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#!/usr/bin/env python
import sys
import os
from multiprocessing import Pool
from multiprocessing import cpu_count
from subprocess import *
from itertools import combinations
from itertools import ifilter
from itertools import imap
import numpy as np
import pdb
fg_mers = {}
bg_mers = {}
seq_ends = []
if(len(sys.argv) == 5):
selectivity_fn = sys.argv[1]
fg_fasta_fn = sys.argv[2]
bg_fasta_fn = sys.argv[3]
output_file = sys.argv[4]
fg_genome_length = os.path.getsize(fg_fasta_fn)
bg_genome_length = os.path.getsize(bg_fasta_fn)
else:
print "please specify your inputs"
print "ex: score_mers.py selectivity_file fg_fasta_file bg_fasta_file"
exit()
# empty class to fill up mer information with
class Mer:
pass
# import our variables
cpus = int(os.environ.get("cpus", cpu_count()));
min_mer_range = int(os.environ.get("min_mer_range", 6));
max_mer_range = int(os.environ.get("max_mer_range", 12));
min_mer_count = int(os.environ.get("min_mer_count", 0));
max_select = int(os.environ.get("max_select", 15));
max_check = int(os.environ.get("max_check", 35));
max_mer_distance = int(os.environ.get("max_mer_distance", 5000));
max_consecutive_binding = int(os.environ.get("max_consecutive_binding", 4));
def get_max_consecutive_binding(mer1, mer2):
'''
Return the maximum number of consecutively binding mers
when comparing two different mers, using the reverse compliment.
'''
binding = { 'A': 'T',
'T': 'A',
'C': 'G',
'G': 'C',
'_': False
}
# Swap variables if the second is longer than the first
if len(mer2) > len(mer1):
mer1, mer2 = mer2, mer1
# save the len because it'll change when we do a ljust
mer1_len = len(mer1)
# reverse mer2,
mer2 = mer2[::-1]
# pad mer one to avoid errors
mer1 = mer1.ljust(mer1_len + len(mer2), "_")
max_bind = 0;
for offset in range(mer1_len):
consecutive = 0
for x in range(len(mer2)):
if binding[mer1[offset+x]] == mer2[x]:
consecutive += 1
if consecutive > max_bind:
max_bind = consecutive
else:
consecutive = 0
return max_bind
def pop_fg(mer):
''' helper for map function '''
populate_locations(fg_fasta_fn, fg_mers, mer)
def pop_bg(mer):
''' helper for map function '''
populate_locations(bg_fasta_fn, bg_mers, mer)
def populate_locations(input_fn, mers, mer):
'''
Run the strstreamone command, and parse in the integers that are output
by the command, and add it to mers[mer].pts
strstreamone just prints the location of a string argv[1] in stdout.
We also do the reverse compliment, using tac and tr piped together.
'''
cmd = 'strstreamone ' + mer + " < " + input_fn
strstream = Popen(cmd, stdout=PIPE, shell=True)
for line in strstream.stdout:
mers[mer].pts.append(int(line))
cmd = 'tac ' + input_fn + " | tr '[ACGT]' '[TGCA]' | strstreamone " + mer + " " + input_fn
strstream = Popen(cmd, stdout=PIPE, shell=True)
for line in strstream.stdout:
mers[mer].pts.append(int(line))
def filter_mers(combination):
for combo in combinations(combination, 2):
if heterodimer_dic[combo]:
return True
for mer in combination:
for other_mer in combination:
if not mer == other_mer:
if mer in other_mer:
return True
return False
def check_feasible(selected):
total = 0;
for mer in selected:
total += len(fg_mers[mer].pts)
if (fg_genome_length / total) > max_mer_distance:
print "even if we select all top ", max_select,
print "mers disregarding any critera, and they were perfectly evenly spaced we would ",
print "still not meet the right max mer distance < ", max_mer_distance, "requirement."
