'jaspar_info_percentage','e_val','motif_score','motif_score_percentage',\
'failure_rate','success_rate','false_positive_rate','false_negative',\
'promoter_size','number_of_sequences','model','markov_order','iterations'\
,'stdev(avg_eval)','stdev(avg_score)','stdev(avg_pscore)',\
'stdev(avg_frate)','stdev(avg_srate)','stdev(avg_fprate)','stdev(avg_fn)'\
,sep=', ')
else:
#seq, motif,nsites,m_pos, m_strand,meme_wid,j_pos,j_strand,len(jpwm),p_val\
#,score,p_score,meme_eval,fpos,0,posdis,fl,overlap,overlap_p,p,n,m,o
print('file','sequence','motif','number_of_sites','meme_position', 'meme_strand'\
,'meme_width','j_position','j_strand','j_width','p_value','score',\
'score_percentage','e_value','false_pos','false_neg','positional_distance'\
,'fail','overlap','overlap percentage', 'promoter length',\
'number_of_sequences','model', 'markov_order','iterations',sep=', ')
jpwm = motiflib.read_JASPAR(arg.jasparfile)
bits, p_bits = motiflib.score_motifbit(jpwm)
for p in promoter:
for n in numseq:
for m in model:
#for o in markov_order:
if arg.condenseddata:
final = []
for r in range(iterations):
#(p,n,m,o,r)
#print(' ALL INFO !!!!!!!!!!!!!!!!')
#print('r',r)
promoter_file = memepipelib.generate_promoter_bg\
(arg.jasparfile,p,n,freq,background,r+1,arg.condenseddata)
#print('file',promoter_file)
#print('')
j_info, numjsites = motiflib.read_testmotif(promoter_file)
model = ['zoops', 'oops', 'anr']
jasparfiles = os.listdir(directory)
jasparfiles.sort()
print('file',
'length motif',
'information in bits',
'percentage bit score',
'performance',
'performance percentage',
'model',
sep=', ')
for fl in jasparfiles:
filepath = f'Jaspar/{fl}'
motif = motiflib.read_JASPAR(filepath)
bits, p_bits = motiflib.score_motifbit(motif)
for m in model:
modscore = []
modscorep = []
for i in range(arg.numiterations):
tmpfile = f'/tmp/testmotif{fl}_{i}.fa'
cmd = f'python3 markov_motifapalooza.py --jasparfile {filepath} \
--dnafile {arg.dnafile} --markov_order {o} --numseq {n} --seqlen {p} --freq {freq} > {tmpfile} '
os.system(cmd)
cmd = f'{arg.memepath} {tmpfile} -dna -markov_order {o} -mod {m}'
os.system(cmd)
memetxt = 'meme_out/meme.txt'
motifs, motif_stats = motiflib.memepwm(memetxt)
for mot in motifs:
performance = motiflib.local_motcompare(mot, motif)