:
samps += 1
hh += 1
sample_id = " ".join(all_id_list)
if len(all_id_list) > 1:
mult_ids += 1
ix1_loc = rslt[1][2][0]
ix2_loc = rcrslt[1][2][0]
else:
strand = 'x'; strand2 = 'x' # report total misses as x(-1), x(-1) not ?(-1), ?(-1)
sample_id = unknown_id
else:
ed1 = '(-1,-1)'; ed2 = '(-1,-1)'
if rslt and int(rslt[1]) > -1:
ed1 = '({},{})'.format(rslt[1],rslt[2])
strand2 = 'x'; strand = 'x' # report total misses as x(-1,-1), x(-1,-1) not ?(-1,-1), ?(-1,-1)
sample_id = unknown_id
rcrslt = (-1, '', (), -1, ())
if outtype == 3: # 3 is the diagnostic output mode
if strand != '?':
print(seq_number, sample_id, '\t', strength, strand, rslt[1:], strand2, rcrslt[1:])
else:
if outtype == 2: # use upper/lower case to distinguish index in sequence, and potentially color
fseq = make_display_seq(fseq, ix1_loc, prm1_loc, ix2_loc, prm2_loc, show_color)
elif outtype == 4: # Trim primers from sequence and if fastq also trim qual line
beg_seq, end_seq = get_trim_locs(fseq, ix1_loc, prm1_loc, ix2_loc, prm2_loc)
fseq = fseq[beg_seq: end_seq]
if ops.isfastq:
rec_quals[seq_ix] = rec_quals[seq_ix][beg_seq: end_seq]
output_seq(sample_id, ID_matches, fh_map, ops,
fseq, rec_hdrs[seq_ix], rec_quals[seq_ix],
strength[0]+strand+ed1+','+strength[1]+strand2+ed2)
return seq_number
def output_seq(sample_id, ID_matches, fh_map, ops, seq, hdr, quals, match_info):
fh = sys.stdout
if ops.output_to_files:
sample_name = sample_id if ID_matches <= 1 else "Multiple_Matches" # see where we are writing the output
fh = get_sample_fh(sample_name, fh_map, ops.output_file_prefix, ops.isfastq)
fh.writelines([hdr,' ', match_info, ' ', sample_id, '\n'])
fh.writelines([seq, '\n'])
if ops.isfastq:
fh.writelines(['+\n', quals, '\n'])
def get_trim_locs(seq, ix1_loc, prm1_loc, ix2_loc, prm2_loc):
ln = len(seq)
beg_seq = 0
end_seq = ln
if prm1_loc: # set begin to char after end of left primer
beg_seq = prm1_loc[1]
elif ix1_loc: # set begin to char after end of index + length of primer
beg_seq = ix1_loc[0] + len_first_index + len_fwd_primer
if prm2_loc:
end_seq = ln-1 - prm2_loc[1]
elif ix2_loc:
end_seq = ln-1 - ix2_loc[1] - len_rev_primer
return beg_seq, end_seq
blue = "\033[0;34m"; green = "\033[0;32m"; red = "\033[0;31m"; NC = "\033[0m" # No Color (reset)
# flip case to highlight index and primer features in sequence, and apply color to these areas if requested
def make_display_seq(seq, ix1_loc, prm1_loc, ix2_loc, prm2_loc, show_colors = False, invert2s = True):
if not ix1_loc:
return seq
clr_ix1 = ''; clr_prm1 = ''; clr_ix2 = ''; clr_prm2 = ''; reset = NC if show_colors else ''
# set values for 5' index and primer
ix1_beg = ix1_loc[0]; ix1_end = ix1_beg + len_first_index
if show_colors:
clr_ix1 = blue
if prm1_loc:
prm1_beg = prm1_loc[0]
prm1_end = prm1_loc[1]
if show_colors: clr_prm1 = green
else:
prm1_beg = ix1_end
prm1_end = prm1_beg + len_fwd_primer
if show_colors: clr_prm1 = red
# set values for 3' primer and index, if no ix2_loc then no prm2_loc
if ix2_loc:
ix2_beg = ix2_loc[0]; ix2_end = ix2_loc[1]
if show_colors: clr_ix2 = blue
if prm2_loc:
prm2_beg = prm2_loc[0]; prm2_end = prm2_loc[1]
if show_colors: clr_prm2 = green
else:
ix2_beg = ix2_end = prm2_loc = None
if invert2s:
ln = len(seq)
if ix2_loc:
ib = ix2_beg
ix2_beg = ln-1 - ix2_end
ix2_end = ln - ib
if prm2_loc:
pb = prm2_loc[0]
prm2_beg = ln-1 - prm2_loc[1]
prm2_end = ln - pb
else: # set primer2 right before ix2_loc
prm2_beg = ix2_beg - len_rev_primer
prm2_end = ix2_beg
if show_colors: clr_prm2 = red
if not ix2_loc: # no 3' index or primer
display_seq = seq[0:ix1_beg].lower() + \
