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plateau3-1.4.1_web.py
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#!/usr/python
#
# Copyright (c) 2019 AG Freund, Freie Universitaet Berlin (GER)
#
# This program is distributed as a free scientific tool,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
#
# For questions about running the script or for reporting bugs, please contact either:
# Christian Freund ([email protected])
# or
# Eliot Morrison ([email protected])
#
import sys
import re
import csv
import os
import numpy as np
from scipy import stats
import math
import itertools
from pyteomics import fasta, parser, mass, achrom, electrochem, auxiliary
from scipy import stats
from os import listdir
from os.path import isfile, join
from scipy.stats import ttest_ind
import statistics
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
from matplotlib_venn import venn3, venn3_circles, venn2, venn2_circles
import matplotlib.patches as patches
import cgi
import cgitb
#cgitb.enable()
import timeit
import html
from PIL import Image, ImageDraw, ImageFont
#expdir = sys.argv[7]
#resultdir = '../nobackup/results/'+(expdir.split('../../nobackup/uploads/')[-1])
expdir = './'
resultdir = './'
net_path = '/Users/eliotmorrison/netMHCIIpan-3.2/'
iedb_path = '../IEDB/'
if not os.path.exists(resultdir):
os.mkdir(resultdir, 0o755 )
else:
pass
def openfile(filename, cols):
filename = str(filename)
filename = re.sub("b'", "", filename)
filename = re.sub("'", "", filename)
if 'final_renorm' not in filename:
filename = os.path.expanduser(expdir+filename.split('/')[-1])
else:
filename = os.path.expanduser(resultdir+filename.split('/')[-1])
raw = list(csv.reader(open(filename, 'r'), delimiter='\t'))
headers = raw[0]
raw = raw[1:]
count = 0
inds = []
for a in headers:
for b in cols:
if a == b:
inds.append(count)
count += 1
return raw, headers, inds
def savefile(filename, out, headers_out):
filename = str(filename)
filename = re.sub("b'", "", filename)
filename = re.sub("'", "", filename)
if headers_out != []:
out.insert(0,headers_out)
filename = filename.split('/')[-1]
if 'volcano_table' in filename or 'final_renorm' in filename:
path = resultdir
else:
path = expdir
with open(os.path.expanduser(path+filename), 'w') as a_file:
for result in out:
result = '\t'.join(result)
a_file.write(result + '\n')
def get_geo_mean(ratios):
rat_sum = 0.0
rat_prod = 1.0
for b in ratios:
rat_sum += b
rat_prod *= b
avg = float(rat_sum) / float(len(ratios))
geo_mean = (rat_prod)**(1.0/float(len(ratios)))
geo_sum = 0.0
for i in ratios:
sqr = math.log(float(i)/float(geo_mean))**2
geo_sum += sqr
sd_geo = math.exp(math.sqrt(geo_sum/float(len(ratios))))
