def makePWMscorefiles(fastafiles, pwmfiles, destdir, both_strands=True):
for fastaf in fastafile:
thisseqname = fastaf.split('/')[-1].split('.')[0]
handle = open(fastaf, "r")
records = list(Bio.SeqIO.parse(handle, "fasta"))
handle.close()
thisseq = records[0].seq
print 'Doing sequence ', thisseqname, 'length=', len(thisseq)
for pwmf in pwmfiles:
thispwmname = pwmf.split('/')[-1]
print ' Doing pwm ', thispwmname
thispwm = MOODS.load_matrix(pwmf)
thispwmcomplement = MOODS.reverse_complement(thispwm)
print ' strand 1'
onestrandindexvector=getMOODSscore(thisseq, thispwm)
print ' strand 2'
otherstrandindexvecor=getMOODSscore(thisseq, thispwmcomplement)
print ' finding best score per bp'
bothstrandsindexvector = np.append( onestrandindexvector, otherstrandindexvecor, axis=0)
bestscorevector = getMaxPWMScore( bothstrandsindexvector, len(thispwm[0]))
for strandnbr in range(len(bothstrandsindexvector)):
print ' writing wiggle for strand', str(strandnbr)
vegardswritewiggle(bothstrandsindexvector[strandnbr,:], name=thispwmname, chr=thisseqname, destpath=destdir + '/' + 'start_index_score/'+ thispwmname+ '/strand_'+str(strandnbr))
print ' writing wiggle for bestscore'
vegardswritewiggle(bestscorevector, name=thispwmname, chr=thisseqname, destpath=destdir + '/' + 'best_score_in_window/'+thispwmname)
def getMOODSscore(seqfile, pwmfiles, both_strands=False):
handle = open(seqfile, "r")
records = list(Bio.SeqIO.parse(handle, "fasta"))
handle.close()
seq = records[0].seq
print 'len(seq)=', len(seq)
matrixlist = list()
for f in pwmfiles:
matrix = MOODS.load_matrix(f)
print 'pwm ', f, 'windowlength=', len(matrix[0])
matrixlist.append(matrix)
if both_strands:
matrixlist.append(
MOODS.reverse_complement(matrix)
) # both_strand option in MOODS returned a akward result.
print 'starting MOODS.search', datetime.now()
results = MOODS.search(seq,
matrixlist,
thresholds=1,
absolute_threshold=False)
print 'done MOODS.search', datetime.now()
reslist = []
for n in range(len(pwmfiles)):
thisind = n * (1 + both_strands)
reslist.append(vegardparseMOODSres(results[thisind], len(seq)))
if both_strands:
reslist[n] = np.append(reslist[n],
vegardparseMOODSres(results[thisind + 1],
len(seq)),
axis=0)
return (reslist)
def makePWMscorefiles(fastafiles, pwmfiles, destdir, both_strands=True):
for fastaf in fastafile:
### seqence only needed for length here. MOODS does this parsing again later but without reporting length.
thisseqname = fastaf.split('/')[-1].split('.')[0]
handle = open(fastaf, "r")
records = list(Bio.SeqIO.parse(handle, "fasta"))
handle.close()
thisseq = records[0].seq
print 'Doing sequence ', thisseqname, 'length=', len(thisseq)
for pwmf in pwmfiles:
thispwmname = pwmf.split('/')[-1]
thispwm = MOODS.load_matrix(pwmf)
print ' Doing MOODS both strands for pwm ', thispwmname, ', length=', len(
thispwm[0]), datetime.now()
onestrandsindexvector = getMOODSscore(fastaf, pwmf, len(thisseq))
print ' bp with no score (given ', NO_SCORE_VALUE, ') is ', (
onestrandsindexvector == NO_SCORE_VALUE).sum(), ' expected ', (
len(thispwm[0]) - 1)
print ' finding best score per bp, ', datetime.now()
bestscorevector = getMaxPWMScore(onestrandsindexvector,
len(thispwm[0])),
print ' writing wiggle for score per start index.', datetime.now()
vegardswritewiggle(onestrandsindexvector[0, ],
name=thispwmname,
chr=thisseqname,
destpath=destdir + '/' + 'start_index_score/' +
thispwmname)
print ' writing wiggle for bestscore. ', datetime.now()
vegardswritewiggle(bestscorevector[0],
name=thispwmname,
chr=thisseqname,
destpath=destdir + '/' +
'best_score_in_window/' + thispwmname)
def makePWMscorefiles(fastafiles, pwmfiles, destdir, both_strands=True):
for fastaf in fastafile:
### seqence only needed for length here. MOODS does this parsing again later but without reporting length.
thisseqname = fastaf.split('/')[-1].split('.')[0]
handle = open(fastaf, "r")
records = list(Bio.SeqIO.parse(handle, "fasta"))
handle.close()
thisseq = records[0].seq
print 'Doing sequence ', thisseqname, 'length=', len(thisseq)
for pwmf in pwmfiles:
thispwmname = pwmf.split('/')[-1]
thispwm = MOODS.load_matrix(pwmf)
print ' Doing MOODS both strands for pwm ', thispwmname, ', length=', len(thispwm[0]), datetime.now()
onestrandsindexvector = getMOODSscore(fastaf, pwmf, len(thisseq))
print ' bp with no score (given ', NO_SCORE_VALUE, ') is ', (onestrandsindexvector == NO_SCORE_VALUE).sum(), ' expected ', (len(thispwm[0])-1)
print ' finding best score per bp, ', datetime.now()
bestscorevector = getMaxPWMScore( onestrandsindexvector, len(thispwm[0])),
print ' writing wiggle for score per start index.', datetime.now()
vegardswritewiggle(onestrandsindexvector[0,], name=thispwmname, chr=thisseqname, destpath=destdir + '/' + 'start_index_score/'+ thispwmname)
print ' writing wiggle for bestscore. ', datetime.now()
vegardswritewiggle(bestscorevector[0], name=thispwmname, chr=thisseqname, destpath=destdir + '/' + 'best_score_in_window/'+thispwmname)
def getMOODSscore(seqfile, pwmfiles, both_strands=False):
handle = open(seqfile, "r")
records = list(Bio.SeqIO.parse(handle, "fasta"))
handle.close()
seq = records[0].seq
print 'len(seq)=',len(seq)
matrixlist=list()
for f in pwmfiles:
matrix = MOODS.load_matrix(f)
print 'pwm ', f , 'windowlength=', len(matrix[0])
matrixlist.append(matrix)
if both_strands:
matrixlist.append(MOODS.reverse_complement(matrix)) # both_strand option in MOODS returned a akward result.
print 'starting MOODS.search', datetime.now()
results = MOODS.search(seq, matrixlist, thresholds=1, absolute_threshold=False)
print 'done MOODS.search', datetime.now()
reslist=[]
for n in range(len(pwmfiles)):
thisind = n * (1 + both_strands)
reslist.append(vegardparseMOODSres( results[thisind] , len(seq)))
if both_strands:
reslist[n] = np.append( reslist[n] , vegardparseMOODSres( results[thisind+1] , len(seq)), axis=0)
return(reslist)
#datetime.now()
print 'running getMOODSscore', datetime.now()
indexscorematrix = getMOODSscore(fastafile,
pwmfiles,
both_strands=calculate_both_strands)
print 'finished getMOODSscore', datetime.now()
#datetime.now()
## for alle pwm.
## lage max array
## skrive ut 3 filer.
for n in range(len(pwmfiles)):
thisname = pwmfiles[n].split('/')[-1]
print 'making maxscpre for ', thisname, datetime.now()
thisscorematrix = indexscorematrix[n] #
thispwmlength = len(MOODS.load_matrix(pwmfiles[n])[0])
thismaxvector = getMaxPWMScore(thisscorematrix, thispwmlength)
print 'writing wiggle for ', thisname, datetime.now()
### best score file
vegardswritewiggle(thismaxvector,
name=thisname,
chr=seqname,
path=outputdir + '/' + 'best_score_in_window/' +
thisname)
for strandnbr in range(len(thisscorematrix)):
vegardswritewiggle(thisscorematrix[strandnbr, :],
name=thisname,
chr=seqname,
path=outputdir + '/' + 'start_index_score/' +
thisname + '/strand_' + str(strandnbr))
def ProcessCLI(args):
outputDirectory = '/N/u/jubudka/Mason/BindingFiles/'
weightMatrixDirectory = '/N/u/jubudka/Mason/PWMsmall/'
sequencesFileName = 'FASTA_All_Merged_Encode.fasta'
p_val = 0.0001
print args
for i in xrange(len(args)):
if args[i] == "-f":
sequencesFileName = args[i+1]
print "Fasta file is: ", sequencesFileName
elif args[i] == "-p":
weightMatrixDirectory = args[i+1]
print "PWM file is: ", weightMatrixDirectory
elif args[i] == "-o":
outputDirectory = args[i+1]
print "Output file is: ", outputDirectory
elif args[i] == "-t":
p_val = float(args[i+1])
if not os.path.exists(outputDirectory):
