def main():
parser = argparse.ArgumentParser(description='')
parser.add_argument("-i",
"--input",
dest="infile",
required=True,
help="input file")
parser.add_argument("-n",
"--name",
dest="name",
required=True,
help="name of motif")
args = parser.parse_args()
name = args.name
with open(args.infile) as handle:
m = motifs.read(handle, "pfm")
pwm = m.counts.normalize(pseudocounts={
"A": 0.6,
"C": 0.4,
"G": 0.4,
"T": 0.6
})
print(">{0}\t{1}".format(str(pwm.consensus), name))
for i in range(len(pwm[0])):
print("{0:f}\t{1:f}\t{2:f}\t{3:f}".format(pwm[0][i], pwm[1][i],
pwm[2][i], pwm[3][i]))
def process_data(data, data_type='counts', seq_type='dna'):
if data_type == 'counts':
pfm, total = count_to_pfm(data)
ic = calc_relative_information(pfm, total)
elif data_type in ['fasta', 'stockholm']:
#motif, ic = read_alignment(data, data_type, seq_type)
#pfm = motif.counts.normalize(pseudocounts=1)
data, total = read_alignment(data, data_type, seq_type)
pfm, _ = count_to_pfm(data)
ic = calc_relative_information(pfm, total)
elif data_type in [
'alignace', 'meme', 'mast', 'transfac', 'pfm', 'sites', 'jaspar'
]:
if data_type in ['jaspar', 'transfac']:
motif = motifs.parse(open(data, 'r'), data_type.upper())[0]
pfm = dict(motif.counts.normalize())
total = sum(list(motif.counts.values())[0])
else:
motif = motifs.read(open(data, 'r'), data_type)
try:
pfm = motif.counts.normalize(psuedocounts=1)
except:
pfm = motif.counts.normalize()
total = motif.counts
ic = calc_relative_information(pfm, total)
return (format_matrix(pfm), format_matrix(ic))
def load_motifs(motif_dir, pseudocounts=0.05, key='full'):
'''
read in motifs; motifs have to be in jaspar format as below:
>MA0002.2 RUNX1
A [ 287 234 123 57 0 87 0 17 10 131 500 ]
C [ 496 485 1072 0 75 127 0 42 400 463 158 ]
G [ 696 467 149 7 1872 70 1987 1848 251 81 289 ]
T [ 521 814 656 1936 53 1716 13 93 1339 1325 1053 ]
Parameters:
motif_dir: folder that contains motif files; one file for individual motif
pseudocounts: w.r.t. position weight matrix, the probability adding to every nucleotide
key: specify the way to name the motifs in the output dictionary
options: 'full' (default), 'id'
'''
motif_dict = {}
nuc = ['A', 'C', 'G', 'T']
for mf in os.listdir(motif_dir):
with open(motif_dir + mf) as f:
m = motifs.read(f, 'jaspar')
counts = np.array([m.counts[n] for n in nuc])
avg_counts = counts.sum(axis=0).mean()
m.pseudocounts = avg_counts*pseudocounts
m.background = None
if key == 'full':
motif_dict[m.name+'$'+m.matrix_id] = m
elif key == 'id':
motif_dict[m.matrix_id] = m
return motif_dict
def __init__(self,matrixfile=None,pfmdir=None):
annotationmatrixfile= 'SaccCereAnnotation.txt'
matrixfile='SaccCereTFMATRIX.txt'
annotationmatrixfile= path.join('Data', annotationmatrixfile)
matrixfile= path.join('Data', matrixfile)
TFmatrixFile= path.join(current_directory, matrixfile)
annotationfile= path.join(current_directory, annotationmatrixfile)
medline= open(annotationfile, 'r')
medlinematrix = csv.reader(medline, dialect='excel-tab')
self.medline_dict= {}
for row in medlinematrix:
if row[1] == 'medline':
self.medline_dict[row[0]]= row[2]
if pfmdir is None:
pfmdir="SaccCerePFMFlatFileDir"
pfm_folder= path.join(current_directory,'Data', pfmdir)
else:
pfm_folder=pfmdir
TFmatrixReader= open(TFmatrixFile, 'r')
TFmatrix = csv.reader(TFmatrixReader, dialect='excel-tab')
self.motif_dict = dict()
for row in TFmatrix:
tf_accession= row[0]
common_name= row[4]
tf_pfm= "{}.{}.pfm".format(row[2],row[3])
filename= path.join('Data',pfm_folder, tf_pfm)
with open(filename, 'r') as handle:
self.motif_dict[common_name] = motifs.read(handle,'pfm')
try:
sgdid= idconverter.getgene(common_name).SGDID
self.motif_dict[sgdid]= self.motif_dict.pop(common_name)
self.motif_dict[sgdid].medline= self.medline_dict[tf_accession]
except Exception:
self.motif_dict.pop(common_name, None)
continue
def jaspar_to_pwm(output_dir="./"):
"""
For each taxon, this function reformats all profiles from JASPAR to
PWMScan format.