# SD(geo) is never less than 1.0. It is a factor.
# If the geo mean is 0.91, and SD(geo) is 1.06, then the +/- SD confidence interval
# is 0.86 (which is 0.91 / 1.06) to 0.96 (0.91*1.06). The 95% confidence interval is
# 0.91 / 1.06**(1.96) = 0.81 to 0.91 * 1.06**1.96 = 1.02.
# - 95% confidence interval
minus_95_conf = geo_mean / (sd_geo**(1.96))
plus_95_conf = geo_mean * (sd_geo**(1.96))
est = math.log(geo_mean)
l = math.log(minus_95_conf)
u = math.log(plus_95_conf)
se = (u - l) / (2*1.96)
z = abs(est / se)
p = math.exp(-0.717*z - 0.416*(z**2))
sem = statistics.stdev(ratios)/math.sqrt(float(len(ratios)))
return geo_mean, sd_geo, p, sem
def html_output(content):
# Headers
#print("Content-Type:text/html; charset=UTF-8")
#print() # blank line required at end of headers
print('<center>')
# Content
#print("<html><body>")
for x in content:
print(x + '<br>')
#print("</body></html>")
# This gets the total intensity areas for each raw file
def add_areas_to_evidence(evidence_file):
cols = ['Raw file', 'm/z', 'Retention time', 'Intensity']
raw, headers, inds = openfile(evidence_file, cols)
unique_exps = []
headers_out = ['Run', 'Total intensity']
out = []
for a in raw:
unique_exps.append(a[inds[0]])
unique_exps = list(set(unique_exps))
unique_exps.sort()
for a in unique_exps:
entry = ['']*len(headers_out)
entry[0] = a
intens_sum = 0.0
for b in raw:
if b[inds[0]] == entry[0] and b[inds[3]] != '':
intens_sum += float(b[inds[3]])
entry[1] = str(intens_sum)
out.append(entry)
out_file = 'ms_exps_total_intens_areas_for_norm.txt'
savefile(out_file, out, headers_out)
exp_tots = out[1:]
headers_out = headers
headers_out.append('Intensity')
headers_out.append('Intensity (normalized)')
out = []
for a in raw:
entry = ['']*len(headers_out)
for x in range(0,len(a)):
entry[x] = a[x]
last_ind = x + 1
intens = a[inds[3]]
exp = a[inds[0]]
for b in exp_tots:
if b[0] == exp:
intens_tot = float(b[1])
intens_norm = ''
if intens != '':
intens_norm = float(intens)/intens_tot
entry[last_ind] = str(intens)
entry[last_ind+1] = str(intens_norm)
out.append(entry)
evidence_file = str(evidence_file)
out_file = evidence_file.split('.txt')[0] + '_intens_norm.txt'
savefile(out_file, out, headers_out)
def get_cond_peps(file_in, fasta_file):
cols = ['Sequence', 'Proteins', 'Raw file', 'Intensity (normalized)']
raw, headers, inds = openfile(file_in, cols)
unique_prots_raw = []
unique_conds = []
unique_bio_reps = ['']
count = 0
prot_seqs = []
no_fasta = []
for a in raw:
prots = a[inds[1]].split(';')
for b in prots:
if len(b) > 0:
prot = b.split('CON__')[-1]
if '|' in prot:
prot = prot.split('|')[1]
unique_prots_raw.append(prot)
unique_conds.append(a[inds[2]])
unique_prots_raw = list(set(unique_prots_raw))
unique_conds = list(set(unique_conds))
unique_prots = []
fasta_str = ''
fasta_seqs = []
for description, sequence in fasta.read(fasta_file):
if '|' in description:
fasta_str += description.split('|')[1] + ';'
else:
fasta_str += description + ';'
fasta_seqs.append(sequence)
count = 0
for prot in unique_prots_raw:
count += 1
fasta_test = False
if prot+';' in fasta_str:
fasta_test = True
#print('Fasta match found:', prot, count, '/', len(unique_prots_raw))
unique_prots.append(prot)
else:
no_fasta.append([prot])
unique_prots = list(set(unique_prots))
headers_out = ['Protein', 'Experiment', 'Passing Peptides', 'FASTA seq', 'Intensities (normalized)']
