#!/usr/bin/env python
import csv
from collections import defaultdict
import sys, getopt
import os
import math
from shutil import copyfile
import pandas as pd
import numpy as np
from scipy.stats import chi2
from Bio import SeqIO
import os.path
SOFTWARES = {'meme','dreme','centrimo','meme_tomtom'}
##TYPES_SEARCHES = {'All','Narrow','Const','Max'}
def get_size_fasta(path_motif, exp_design_name):
with open(path_motif + 'Fasta_Summary.txt', 'w') as fasta_summary:
for data_name in os.listdir(path_motif+'fasta/'):
if data_name.endswith('.fasta'):
print(data_name)
fasta_seq = SeqIO.to_dict(SeqIO.parse(path_motif+'fasta/'+data_name, "fasta"))
print(data_name,len(fasta_seq))
fasta_summary.write(data_name.replace('.fasta','')+'\t'+str(len(fasta_seq))+'\n')
def parse_all_logs(path_motif, exp_design_name):
with open(path_motif + '../Motif_search.log', 'w') as general_log:
header = 'Data_name\t' + '\t'.join(SOFTWARES)
general_log.write(header+'\n')
for data_name in os.listdir(path_motif):
if not data_name.startswith('.') and not data_name.endswith('.log') and not data_name.endswith('.sh'):
progress_filename = path_motif + data_name + '/progress_log.txt'
line = data_name + '\t'
for software in SOFTWARES:
found = False
with open(progress_filename,'r') as file:
for row in file:
status_soft = 'name: '+software+' status: 0'
if status_soft in row:
found = True
if found:
line += '1\t'
else:
line += '0\t'
general_log.write(line.strip() + '\n')
def parse_dreme(path_motif, exp_design_name):
with open(path_motif + 'dreme.sh', 'w') as dreme_file:
for data_name in os.listdir(path_motif):
if exp_design_name in data_name:
print(data_name)
if not data_name.startswith('.') and not data_name.endswith('.log') and not data_name.endswith('.sh'):
# print(data_name)
dreme_xml = path_motif + data_name + '/dreme_out/dreme.xml'
if os.path.exists(dreme_xml):
dreme_list = list()
with open(dreme_xml, 'r') as file:
for row in file:
dreme_list.append(row.strip())
if '<command_line>' in row:
dreme_file.write(row)
dreme_txt = path_motif + data_name + '/dreme_out/dreme.txt'
dreme_txt_list = list()
with open(dreme_txt, 'r') as file:
for row in file:
dreme_txt_list.append(row.strip())
dreme_txt_intro = ''
write_intro = False
for row in dreme_txt_list:
if row.startswith('MOTIF '):
break
if 'MEME version' in row:
write_intro = True
if write_intro:
dreme_txt_intro += row + '\n'
#print(dreme_txt_intro)
# parse dreme
with open(path_motif + '../logs/Dreme_' + data_name + '.log', 'w') as general_log:
header = 'ID\tMotif\tNb_sites\tPos_Occurence\tNeg_Occurence\tNb_Seq\t' \
'Percent\tPos_Percent\tNeg_Percent\tEvalue'
general_log.write(header + '\n')
# search length fasta
for line in dreme_list:
if '<positives name=' in line:
number_seq = float(line.split(' ')[2].replace('\"','').replace('count=',''))
# search motifs
for i in range(1, len(dreme_list)):
line = dreme_list[i]
if '<motifs>' in line:
index_motifs = i
break
# get motifs info
for k in range(index_motifs+1, len(dreme_list)):
if '<motif' in dreme_list[k]:
new_line = dreme_list[k].replace('\"','').split(' ')
#print(new_line)
id = new_line[1].replace('id=', '')
#print(id,data_name)
print(dreme_txt_intro)
seq = new_line[2].replace('seq=', '')
occ = float(new_line[4].replace('nsites=', ''))
pos_occ = float(new_line[5].replace('p=', ''))
neg_occ = float(new_line[6].replace('n=', ''))
evalue = new_line[8].replace('evalue=', '')
# save table
line_to_write = [id, seq, str(occ), str(pos_occ),str(neg_occ), str(number_seq),
str(occ/number_seq), str(pos_occ/number_seq), str(neg_occ/number_seq),
str(evalue)]
general_log.write('\t'.join(line_to_write) + '\n')
# copy png file
for imagefile in os.listdir(path_motif + data_name + '/dreme_out/'):
if id in imagefile:
if 'nc_' in imagefile:
src = path_motif + data_name + '/dreme_out/' + imagefile
dst = path_motif + '../motif_figure/'+seq + '_nc.png'
copyfile(src, dst)
else:
src = path_motif + data_name + '/dreme_out/' + imagefile