"""
# Initialize
# perl_script = os.path.join(os.path.dirname(os.path.realpath(__file__)),
# "jasparconvert.pl")
# For each taxon...
for taxon in taxons:
# Initialize
taxon_dir = os.path.join(os.path.abspath(output_dir), taxon)
# For each profile...
for f in os.listdir(taxon_dir):
# Skip non-JASPAR profiles
if not f.endswith(".jaspar"):
continue
# JASPAR to PWMScan
with open(os.path.join(taxon_dir, f)) as handle:
m = motifs.read(handle, "jaspar")
m.pseudocounts = motifs.jaspar.calculate_pseudocounts(m)
pwm = list(map(list, zip(*[m.pssm[nt] for nt in "ACGT"])))
pwm_file = os.path.join(taxon_dir, f"{f[:8]}.pwm")
if not os.path.exists(pwm_file):
with open(pwm_file, "w") as handle:
for i in pwm:
s = " ".join(
["{:7d}".format(round(j * 100)) for j in i])
handle.write("%s\n" % s)
def motif2pssm(path2motif, format):
if format == "ppm":
ppm = np.loadtxt(path2motif)
print "PPM:"
print ppm
print ""
pssm = np.log2((ppm + 1E-9) / 0.25)
return pssm
else:
with open(path2motif) as handle:
m = motifs.read(handle, format)
pfm = m.counts
print "PFM:"
print pfm
ppm = pfm.normalize(pseudocounts=C_PSEUDOCOUNTS)
print "PPM:"
print ppm
pssm = ppm.log_odds(background=C_BACKGROUND)
np_pssm = np.zeros(shape=(4, pssm.length))
for i, nt in enumerate(['A', 'C', 'G', 'T']):
np_pssm[i] = pssm[nt]
return np_pssm
def read_motif(motif):
"""
motif: a single .fm file within the directory.
Returns a motif.jaspar.Motif (the motif matrix) from the .fm file.
"""
return motifs.read(open(motif), "pfm")
def readMotifFile(motifPath):
with open(motifPath) as f:
m = motifs.read(f, 'jaspar')
name = m.name
matrix = [m.pwm['A'], m.pwm['C'], m.pwm['G'], m.pwm['T']]
matrix = np.array(matrix).T
return (name, np.array(matrix), m.matrix_id)
def __init__(self, input_file_name, pseudocounts, precision, fpr,
thresholds):
"""
Initializes Motif.
Variables:
pfm -- Position Frequency Matrix.
pwm -- Position Weight Matrix.
pssm -- Position Specific Scoring Matrix.
threshold -- Motif matching threshold.
len -- Length of the motif.
max -- Maximum PSSM score possible.
is_palindrome -- True if consensus is biologically palindromic.