out = []
count = 0
small_fasta = []
for prot in unique_prots:
count += 1
split1 = fasta_str.split(prot+';')
fasta_ind = len(split1[0].split(';'))
prot_seq = fasta_seqs[fasta_ind-1]
small_fasta.append(['>|' + prot + '|'])
small_fasta.append([prot_seq])
prot_raw = []
for a in raw:
if prot in a[inds[1]]:
prot_raw.append(a)
for cond in unique_conds:
pep_str = ''
intens_str = ''
for a in prot_raw:
if cond == a[inds[2]]:
pep_str += a[inds[0]] + ';'
intens_str += a[inds[3]] + ';'
pep_str = pep_str[:-1]
intens_str = intens_str[:-1]
if pep_str != '':
entry = [prot, cond, pep_str, prot_seq, intens_str]
#print(prot, count, '/', len(unique_prots))
out.append(entry)
file_out = file_in.split('.txt')[0] + '_passpeps.txt'
savefile(file_out, out, headers_out)
file_out = file_in.split('.txt')[0] + '_missing_fasta.txt'
savefile(file_out, no_fasta, ['Missing'])
file_out = fasta_file.split('.fasta')[0] + '_small.fasta'
savefile(file_out, small_fasta, [])
def get_cond_peps_filt(file_in, fasta_file, var_file, bio_rep_min, tech_rep_min):
cols = ['Sequence', 'Proteins', 'Raw file', 'Raw file', 'Intensity (normalized)']
raw, headers, inds = openfile(file_in, cols)
unique_peps = []
for a in raw:
unique_peps.append(a[inds[0]])
unique_peps = list(set(unique_peps))
unique_conds_1 = []
unique_bio_reps_1 = []
unique_tech_reps_1 = []
var_raw, v_h, v_i = openfile(os.path.expanduser(var_file), [])
vars = []
var_raw.insert(0,v_h)
for a in var_raw:
raw_file = a[0]
cond = a[1]
bio_rep = a[2]
tech_rep = a[3]
unique_conds_1.append(cond)
unique_bio_reps_1.append(cond+';'+bio_rep)
unique_tech_reps_1.append(cond+';'+bio_rep+';'+tech_rep)
vars.append(a)
unique_conds_1 = list(set(unique_conds_1))
unique_bio_reps_1 = list(set(unique_bio_reps_1))
unique_tech_reps_1 = list(set(unique_tech_reps_1))
pep_headers = ['Sequence', 'Protein']
for cond in unique_conds_1:
pep_headers.append('Pass in ' + cond)
pep_pass_table = []
unique_peps.sort()
raw.sort()
start_ind = 0
count = 0
for pep in unique_peps:
count += 1
pass_test = False
pep_ind = 2
entry = ['']*len(pep_headers)
entry[0] = pep
prots = []
pep_raw = []
for i in range(start_ind,len(raw)-1):
a = raw[i]
next_ent = raw[i+1]
if i >= len(raw)-1:
pep_raw.append(a)
if a[inds[0]] == pep:
pep_raw.append(a)
if next_ent[inds[0]] != pep:
start_ind = i-1
break
for cond in unique_conds_1:
cond_raws = []
for b in vars:
if b[1] == cond:
cond_raws.append(b[0])
bio_pass = False
bio_reps_found = []
for a in pep_raw:
if a[inds[0]] == pep:
prots.append(a[inds[1]])
check = False
raw_file = a[inds[2]]
for c in cond_raws:
if c == raw_file:
check = True
for d in vars:
if d[0] == raw_file:
bio_rep = d[2]
tech_rep = d[3]
if check == True:
if bio_rep not in bio_reps_found:
bio_reps_found.append(bio_rep)
if len(bio_reps_found) >= bio_rep_min:
bio_nums = []
for bio_rep_a in bio_reps_found:
bio_rep_raws = []
for c in vars:
if c[1] == cond and c[2] == bio_rep_a:
bio_rep_raws.append(c[0])
tech_reps_found = []
for a in pep_raw:
if a[inds[0]] == pep:
check = False
raw_file = a[inds[2]]
for c in bio_rep_raws:
if c == raw_file:
check = True
for d in vars:
if d[0] == raw_file:
bio_rep = d[2]
tech_rep = d[3]
if check == True and bio_rep_a == bio_rep:
tech_reps_found.append(tech_rep)
tech_reps_found = list(set(tech_reps_found))
bio_nums.append([bio_rep_a, len(tech_reps_found)])
passing_bios = 0
for d in bio_nums:
if d[1] >= tech_rep_min:
passing_bios += 1
if passing_bios >= bio_rep_min:
entry[pep_ind] = 'Yes'
else:
entry[pep_ind] = 'No'
else:
entry[pep_ind] = 'No'
pep_ind += 1
prots = list(set(prots))
prot_str = ''
for b in prots:
prot_str += b + ';'
entry[1] = prot_str
#print(pep, count, '/', len(unique_peps))
pep_pass_table.append(entry)
pep_file_out = file_in.split('.txt')[0] + '_pep_pass_table.txt'
savefile(pep_file_out, pep_pass_table, pep_headers)
pep_pass_table = pep_pass_table[1:]
unique_prots_raw = []
unique_conds = []
unique_bio_reps = ['']
count = 0
prot_seqs = []
no_fasta = []
for a in raw:
prots = a[inds[1]].split(';')
count += 1
for b in prots:
if len(b) > 0:
prot = b.split('CON__')[-1]
if '|' in prot:
prot = prot.split('|')[1]
unique_prots_raw.append(prot)
unique_conds.append(a[inds[2]])
unique_prots_raw = list(set(unique_prots_raw))
unique_conds = list(set(unique_conds))
unique_prots = []
fasta_str = ''
fasta_seqs = []
for description, sequence in fasta.read(fasta_file):
if '|' in description:
fasta_str += description.split('|')[1] + ';'
else:
fasta_str += description + ';'
fasta_seqs.append(sequence)
count = 0
for prot in unique_prots_raw:
count += 1
fasta_test = False
if prot+';' in fasta_str:
fasta_test = True
unique_prots.append(prot)
else:
no_fasta.append([prot])
unique_prots = list(set(unique_prots))
headers_out = ['Protein', 'Experiment', 'Passing Peptides', 'FASTA seq', 'Intensities (normalized)']
out = []
small_fasta = []
count = 0
for prot in unique_prots:
count += 1
split1 = fasta_str.split(prot+';')
fasta_ind = len(split1[0].split(';'))
prot_seq = fasta_seqs[fasta_ind-1]
small_fasta.append(['>|' + prot + '|'])
small_fasta.append([prot_seq])
prot_raw = []
for a in raw:
if prot in a[inds[1]]:
prot_raw.append(a)
pep_raw = []
for a in pep_pass_table:
if prot in a[1]:
pep_raw.append(a)
ents = []
zeroes = 0
for raw_file in unique_conds:
pep_str = ''
intens_str = ''
for a in prot_raw:
if raw_file == a[inds[2]]:
pass_test = False
for b in vars:
if raw_file == b[0]:
cond = b[1]
for i in range(0,len(pep_headers)):
if 'Pass in ' + cond in pep_headers[i]:
pep_ind = i
for c in pep_raw:
if c[0] == a[inds[0]]:
if c[pep_ind] == 'Yes':
pep_str += a[inds[0]] + ';'
intens_str += a[inds[4]] + ';'
pep_str = pep_str[:-1]
intens_str = intens_str[:-1]
entry = [prot, raw_file, pep_str, prot_seq, intens_str]
ents.append(entry)
if intens_str == '':
zeroes += 1
#out.append(entry)
if zeroes != len(ents):
for entry in ents:
out.append(entry)
print(entry[-1], count, '/', len(unique_prots))
file_out = file_in.split('.txt')[0] + '_passpeps_filt.txt'
savefile(file_out, out, headers_out)
file_out = file_in.split('.txt')[0] + '_missing_fasta.txt'
savefile(file_out, no_fasta, ['Missing'])
file_out = fasta_file.split('.fasta')[0] + '_small.fasta'
savefile(file_out, small_fasta, [])
def gen_epitopes(file_in, fasta_file, min_epi_len, min_step_size, min_epi_overlap):
cols = ['Protein', 'Passing Peptides', 'FASTA seq', 'Intensities (normalized)']
raw, headers, inds = openfile(file_in, cols)
headers_out = headers[:-1]
headers_out.append('Core epitopes')
headers_out.append('Whole epitopes')
headers_out.append('PLAtEAU Intens. (norm)')
headers_out.append('Peptides contributing to core epitopes')
out = []
jump_count = 0
total_count = 0
for j in raw:
total_count += 1
entry = ['']*len(headers_out)
for i in range(0,len(j)-1):