print total, " / ", fg_genome_length, " = ", total / fg_genome_length
exit()
def score_mers(selected):
import time
total_scored = 0;
check_feasible(selected)
p = Pool(cpus)
fh = open(output_file, 'wb');
fh.write("Combination\tScore\tFG_mean_dist\tFG_stdev_dist\tBG_mean_dist\tBG_var_dist\n");
for select_n in range(1, max_select+1):
print "scoring size ", select_n,
t = time.time()
scores_it = p.imap_unordered(score, combinations(selected, select_n), chunksize=8192)
for score_res in scores_it:
if score_res is not None:
total_scored += 1;
combination, scores, fg_mean_dist, fg_stddev_dist, bg_ratio = score_res
fh.write(str(combination) + "\t");
fh.write(str(scores) + "\t");
fh.write(str(fg_mean_dist) + "\t");
fh.write(str(fg_stddev_dist) + "\t");
fh.write(str(bg_ratio) + "\n");
print "size ", select_n, "took:", time.time() - t
if(total_scored == 0):
print "NO RESULTS FOUND"
fh.write("NO RESULTS FOUND\n");
heterodimer_dic = {}
def score(combination):
# input is a string of mers like
# ['ACCAA', 'ACCCGA', 'ACGTATA']
# check if the combination passes our filters
if filter_mers(combination):
return None
# fg points
fg_pts = []
fg_dist = []
for mer in combination:
fg_pts = fg_pts + fg_mers[mer].pts
fg_pts = fg_pts + seq_ends
fg_pts.sort()
if fg_pts[0] is not 0:
fg_pts = [0] + fg_pts
# fg distances
fg_dist = np.diff(fg_pts)
# return without calculating scores if any objects are higher than our max distance
if any(dist > max_mer_distance for dist in fg_dist):
#return [combination, "max", max(fg_dist)]
return None
min_mer_distance = max(len(i) for i in combination)
# return without calculating scores if any mers are closer than the length of
# our longest mer in the combination
if any(dist < min_mer_distance for dist in fg_dist):
#return [combintaion, 'max']
return None
# bg points
bg_pts = []
bg_dist = []
for mer in combination:
bg_pts = bg_pts + bg_mers[mer].pts
if len(bg_pts()) <= 1:
bg_pts.append(0, 1, fg_genome_length)
bg_sum = sum(bg_pts)
bg_ratio = (bg_genome_length / bg_sum)
nb_primers = len(combination)
fg_mean_dist = np.mean(fg_dist)
fg_std_dist = np.std(fg_dist)
# this is our equation
score = (nb_primers * fg_mean_dist * fg_std_dist) / bg_ratio
return [combination, score, fg_mean_dist, fg_std_dist, bg_ratio]
def load_end_points(fn):
end_points = []
cmd = "sequence_end_points < " + fn
points_fh = Popen(cmd, stdout=PIPE, shell=True)
for line in points_fh.stdout:
end_points.append(int(line))
return end_points
def load_heterodimer_dic(selected_mers):
'''
Generate a heterodimer dict which contains every possible combination of
selected mers, so later we can check each combination without re-running the
max_consecutive_binding function.
The stored values are Booleans, True if the result is larger than acceptable.
'''
for (mer1, mer2) in combinations(selected_mers, 2):
res = get_max_consecutive_binding(mer1, mer2)
heterodimer_dic[(mer1, mer2)] = res > max_consecutive_binding
heterodimer_dic[(mer2, mer1)] = res > max_consecutive_binding
# print res, heterodimer_dic[(mer1, mer2)]
def main():
'''
Basic worflow:
Load Top X Selective Primers
Populate Locations of Primers
Score Combinations For All Sizes
'''
import time
selected = []
selectivity_fh = open(selectivity_fn, "r")
# get our genome length
for row in selectivity_fh:
(mer, fg_count, bg_count, selectivity) = row.split()
fg_mers[mer] = Mer()
fg_mers[mer].pts = []
fg_mers[mer].count = fg_count
bg_mers[mer] = Mer()
bg_mers[mer].pts = []
bg_mers[mer].count = bg_count
selected.append([mer, selectivity])
selected = selected[-max_check:]
selected_mers = [row[0] for row in selected]
# print selected_mers
print "Populating sequence end points"
seq_ends = load_end_points(fg_fasta_fn)
print "Populating foreground locations"
map(pop_fg, selected_mers)
print "Populating background locations"
map(pop_bg, selected_mers)
print "calculating heterodimer distances"
load_heterodimer_dic(selected_mers)
print "scoring mer combinations"
score_mers(selected_mers)
print "output_file:", output_file
if __name__ == "__main__":
sys.exit(main())
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