"""
# Initializing error handler
err = ErrorHandler()
# Initializing name
self.name = ".".join(basename(input_file_name).split(".")[:-1])
repository = input_file_name.split("/")[-2]
# Creating PFM & PWM
input_file = open(input_file_name, "r")
self.pfm = motifs.read(input_file, "pfm")
self.pwm = self.pfm.counts.normalize(pseudocounts)
input_file.close()
self.len = len(self.pfm)
# Creating PSSM
background = {'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25}
self.pssm = self.pwm.log_odds(background)
self.pssm_list = [self.pssm[e] for e in ["A", "C", "G", "T"]]
self.max = self.pssm.max
# Evaluating threshold
try:
if pseudocounts != 0.1 or precision != 10000:
raise ValueError()
self.threshold = thresholds.dict[repository][self.name][fpr]
except Exception:
err.throw_warning(
"DEFAULT_WARNING",
add_msg="Parameters not matching pre-computed Fpr data. "
"Recalculating (might take a while)..")
try:
distribution = self.pssm.distribution(background=background,
precision=precision)
except Exception:
err.throw_error("MM_PSEUDOCOUNT_0")
self.threshold = distribution.threshold_fpr(fpr)
# Evaluating if motif is palindromic
if str(self.pfm.consensus) == str(
self.pfm.consensus.reverse_complement()):
self.is_palindrome = True
else:
self.is_palindrome = False
def yield_motifs(path):
with open(path) as handle:
for key, lines in groupby(handle, methodcaller('startswith', '>')):
if key:
name = lines.next().strip().split()[-1].lower()
else:
tmp = ''.join(lines)
mot = motifs.read(StringIO(tmp), 'pfm')
yield name, mot
yield name+'-R', true_motif_rev_complement(mot)
def MotifToBP(motif,name):
motifStr = '>' + name + '\n'
motifStr += 'A ' + str(motif['PWM']['A']).replace(',','') + '\n'
motifStr += 'C ' + str(motif['PWM']['C']).replace(',','') + '\n'
motifStr += 'G ' + str(motif['PWM']['G']).replace(',','') + '\n'
motifStr += 'T ' + str(motif['PWM']['T']).replace(',','') + '\n'
handle = StringIO(motifStr)
motif = motifs.read(handle, 'jaspar')
return motif
def pearsonpwm(
pwm1,
pwm2): #this function computes the Pearson coefficient between 2 pwm
cisbpmat = motifs.read(open(pwm1), "pfm")
cisbpmat.pseudocounts = 3.0
pwmnp1 = (np.loadtxt(pwm1, skiprows=1))
tf1 = cisbpmat.pssm
cisbpmat2 = motifs.read(open(pwm2), "pfm")
cisbpmat2.pseudocounts = 3.0
pwmnp2 = (np.loadtxt(pwm2, skiprows=1))
tf2 = cisbpmat2.pssm
distance, offset = tf2.dist_pearson(tf1)
return [pwm1, 1 - distance,
math.fabs(offset),
len(np.transpose(pwmnp1))], [
pwm2, 1 - distance,
math.fabs(offset),
len(np.transpose(pwmnp2))
]
def pfm(self):
s = ''
for l in ['a', 'c', 'g', 't']:
for n in self.PFM[l]:
s += str(n) + ' '
s += '\n'
sio = StringIO(s)
m = motifs.read(sio, 'pfm')
return m
def __init__(self, input_file_name):
input_file = open(input_file_name,"r")
self.pfm = motifs.read(input_file, "pfm")
self.pwm = self.pfm.counts.normalize(0.0001)
input_file.close()
self.len = len(self.pfm)
background = {'A':0.25,'C':0.25,'G':0.25,'T':0.25}
self.pssm = self.pwm.log_odds(background)
self.pssm_list = [self.pssm[e] for e in ["A","C","G","T"]]
self.max = self.pssm.max
self.min = self.pssm.min
def motif_reader(path_name):
motif_list = []
for filename in os.listdir(path_name):
print(filename)
with open(path_name + filename) as handle:
word = motifs.read(handle, "pfm")
handle.close()
motif = str(word.consensus)
print(motif)
motif_list.append(motif)
return motif_list
def read_jaspar_motif_file(motifPath, pseudocount):
'''
reads jaspar motif file
inputs: path to a jaspar motif file
outputs: a tuple representing a motif
'''
with open(motifPath) as f:
m = motifs.read(f, 'jaspar')
default_pseudocount = motifs.jaspar.calculate_pseudocounts(m)
scaled_pseudocount = pseudocount/0.01 * default_pseudocount['A']
m.pseudocounts = int(scaled_pseudocount)
return (m.name, m)
def __init__(self, input_file_name, pseudocounts, precision, fpr, thresholds):
"""
Initializes Motif.