entry[i] = j[i]
core_epis = []
whole_epis = []
founds_for_entry = []
pep_matches = []
prot = j[inds[0]]
prot_seq = j[inds[2]]
if j[inds[1]] != '':
pep_list = j[inds[1]].split(';')
intens_list = j[inds[3]].split(';')
# 0 = AA, 1 = position
seq_pos = []
count = 1
for a in prot_seq:
seq_pos.append([a, count])
count += 1
# pep_pos: 0 = peptide, 1 = start_index
pep_pos = []
# 0 = AA, 1 = position
tot_found = []
for y in range(0,len(pep_list)):
pep = pep_list[y]
intens = intens_list[y]
if intens == '':
intens = '0.0'
before_pep = prot_seq.split(pep)[0]
if len(prot_seq.split(pep)) > 1:
after_pep = prot_seq.split(pep)[1]
else:
after_pep = ''
pep_start_ind = len(before_pep)
pep_pos.append([pep, pep_start_ind])
for i in range(0,len(pep)):
tot_found.append([pep[i], pep_start_ind + i + 1, float(intens)])
pep_pos.sort(key=lambda x: int(x[-1]))
# this gives a total protein primary sequence w/ spec. count at each position
seq_list = []
for a in seq_pos:
tot_count = 0
intens_count = 0.0
for b in tot_found:
if b[0] == a[0] and b[1] == a[1]:
tot_count += 1
intens_count += b[2]
#seq_list.append([a[0], a[1], tot_count])
seq_list.append([a[0], a[1], intens_count])
# Trim the seq_list to 5 residues before first intens
# and 5 residues after last intens, to minimize time
# spent going over 0-intensity residues
epis = []
start_pos = []
end_pos = []
for i in range(1,len(seq_list)-1):
prev_num = float(seq_list[i-1][2])
current_num = float(seq_list[i][2])
next_num = float(seq_list[i+1][2])
#if current_num != 0 or next_num != 0:
#print(i, prev_num, current_num, next_num)
if current_num == 0.0 and next_num != 0.0:
#start_pos.append(i-3) # ******
start_pos.append(i) # ******
#print('Start pos:', i+1)
if i == 1 and current_num != 0.0:
start_pos.append(i-1)
#print('Start pos:', i+1)
if current_num != 0.0 and next_num == 0.0:
end_pos.append(i+2)
#print('End pos:', i)
#print('')
if i == len(seq_list)-2 and current_num != 0.0:
end_pos.append(i+2)
new_start_pos = []
new_end_pos = []
for i in range(0,len(start_pos)):
epi = seq_list[start_pos[i]:end_pos[i]]
found_str = ''
founds = []
for x in epi:
if float(x[2]) not in founds:
if float(x[2]) != 0.0:
founds.append(float(x[2]))
found_str += str(x[2]) + '--'
found_str = found_str[:-2]
# a normal epitope should go low > high > low
# two local maxima will go low > high > low > high > low
# Create a string: 0 = less than or equal, 1 = greater than
found_strs = []
founds.sort()
for y in founds:
bin_str = ''
for z in epi:
if float(z[2]) <= y:
bin_str += '0'
else:
bin_str += '1'
#print(y, bin_str)
split_1 = bin_str.split('0')
split_2 = []
for z in split_1:
if z != '':
split_2.append(z)
#print(y, split_1, split_2)
if len(split_2) > 1:
if len(split_2[0]) >= min_epi_len and len(split_2[-1]) >= min_epi_len:
found_strs.append([y, len(split_2[0]), len(split_2[-1]), bin_str])
#print(y, bin_str)
#print(found_str)
#print(found_strs[-1])
if len(found_strs) > 0:
#print(found_strs)
new_min = found_strs[0][0]
bin_str = found_strs[0][-1]
zero_list = []
for k in bin_str.split('1'):
if k != '':
zero_list.append(k)
gap_size = len(zero_list[1])
gap_pos_bin = len(zero_list[0]) + found_strs[0][1]
gap_pos = epi[gap_pos_bin][1]
#print('Gap size:', gap_size, 'starting:', gap_pos_bin, gap_pos)
# gap_pos = first '0' of gap
# if gap size = 1, add new end = gap_pos - 1, new start = gap_pos
# if gap size > 1:
# new end = len(first 1s) + gap_size
# new start = len(first 1s)
new_starts = []