os.makedirs(outputDirectory)
# file for saving average score stuff
# load position weight matrices
# order is A C G T
sequences = {}
seqIDs = []
current_sequence = ''
sequencesFile = open(sequencesFileName)
aCount = 0
cCount = 0
gCount = 0
tCount = 0
totalLength = 0
for lines in sequencesFile:
line = lines.strip()
if line == '':
continue
if (line[0].startswith('>')):
seqIDs.append(line[1:])
#add previous sequence to dictionary
#create the reverse complement and add to dictionary
#perform nucleotide counting
#reset sequence to '' for next fasta sequence
if (len(current_sequence) > 0):
upper_current_sequence = current_sequence.upper()
seqID = seqIDs.pop(0)
sequences[seqID + ' ' + 'p'] = upper_current_sequence
reverseSequence = reverse_complement(upper_current_sequence)
sequences[seqID + ' ' + 'm'] = reverseSequence
aCount = aCount + upper_current_sequence.count('A')
cCount = cCount + upper_current_sequence.count('C')
gCount = gCount + upper_current_sequence.count('G')
tCount = tCount + upper_current_sequence.count('T')
totalLength = totalLength + len(current_sequence)
current_sequence = ''
else:
current_sequence += line
upper_current_sequence = current_sequence.upper()
seqID = seqIDs.pop(0)
sequences[seqID + ' ' + 'p'] = upper_current_sequence
reverseSequence = reverse_complement(upper_current_sequence)
sequences[seqID + ' ' + 'm'] = reverseSequence
aCount = aCount + upper_current_sequence.count('A')
cCount = cCount + upper_current_sequence.count('C')
gCount = gCount + upper_current_sequence.count('G')
tCount = tCount + upper_current_sequence.count('T')
totalLength = totalLength + len(current_sequence)
aContent = aCount/float(totalLength)
cContent = cCount/float(totalLength)
gContent = gCount/float(totalLength)
tContent = tCount/float(totalLength)
backgroundScores = {'A':aContent, 'C':cContent, 'G':gContent, 'T':tContent}
bg = [backgroundScores['A'], backgroundScores['C'], backgroundScores['G'], backgroundScores['T']]
print bg
matrix_names = [filename for filename in os.listdir(weightMatrixDirectory) if filename[-4:] == '.pfm']
pseudocount = 1
matrices = [MOODS.load_matrix(weightMatrixDirectory + filename) for filename in matrix_names]
matrices = [MOODS.count_log_odds(matrix, bg, pseudocount) for matrix in matrices]
thresholds = [MOODS.threshold_from_p(matrix, bg, p_val) for matrix in matrices]
for (matrix, matrix_name, threshold) in zip(matrices, matrix_names, thresholds):
motifLength = len(matrix[0])
if motifLength >= 18:
matrix_mapper_long(matrix, matrix_name, threshold, outputDirectory, sequences)
continue
else:
matrix_mapper(matrix, matrix_name, threshold, outputDirectory, sequences)
print "Finished"
####### running MOODS algorithm on sequence with all pwm files.
#datetime.now()
print 'running getMOODSscore', datetime.now()
indexscorematrix = getMOODSscore(fastafile, pwmfiles, both_strands=calculate_both_strands)
print 'finished getMOODSscore', datetime.now()
#datetime.now()
## for alle pwm.
## lage max array
## skrive ut 3 filer.
for n in range(len(pwmfiles)):
thisname = pwmfiles[n].split('/')[-1]
print 'making maxscpre for ',thisname, datetime.now()
thisscorematrix = indexscorematrix[n] #
thispwmlength = len(MOODS.load_matrix(pwmfiles[n])[0])
thismaxvector = getMaxPWMScore(thisscorematrix, thispwmlength)
print 'writing wiggle for ',thisname, datetime.now()
### best score file
vegardswritewiggle(thismaxvector, name=thisname, chr=seqname, path=outputdir + '/' + 'best_score_in_window/'+thisname)
for strandnbr in range(len(thisscorematrix)):
vegardswritewiggle(thisscorematrix[strandnbr,:], name=thisname, chr=seqname, path=outputdir + '/' + 'start_index_score/'+ thisname+ '/strand_'+str(strandnbr))
temp1 = getMaxPWMScore(temp1, thispwmlength)