Variables:
pfm -- Position Frequency Matrix.
pwm -- Position Weight Matrix.
pssm -- Position Specific Scoring Matrix.
threshold -- Motif matching threshold.
len -- Length of the motif.
max -- Maximum PSSM score possible.
is_palindrome -- True if consensus is biologically palindromic.
"""
# Initializing error handler
err = ErrorHandler()
# Initializing name
self.name = ".".join(basename(input_file_name).split(".")[:-1])
repository = input_file_name.split("/")[-2]
# Creating PFM & PWM
input_file = open(input_file_name, "r")
self.pfm = motifs.read(input_file, "pfm")
self.pwm = self.pfm.counts.normalize(pseudocounts)
input_file.close()
self.len = len(self.pfm)
# Creating PSSM
background = {'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25}
self.pssm = self.pwm.log_odds(background)
self.pssm_list = [self.pssm[e] for e in ["A", "C", "G", "T"]]
self.max = self.pssm.max
# Evaluating threshold
try:
if pseudocounts != 0.1 or precision != 10000:
raise ValueError()
self.threshold = thresholds.dict[repository][self.name][fpr]
except Exception:
err.throw_warning("DEFAULT_WARNING", add_msg="Parameters not matching pre-computed Fpr data. "
"Recalculating (might take a while)..")
try:
distribution = self.pssm.distribution(background=background, precision=precision)
except Exception:
err.throw_error("MM_PSEUDOCOUNT_0")
self.threshold = distribution.threshold_fpr(fpr)
# Evaluating if motif is palindromic
if str(self.pfm.consensus) == str(self.pfm.consensus.reverse_complement()):
self.is_palindrome = True
else:
self.is_palindrome = False
def read_pfm(filename):
"""Facilitates readings a Bio.motif object with set parameters. The output is a Bio.motif object that can quickly be trasnformed to a
PWM, or a PSSM using the associated arguments (.pwm , .pssm).
The expected PFM file format can be found in 'docs/ex_pfmfile.txt'
Additional information can be found on the Biopython motifs page"""
with open(filename, "r") as handle:
motif = motifs.read(handle, "pfm")
motif.pseudocounts = .25
motif.background = {'A': 0.3, 'C': 0.2, 'G': 0.2, 'T': 0.3}
return motif
def __init__(self, input_file_name):
# Standardize input file to be only the nucleotide frequencies
input_file = open(input_file_name,"r")
self.pfm = motifs.read(input_file, "pfm")
self.pwm = self.pfm.counts.normalize(0.1)
input_file.close()
self.len = len(self.pfm)
background = {'A':0.25,'C':0.25,'G':0.25,'T':0.25}
self.pssm = self.pwm.log_odds(background)
self.pssm_list = [self.pssm[e] for e in ["A","C","G","T"]]
self.max = self.pssm.max
self.min = self.pssm.min
def get_motifs(self, transcription_factor):
motifs = []
# Try Jaspar
JASPAR_dir = '../data/preprocess/JASPAR/'
for f in os.listdir(JASPAR_dir):
if transcription_factor.upper() in f.upper():
with open(os.path.join(JASPAR_dir, f)) as handle:
motif = read(handle, 'pfm')
print "motif found in JASPAR", f
motifs.append(motif)
# Try SELEX
SELEX_dir = '../data/preprocess/SELEX_PWMs_for_Ensembl_1511_representatives/'
for f in os.listdir(SELEX_dir):
if f.upper().startswith(transcription_factor.upper()):
with open(os.path.join(SELEX_dir, f)) as handle:
motif = read(handle, 'pfm')
print "motif found in SELEX", f
motifs.append(motif)
# Try Factorbook
return motifs
def read_motif(motif_filename, verb=0):
"""Reads a motif as a collection of sites from a file
Reads a motif and uses the biopython.motifs class to store it. If the motif
is in FASTA format, it uses the parser directly. Otherwise, it loads and
reads a concatenated text file and creates the motif.