new_ends = []
if gap_size == 1:
new_ends.append(gap_pos-1)
new_end_pos.append(gap_pos-1)
new_starts.append(gap_pos)
new_start_pos.append(gap_pos)
elif gap_size > 1:
new_ends.append(gap_pos+gap_size)
new_end_pos.append(gap_pos+gap_size)
new_starts.append(gap_pos)
new_start_pos.append(gap_pos)
if len(new_start_pos) > 0:
for f in range(0,len(new_start_pos)):
start_pos.append(new_start_pos[f])
end_pos.append(new_end_pos[f])
start_pos.sort()
end_pos.sort()
# Look for overlapping epitopes
# A standard nested peptide distribution should change by 1-2 residues
# A large jump (> 5 residues) likely means a different epitope distribution
# pep_pos: 0 = peptide, 1 = start_index
pep_pos_unique = []
for x in pep_pos:
if x not in pep_pos_unique:
pep_pos_unique.append(x)
new_start_pos = []
new_end_pos = []
jump_test = False
for i in range(0,len(start_pos)):
epi = seq_list[start_pos[i]:end_pos[i]]
start_ind = start_pos[i]
epi_str = ''
for y in epi:
epi_str += str(y[0])
match_pep_pos = []
for x in pep_pos_unique:
#if epi_str in x[0]:
if x[0] in epi_str:
match_pep_pos.append(x)
found_str = ''
founds = []
for x in epi:
if float(x[2]) not in founds:
if float(x[2]) != 0.0:
founds.append(float(x[2]))
found_str += str(x[2]) + '--'
found_str = found_str[:-2]
#jump_test = False
for i in range(0,len(match_pep_pos)-1):
this_pep = match_pep_pos[i]
next_pep = match_pep_pos[i+1]
step_size = next_pep[1] - this_pep[1]
#print('&&', this_pep, next_pep, step_size, len(this_pep[0]), step_size + min_epi_overlap)
if next_pep[1] - this_pep[1] > min_step_size and len(this_pep[0]) <= step_size + min_epi_overlap:
#print('')
#print('Jump:', this_pep, next_pep)
jump_test = True
if next_pep[1]-1 not in start_pos:
new_start_pos.append(next_pep[1]) ####
if this_pep[1]+len(this_pep[0])-1 not in end_pos:
new_end_pos.append(this_pep[1]+len(this_pep[0])) ####
#print('Step size:', step_size, 'pep length:', len(this_pep[0]))
#print('Start added:', new_start_pos[-1])
#print('End added:', new_end_pos[-1])
# pep_list_1 will be peptides with start index <= this_pep[1]
# pep_list_2 will be peptides with start index > this_pep[1]
pep_list_1_ind_end = this_pep[1]
pep_list_1_ind_start = start_ind
pep_list_2_ind_start = next_pep[1]
pep_list_2_ind_end = match_pep_pos[-1][1] + len(match_pep_pos[-1][0])
#pep_list_2_ind = next_pep[1]
pep_list_1 = []
pep_list_2 = []
if jump_test == True:
for j in match_pep_pos:
if pep_list_1_ind_start <= j[1] <= pep_list_1_ind_end:
pep_list_1.append(j[0])
elif pep_list_2_ind_start <= j[1] <= pep_list_2_ind_end:
pep_list_2.append(j[0])
if jump_test == True:
jump_count += 1
#print('Jumps:', jump_count, '/', total_count, ' (' + str(float(jump_count)/float(total_count)*100.0)+'%)')
if len(new_start_pos) > 0:
#print('**', new_start_pos)
#print(new_end_pos)
for f in range(0,len(new_start_pos)):
start_pos.append(new_start_pos[f])
end_pos.append(new_end_pos[f])
start_pos.sort()
end_pos.sort()
for i in range(0,len(start_pos)):
epi = seq_list[start_pos[i]:end_pos[i]]
core_start = start_pos[i]
core_end = end_pos[i]
founds = []
for x in epi:
if float(x[2]) not in founds:
founds.append(float(x[2]))
whole_epi = ''
for l in epi:
whole_epi += l[0]
list_1_jump_test = False
list_2_jump_test = False
if jump_test == True:
if pep_list_1_ind_start <= start_pos[i] <= pep_list_1_ind_end:
list_1_jump_test = True
if pep_list_2_ind_start <= start_pos[i] <= pep_list_2_ind_end:
list_2_jump_test = True
pep_match = []
intens_match = []
if jump_test == False:
for y in range(0,len(pep_list)):
pep = pep_list[y]
intens = intens_list[y]
if pep in whole_epi:
pep_match.append(pep)
intens_match.append(intens)
else:
#print(start_pos[i], pep_list_1_ind)
#print('List 1', pep_list_1_ind_start, start_pos[i], pep_list_1_ind_end)
#print('List 2', pep_list_2_ind_start, start_pos[i], pep_list_2_ind_end)
if list_1_jump_test == True:
for y in range(0,len(pep_list)):
pep = pep_list[y]
intens = intens_list[y]
if pep in whole_epi and pep in pep_list_1:
pep_match.append(pep)
intens_match.append(intens)
elif list_2_jump_test == True:
for y in range(0,len(pep_list)):
pep = pep_list[y]
intens = intens_list[y]
if pep in whole_epi and pep in pep_list_2:
pep_match.append(pep)
intens_match.append(intens)
else:
for y in range(0,len(pep_list)):
pep = pep_list[y]
intens = intens_list[y]
if pep in whole_epi:
pep_match.append(pep)
intens_match.append(intens)
if list_1_jump_test == True or list_2_jump_test == True:
tot_found_1 = []
for y in range(0,len(pep_match)):
pep = pep_match[y]
intens = intens_match[y]
if intens == '':
intens = 0.0
before_pep = prot_seq.split(pep)[0]
if len(prot_seq.split(pep)) > 1:
after_pep = prot_seq.split(pep)[1]
else:
after_pep = ''
pep_start_ind = len(before_pep)
#pep_pos.append([pep, pep_start_ind])
for n in range(0,len(pep)):
tot_found_1.append([pep[n], pep_start_ind + n + 1, intens])
# this gives a total protein primary sequence w/ spec. count at each position
seq_list_1 = []
for f in seq_pos:
tot_count = 0
intens_count = 0.0
for d in tot_found_1:
if d[0] == f[0] and d[1] == f[1]:
tot_count += 1
if d[2] != '':
intens_count += float(d[2])
#seq_list_1.append([f[0], f[1], tot_count])
seq_list_1.append([f[0], f[1], intens_count])
epi_1 = seq_list_1[start_pos[i]:end_pos[i]]
founds_1 = []
for x in epi_1:
if float(x[2]) not in founds_1:
if float(x[2]) != 0.0:
founds_1.append(float(x[2]))
pep_match_str = ''
for g in pep_match:
pep_match_str += g + ','
pep_match_str = pep_match_str[:-1]
core_str = ''
if list_1_jump_test == True or list_2_jump_test == True:
found_str = ''
for x in epi_1:
found_str += str(x[2]) + '--'
found_str = found_str[:-2]
founds_for_entry.append(found_str)
founds_1.sort(reverse=True)
passing_core = False
for num in founds_1:
core = []
for k in epi_1:
if float(k[2]) >= num:
core.append(k)
if len(core) >= min_epi_len and passing_core == False:
passing_core = True
core_str = ''
core_start = core[0][1]
core_end = core[-1][1]
for l in core:
core_str += l[0]
core_str = ''
core = seq_list[core_start-1:core_end]
for l in core:
core_str += l[0]
# epitope, start pos, end pos
if list_1_jump_test == True:
core_str = core_str + '*'
if list_2_jump_test == True:
core_str = '*' + core_str
core_epis.append([core_str, core_start, core_end])
if list_1_jump_test == True:
whole_epi = whole_epi + '*'
if list_2_jump_test == True:
whole_epi = '*' + whole_epi
whole_epis.append([whole_epi, start_pos[i], end_pos[i]])
pep_matches.append(pep_match_str)
else:
found_str = ''
for x in epi:
found_str += str(x[2]) + '--'
found_str = found_str[:-2]
founds_for_entry.append(found_str)
founds.sort(reverse=True)
passing_core = False
for num in founds:
core = []
for k in epi:
if float(k[2]) >= num:
core.append(k)