dst = path_motif + '../motif_figure/' + seq + '_rc.png'
copyfile(src, dst)
# save motif
with open(path_motif + '../motif/' + seq + '.meme', 'w') as meme_file:
meme_file.write(dreme_txt_intro+'\n')
# find motif info
dreme_txt_motif = ''
write_motif = False
for row in dreme_txt_list:
if write_motif and row.startswith('MOTIF '):
break
if ('MOTIF '+seq+' DREME') in row:
write_motif = True
if write_motif:
dreme_txt_motif += row + '\n'
print(dreme_txt_motif)
meme_file.write(dreme_txt_motif + '\n')
def regroup_motifs(path_motif, exp_design_name):
motif_set = set()
dict_dataset = defaultdict(list)
dict_evalue = defaultdict(list)
for data_name in os.listdir(path_motif+'logs/'):
if data_name.startswith('Dreme_') and data_name.endswith('.log'):
type_data = data_name.replace('Dreme_','').replace('.log','')
print(type_data,data_name)
with open(path_motif+'logs/'+data_name,'r') as log_file:
log_file.readline()
for line in log_file:
motif = line.split('\t')[1]
evalue = line.strip().split('\t')[-1]
motif_set.add(motif)
dict_dataset[motif].append(type_data)
dict_evalue[motif].append(evalue)
with open(path_motif + 'Motif_'+exp_design_name+'.txt','w') as motif_log:
motif_log.write("Motif\tData\tP-value\n")
for motif in motif_set:
motif_log.write(motif+'\t'+';'.join(dict_dataset[motif])+'\t'+';'.join(dict_evalue[motif])+'\n')
def add_motif(path_motif, motif, motif_list_filename):
motif_file = path_motif + '/motif/' + motif + '.meme'
motif_figure_file = path_motif + '/motif_figure/' + motif + '_nc.eps'
# motif_figure_file = path_motif + '/motif_figure/' + motif + '_nc.png'
# Meme-suite as to be installed for using = iupac2meme
iupac2_command = 'iupac2meme -dna ' + motif + ' > ' + motif_file
print(iupac2_command)
os.system(iupac2_command)
ceqlogo_command = 'ceqlogo -i ' + motif_file + ' -m ' + motif + ' -o ' + motif_figure_file + ' -f eps'
# ceqlogo_command = 'ceqlogo -i ' + motif_file + ' -m ' + motif + ' -o '+motif_figure_file+' -f png'
print('Run ' + ceqlogo_command)
os.system(ceqlogo_command)
def regroup_figures(path_motif, motif_list):
'''
Create SVG file with all motifs included
:param path_motif:
:param motif_list:
:return:
'''
print('Create PNG file with all motifs')
df_summary = pd.read_csv(path_motif + motif_list + '.txt', sep='\t')
#df_summary = df_summary.sort_values(by=['Motif'], ascending=[True])
svg_text = '<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<!-- Generator: Adobe Illustrator 15.0.0, SVG Export ' \
'Plug-In . SVG Version: 6.00 Build 0) -->\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\" \"' \
'http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<svg version=\"1.1\" id=\"Calque_1\" ' \
'xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" x=\"0px\" y=\"0px\"' \
' width=\"1300px\" height=\"'+str(len(df_summary.index)*100)+'px\" viewBox=\"0 0 1300 '\
+str(len(df_summary.index)*100)+'\" xml:space=\"preserve\">\n'
i=0
for index, row in df_summary.iterrows():
motif = row['Motif']
data = row['Data']
pvalue = row['P-value']
image_file = 'motif_figure/' + motif + '_nc.png'
new_row = '<image overflow=\"visible\" width=\"299\" height=\"176\" xlink:href=\"'+image_file+'\" ' \
'transform=\"matrix(0.5184 0 0 0.5184 10.00 '+ str(float(100*i))+')\">\n</image>\n'
image_file = 'motif_figure/' + motif + '_rc.png'
new_row += '<image overflow=\"visible\" width=\"299\" height=\"176\" xlink:href=\"' + image_file + '\" ' \
'transform=\"matrix(0.5184 0 0 0.5184 170.00 '+ str(float(100*i))+')\">\n</image>\n'
new_row += '<text transform=\"matrix(0.5184 0 0 0.5184 340 '+ str(float(50+100*i))+')\" font-family=' \
'\"\'MyriadPro-Regular\'\" font-size=\"40\">'+data+'</text>\n'
new_row += '<text transform=\"matrix(0.5184 0 0 0.5184 800 '+ str(float(50+100*i))+')\" font-family=' \
'\"\'MyriadPro-Regular\'\" font-size=\"40\">'+pvalue+'</text>\n'
svg_text += new_row
i+=1
svg_text += '</svg>\n'
with open(path_motif + motif_list + '.svg', 'w') as figure_file:
figure_file.write(svg_text)
print('Convert svg to png')