clr_ix1 + seq[ix1_beg:ix1_end].upper() + \
clr_prm1 + seq[prm1_beg:prm1_end].lower() + reset + seq[prm1_end:].upper()
else:
display_seq = seq[0:ix1_beg].lower() + \
clr_ix1 + seq[ix1_beg:ix1_end].upper() + \
clr_prm1 + seq[prm1_beg:prm1_end].lower() + reset + seq[prm1_end:prm2_beg].upper() + \
clr_prm2 + seq[prm2_beg:prm2_end].lower() + reset + \
clr_ix2 + seq[ix2_beg:ix2_end].upper() + reset + seq[ix2_end:].lower()
return display_seq
def get_sample_fh(sampID, fh_map, prefix, isfastq):
def make_sample_filename(sampID, prefix, isfastq):
sample = "".join([x if x.isalnum() or x in "._-$#" else "_" for x in sampID])
name = prefix + sample
name += '.fastq' if isfastq else '.fasta'
return name
if sampID in fh_map:
return fh_map[sampID]
sample_filename = make_sample_filename(sampID, prefix, isfastq)
try:
sample_fh = open(sample_filename, 'w')
fh_map[sampID] = sample_fh
return sample_fh
except:
return sys.stdout
def minibar(ops):
global H, HH, Hh, hh, samps, mult_ids
global rec_seqs, rec_hdrs, rec_quals
global max_dist_index, max_dist_primer, max_search_area
read_barcode_file(ops.primerfile, ops)
fh, isfastq = open_sequence_file(ops.sequencefile)
if not fh:
print("Problem opening '{}'".format(ops.sequencefile), file=sys.stderr)
return
# holds file handles for sample files
fh_map = {}
ops.isfastq = isfastq
max_search_area = ops.search_len
ixed = ops.index_edit_distance
max_dist_index = ixed if (ixed >= 1) else len_first_index - int(len_first_index * ixed)
max_dist_primer = int(len_fwd_primer * 0.3 + 3) if ops.primer_edit_distance==-1 else ops.primer_edit_distance
info_msg = '{} {} Index edit dist {}, Primer edit dist {}, Search Len {}, Search Method {}, Output Type {}'
print(info_msg.format(ops.primerfile, ops.sequencefile, max_dist_index, max_dist_primer,
max_search_area, ops.search_method, ops.output_letter), file=sys.stderr)
start_time = timeit.default_timer()
sequence_block_size = 10000
last_seq_to_output = ops.num_seqs
all_seqs = last_seq_to_output < 0
seq_num = 0; throwaway = 0
if ops.start_seq > 1:
throwaway = ops.start_seq - 1
last_seq_to_output += throwaway
rec_seqs = []; rec_hdrs = []; rec_quals = []
seq_num = load_seqrecs(fh, throwaway)
H = 0; HH = 0; Hh = 0; hh = 0; samps = 0; mult_ids = 0
while all_seqs or seq_num < last_seq_to_output:
to_read = sequence_block_size if all_seqs else min(sequence_block_size, last_seq_to_output-seq_num)
rec_seqs = []; rec_hdrs = []; rec_quals = []
if load_seqrecs(fh, to_read) < 1:
break
seq_num = search_sequence_list(seq_num, rec_seqs, rec_hdrs, rec_quals, ops, fh_map)
print(seq_num, end='\r', file=sys.stderr); sys.stderr.flush()
elapsed = timeit.default_timer() - start_time
if ops.search_method == 1:
hits = 'H {} HH {} Hh {}'.format(H, HH, Hh)
elif ops.search_method == 2:
hits = 'hh {}'.format(hh)
else: # method 3
hits = 'H {} HH {} Hh {} hh {}'.format(H, HH, Hh, hh)
result_str = '\r{} seqs: {} IDs {} Mult_IDs {} ({:0.4f}s)'.format(seq_num-throwaway, hits, samps, mult_ids, elapsed)
print(result_str, file=sys.stderr)
for fh in fh_map: # close the sample files if we had any open
try:
fh.close()
except:
pass
def version():
# 0.21 02Dec2018 -info both added, for same index in fwd and rev lists: err Method 2, warn Method 3, silent Method 1
# 0.20 20Nov2018 sample_id_from_indexes() 3rd arg to swap indexes btw fwd/rev so same index in rev fwd works
# 0.19 14Jun2018 -info cols
# 0.18 12Jun2018 remove single, tighten file read # 0.17 10Jun2018 -T -w added
return "minibar.py version 0.21"
def error(errmsg, exit_code=3):
print('\n ' + errmsg + '\n', file=sys.stderr)
sys.exit(exit_code)
def warning(warnmsg):
print('\n ' + warnmsg + '\n', file=sys.stderr)
def usage(show_all_descrips=False):
secondary_output_formats = \
"-D diagnostic output, instead of sequence displays edit distances of index and primer matches\n"
secondary_option_descrips = """
-w treat duplicates in barcode_file as warning, not error
-fh first line of barcode file considered a header (default: auto detect header)
-nh first line of barcode file is not a header (default: auto detect header)
-info cols show column settings in barcode file and values for the first line
-info all|fwd|rev|both|primer display barcode index or primer info, including edit distances
-n <num_seqs> number of sequences to read from file (ex: -n 100)
-n <first_seq>,<num_seqs> (ex: -n 102,3)\n"""
extra = secondary_option_descrips if show_all_descrips else ""
extra_fmts = secondary_output_formats if show_all_descrips else ""
usage = """
Usage: minibar.py barcode_file sequence_file [-pct <pct> | -e <int> -E <int>] [-l <int>]
[-F [-P <prefix>]] [-M 1|2|3]
[-S | -T | -C | -CC | -D]
[-cols <int_list>] [-info cols|fwd|rev|primer]
[-w] [-fh | -nh] [-n <num_seqs> | -n <first_seq>,<num_seqs>]
Identify MinION sequence by dual barcode indexes and primers.
The sequence file can be in Fasta or Fastq format, gzipped or plain text.
Sample ID is placed at end of header comment with match hit info before it.
({})
Example: ./minibar.py -C -F Demultiplex.txt example.fq
-h display this with all option's descriptions -v displays version
-p <pct> percentage match (.75)
-e <int> barcode edit distance value, overrides -p (4)
-E <int> primer edit distance value (11)
-l <int> length to search for index and primer at start and end of sequence (80)
-F create individual sample files for sequences with -S or -C output (default: False)
-f write to stdout instead of creating files (default: True)
-P <str> if -F, <str> is prefix for individual files, followed by sample ID. (default: sample_)
-M 1|2|3 Method to identify sample types using the barcodes (default: 3)
1 requires approximate match of barcode and primer at sequence start, this
and barcodes matched at the other end are used to identify sample IDs
2 finds matched barcodes on both ends of sequence, identifies pairs that match a sample ID
3 uses Method 1 and if it does not succeed, uses Method 2
-S outputs sequence record in fasta or fastq format of input (default output)
-T trims barcode and primer from each end of the sequence, then outputs record
-C similar to S but uses upper/lower case to show found barcode indexes and primers
-CC also colors found barcode blue, primer green if found, primer red otherwise
{}
-cols <int_list> column position in barcode_file for: sample, fwd index, fwd primer, rev index, rev primer
(default: 1,2,3,4,5 if 5 cols; 1,3,4,5,6 if 6 cols; 1,3,4,6,7 if 7 cols; 1,3,5,8,10 if 10 or more cols)
{}""".format(version(), extra_fmts, extra)
print(usage, file=sys.stderr)
sys.exit(3)
def getoptions(argv):
def is_int(i):
try:
int(i)
return True
except:
return False
def is_float(f):
try:
float(f)
return True
except:
return False
def num_err(arg, val):
error('Expected numbers, got \'{} {}\''.format(arg, val))
def process_cols_list(cols):
num_cols = 5 if opts.dual_indexes else 3
lst = cols.split(',')
if len(lst) != num_cols:
error('-cols value requires {} comma delimited positions.'.format(num_cols))
for i in range(num_cols):
if not is_int(lst[i]) or int(lst[i])<1:
error('-cols value requires {} comma delimited numbers greater than 1.'.format(num_cols))
ilst = [int(i) for i in lst]
opts.sampleix = ilst[0]-1
opts.fwix = ilst[1]-1