os.system('convert '+path_motif + motif_list + '.svg '+path_motif + motif_list + '.png')
def run_fimo(path_motif, path_script, exp_design_name, motif_list_filename):
motif_list = list()
with open(path_motif + motif_list_filename + '.txt', 'r') as motif_file:
motif_file.readline()
for row in motif_file:
motif_list.append(row.split('\t')[0].strip())
data_name = exp_design_name
with open(path_motif + 'Fimo.sh', 'w') as motif_sh:
for motif in motif_list:
motif_file = path_script + 'motif/' + motif + '.meme'
print(motif_file)
fasta_file = path_script + 'fasta/' + data_name + '.fasta'
background_file = path_script + 'results/' + data_name + '/background'
folder_fimo = path_script + 'fimo/' + motif + '_' + data_name
fimo_sh = 'fimo --parse-genomic-coord --verbosity 1 --thresh 1e-2 --max-stored-scores 1000000 ' \
'--oc ' + folder_fimo + ' --bgfile ' + background_file + ' ' + motif_file + ' ' + fasta_file
print(fimo_sh)
motif_sh.write(fimo_sh+'\n')
def run_centrimo(path_motif, path_script, exp_design_name, motif_list_filename, data_list_filename):
motif_list = list()
with open(path_motif + motif_list_filename + '.txt', 'r') as motif_file:
motif_file.readline()
for row in motif_file:
motif_list.append(row.split('\t')[0].strip())
data_list = list()
with open(path_motif + data_list_filename + '.txt', 'r') as data_file:
for row in data_file:
data_list.append(row.split('\t')[0].strip())
with open(path_motif + 'Centrimo.sh', 'w') as motif_sh:
for motif in motif_list:
motif_file = path_script + 'motif/' + motif + '.meme'
#print(motif_file)
for data_name in data_list:
fasta_file = path_script + 'fasta/' + data_name + '.fasta'
background_file = path_script + 'results/' + data_name + '/background'
folder_centrimo = path_script + 'centrimo/' + motif + '_' + data_name
centrimo_sh = 'centrimo -seqlen 150 -verbosity 1 -oc ' + folder_centrimo +\
' --bgfile ' + background_file + ' -score 5.0 '\
'-ethresh 10.0 ' + fasta_file + ' '+motif_file
print(centrimo_sh)
motif_sh.write(centrimo_sh + '\n')
def create_motif_table(path_motif, path_script, exp_design_name, motif_list_filename, data_list_filename):
motif_list = list()
with open(path_motif + motif_list_filename + '.txt', 'r') as motif_file:
motif_file.readline()
for row in motif_file:
motif_list.append(row.split('\t')[0].strip())
data_list = list()
with open(path_motif + data_list_filename + '.txt', 'r') as data_file:
for row in data_file:
data_list.append(row.split('\t')[0].strip())
fasta_to_length = dict()
fasta_seq = SeqIO.to_dict(SeqIO.parse(path_motif + 'fasta/'+ exp_design_name +'.fasta', "fasta"))
nb_peaks = len(fasta_seq.keys())
np_motif = np.zeros((nb_peaks, len(motif_list)))
df_motif_score = pd.DataFrame(np_motif, index=fasta_seq.keys(), columns=motif_list)
np_motif = np.zeros((nb_peaks, len(motif_list)))
df_motif_count = pd.DataFrame(np_motif, index=fasta_seq.keys(), columns=motif_list)
np_motif = np.ones((nb_peaks, len(motif_list)))
df_motif_pvalue = pd.DataFrame(np_motif, index=fasta_seq.keys(), columns=motif_list)
for motif in motif_list:
print(motif)
for data in data_list:
print(data)
df_motif_peaks = pd.read_csv(path_motif + 'fimo/' + motif + '_' + data + '/fimo.txt', index_col=0, sep='\t')
grouped = df_motif_peaks[['sequence name','score']].groupby('sequence name')
score = grouped.sum()
df_motif_score[motif] = score
counted = grouped.count()
df_motif_count[motif] = counted
# df_motif_pvalue[motif][peak] += pvalue
df_motif_score.to_csv(path_motif + motif_list_filename + '_score.txt', sep='\t')
df_motif_count.to_csv(path_motif + motif_list_filename + '_count.txt', sep='\t')
#df_motif_pvalue.to_csv(path_motif + exp_design_name + '_' + motif_list_filename + '_pvalue.txt', sep='\t')
def motif_vs_fasta(path_motif, path_script, exp_design_name, motif_list_filename, data_list_filename):
# read fasta summary
fasta_to_size = dict()
with open(path_motif + 'Fasta_Summary.txt', 'r') as fasta_summary:
for row in fasta_summary:
fasta_name = row.split('\t')[0]
fasta_size = row.split('\t')[1].strip()
if fasta_size != '0':
fasta_to_size[fasta_name] = fasta_size
# read list
motif_list = list()
with open(path_motif + motif_list_filename + '.txt', 'r') as motif_file:
motif_file.readline()
for row in motif_file:
motif_list.append(row.split('\t')[0].strip())
# read motif summary
df_motif_score = pd.read_csv(path_motif + motif_list_filename + '_score.txt', index_col=0, sep='\t')
df_motif_count = pd.read_csv(path_motif + motif_list_filename + '_count.txt', index_col=0, sep='\t')
# create motif vs fasta tables
nb_fasta = len(fasta_to_size.keys())
np_fasta = np.zeros((len(motif_list), nb_fasta))
df_motif_fasta_score = pd.DataFrame(np_fasta, index=motif_list, columns=fasta_to_size.keys())
np_fasta = np.zeros((len(motif_list), nb_fasta))
df_motif_fasta_count = pd.DataFrame(np_fasta, index=motif_list, columns=fasta_to_size.keys())
print(motif_list)
# go through all fasta file
for fasta_name, fasta_size in fasta_to_size.items():
fasta_peaks = SeqIO.to_dict(SeqIO.parse(path_motif+'fasta/'+fasta_name+'.fasta', 'fasta'))
#print(fasta_peaks.keys())
for motif in motif_list:
motif_score = df_motif_score.loc[fasta_peaks.keys(), motif].sum()
df_motif_fasta_score[fasta_name][motif] = float(motif_score)/float(fasta_size)
motif_count = df_motif_count.loc[fasta_peaks.keys(), motif].sum()
df_motif_fasta_count[fasta_name][motif] = float(motif_count)/float(fasta_size)
df_motif_fasta_score.to_csv(path_motif + motif_list_filename + '_fasta_score.txt', sep='\t')
df_motif_fasta_count.to_csv(path_motif + motif_list_filename + '_fasta_count.txt', sep='\t')
#exp_design_name = 'LivOld'
exp_design_name = 'LiverZT_MaxValues'
#exp_design_name = 'LivOld_MaxValues'
#exp_design_name = 'CecAm_MaxValues'
#path = '/pasteur/projets/policy01/m6aAkker/'
path = '/Volumes/m6aAkker/'
#path_motif = path + 'PeakDiffExpression/' + exp_design_name + '/Motif/'
path_motif = path + 'PeakDiffExpression/Motif/'
path_peaks = path + 'PeakDetection/Peaks/'
# if not os.path.isdir(path_motif + 'logs/'):
# os.mkdir(path_motif + 'logs/')
# if not os.path.isdir(path_motif + 'motif/'):
# os.mkdir(path_motif + 'motif/')
# if not os.path.isdir(path_motif + 'motif_figure/'):
# os.mkdir(path_motif + 'motif_figure/')
# if not os.path.isdir(path_motif + 'fimo/'):
# os.mkdir(path_motif + 'fimo/')
# if not os.path.isdir(path_motif + 'centrimo/'):
# os.mkdir(path_motif + 'centrimo/')
#path_script = path + 'PeakDiffExpression/' + exp_design_name + '/Motif/'
path_script = '/pasteur/projets/policy01/m6aAkker/' + 'PeakDiffExpression/' + exp_design_name + '/Motif/'
# get size fasta
#get_size_fasta(path_motif, exp_design_name)
# first look at log to see which software crashed
#parse_all_logs(path_motif+'/results/', exp_design_name)
# Parse meme results and extract all motifs
#parse_dreme(path_motif+'/results/', exp_design_name)
# regroup all motifs
#regroup_motifs(path_motif, exp_design_name)
#motif_list_filename = 'Motif_'+exp_design_name+'_count'
motif_list_filename = 'Motif_List'
# add new motif
# motif = 'RRACH'
#motif="NGGACN"
#add_motif(path_motif, motif, motif_list_filename)
#motif = 'NBCAN'
#add_motif(path_motif, motif, motif_list_filename)
# put all motifs figures together to filter the list
#regroup_figures(path_motif, motif_list_filename)
data_list_filename = 'list_data_' + exp_design_name
#data_list_filename = 'list_data_All'
# write sh file for running fimo
run_fimo(path_motif, path_script, exp_design_name, motif_list_filename)
# write sh file for running centrimo
#run_centrimo(path_motif, path_script, exp_design_name, motif_list_filename, data_list_filename)
# create a table grouping peak marginal score for each motif
#create_motif_table(path_motif, path_script, exp_design_name, motif_list_filename, data_list_filename)
# Count marginal scrore and marginal motif presence for every fasta file
#motif_vs_fasta(path_motif, path_script, exp_design_name, motif_list_filename, data_list_filename)