opts.fwprm = ilst[2]-1
if num_cols >= 5:
opts.rvix = ilst[3]-1
opts.rvprm = ilst[4]-1
else:
opts.rvix = -1
opts.rvprm = -1
opts.col_set_on_cmdline = True
min_options_required = 2
num_args = len(argv)
if num_args < min_options_required:
usage()
class opts:
primerfile = ''
sequencefile = ''
dual_indexes = True
search_len = 80
num_seqs = -1 # -1 means all sequences
start_seq = 0 # 0 means start with first seq
index_edit_distance = .75 # if < 0 it is a percentage to calculate
primer_edit_distance = -1 # -1 means calculate based on primer length
search_method = 3 # 2: means weak match of pairs, 1: strong ix/prm match at seq begin, 3: try 1, then 2
output_to_files = False # True means write sequences to individual sample ID files
output_file_prefix = 'sample_'
isfastq = True # reset when file sequence actually read
output = 1 # 4 trim, 3 is diagnostic, 2 means use case to show index & primers in sequence, else as is
output_letter = 'S' # letter that corresponds to S
show_color = False # in output 2 show color along with change in case (-F turns off)
# FWIX = 2; RVIX = 7; FWPRM = 4; RVPRM = 9; SAMPLE = 0
sampleix = 0
fwix = 2
fwprm = 4
rvix = 7
rvprm = 9
error_on_duplicate_samples = True
col_set_on_cmdline = False # if using defaults do settings based on num cols in file
first_line_header = -1 # -1 auto detect, 0 don't treat as header, otherwise it's a header
barcode_file_info = '' # valid values start with either 'f' 'r' or 'p'
unrecog = ''
ix_arg = 0
while ix_arg < (num_args - 1):
ix_arg += 1
arg = argv[ix_arg]
if arg == '-h' or arg == '--help':
usage(True)
elif arg == '-v' or arg == '--version':
print(version())
sys.exit(0)
if arg[0] != '-': # first 2 args without '-' are primer file and sequence file name
if opts.primerfile == "":
opts.primerfile = arg
elif opts.sequencefile == "":
opts.sequencefile = arg
else:
unrecog = arg
elif len(arg) > 1:
op = arg[1]
if op in 'SCDT': # single letter options
opts.output = {'S':1, 'C':2, 'D':3, 'T':4}.get(op,1)
opts.output_letter = op
if op == 'C' and len(arg)>2: opts.show_color = True
elif arg == '-nh' or arg == '-fh': # args to force 1st line as header or force as non-header
opts.first_line_header = 0 if arg == '-nh' else '1'
elif op == 'F': # trigger writing each sample to its own file
opts.output_to_files = True
elif op == 'f': # trigger writing each sample to its own file
opts.output_to_files = False
elif op == 'w': # treat duplicate samples as warning not error
opts.error_on_duplicate_samples = False
elif op in 'pkeKElMncPi': # these have a value after option
ix_arg += 1
if ix_arg > (num_args - 1):
error('expected value after {}'.format(arg))
val = argv[ix_arg]
have_int = is_int(val); have_float = is_float(val)
if op == 'n': # -n has 2 formats -n int and -n int,int
if ',' in val:
n1, n2 = val.split(',')
if not is_int(n1) or not is_int(n2): num_err(arg, val)
opts.start_seq = int(n1)
opts.num_seqs = int(n2)
else:
if not have_int: num_err(arg, val)
opts.num_seqs = int(val)
elif op == 'c':
if arg == '-cols':
process_cols_list(val)
else:
unrecog = arg
elif op == 'i':
if arg == '-info':
opts.barcode_file_info = val
else:
unrecog = arg
if (op in 'lkeKEM' and not have_int) or (op == 'p' and not have_float):
num_err(arg, val)
if op == 'l':
opts.search_len = int(val)
elif op == 'p':
opts.index_edit_distance = float(val)
elif op == 'k' or op == 'e':
opts.index_edit_distance = int(val)
elif op == 'K' or op == 'E':
opts.primer_edit_distance = int(val)
elif op == 'P':
opts.output_file_prefix = val
elif op == 'M':
opts.search_method = int(val) if int(val) in [1,2,3] else 1
else:
unrecog = arg