File type is determined by extension:
* FASTA for .fas, .fasta and .fa files
* One-per-line text file otherwise
Input:
* The motif filename; required
* Verbose mode (default=0)
Returns:
* the read motif
"""
#create file handler for reading file
try:
motif_file = open(motif_filename, "r")
except (IOError, OSError) as file_open_exception:
print "*** The file name provided:", motif_filename, " does not exist"
print "*** Error: ", file_open_exception.errno, " - ",\
file_open_exception.strerror
sys.exit()
#Figure out file type based on extension, read sites and create motif
extension = motif_filename.split('.')[-1]
if extension not in ['fas', 'fasta', 'fa']:
if verb:
print 'Reading motif... raw text sequence mode assumed \
(one site per line, not FASTA parsing)'
sites = []
for line in motif_file:
sites.append(Seq(line.rstrip('\n\r'), IUPAC.unambiguous_dna))
mot = motifs.create(sites)
if verb: print mot.degenerate_consensus
else:
if verb: print 'Reading motif... attempting to parse FASTA file'
mot = motifs.read(motif_file, 'sites')
motif_file.close()
return mot
def get_observed_result(self, protein):
""" Find the DNA binding motif for a given protein
Args:
protein (:obj:`models.ProteinSubunit`): protein subunit to find data for
Returns:
:obj:`list` of :obj:`data_model.Observable`: list of observables
"""
versions = self.get_DNA_by_protein(protein)
index = 0
observed_result = []
for motif in versions:
binding_matrix = []
for position in motif.all():
binding_matrix.append([
position.frequency_a, position.frequency_c,
position.frequency_g, position.frequency_t
])
binding_matrix = map(list, zip(*binding_matrix))
self.cache_dirname = tempfile.mkdtemp()
with open(self.cache_dirname + '/data.pfm', 'w') as pfm:
for items in binding_matrix:
writer = csv.writer(pfm, delimiter='\t')
writer.writerow(items)
m = motifs.read(open(self.cache_dirname + '/data.pfm'), 'pfm')
dna_specie = data_model.DnaSpecie(binding_matrix=m.counts,
sequence=str(m.counts.consensus))
metadata = self.metadata_dump(motif.all()[0].dataset)
observed_result.append(
data_model.ObservedSpecie(specie=dna_specie,
metadata=metadata))
for position in motif.all():
observed_result[index].specie.cross_references = data_model.Resource(namespace ='pubmed',\
id = position.dataset._metadata.resource[0]._id),
break
shutil.rmtree(self.cache_dirname)
index += 1
return observed_result
def get_protein_by_DNA_sequence(self,
sequence,
select=models.ProteinSubunit):
"""
NOTE: Currently there are no Gene objects in common schema models. When added this
query will be updated to input models.Gene and output data_model.ProteinSpecie
Args:
sequence (:obj:`data_model.DnaSpecie.sequence`): sequence of DNA segment
Returns:
:obj:`list` of :obj:`tuple`: Returns the query for a protein, sequence position, and score
"""
#TODO: Make more efficient. Add gene location filter
ans = []
size = len(sequence)
all_matricies = self.data_source.session.query(
models.DNABindingDataset).all()
all_sequences = []
for matricies in all_matricies:
if len(matricies.dna_binding_data) == size:
binding_matrix = []
for position in matricies.dna_binding_data:
binding_matrix.append([
position.frequency_a, position.frequency_c,
position.frequency_g, position.frequency_t
])
binding_matrix = map(list, zip(*binding_matrix))
self.cache_dirname = tempfile.mkdtemp()
with open(self.cache_dirname + '/data.pfm', 'w') as pfm:
for items in binding_matrix:
writer = csv.writer(pfm, delimiter='\t')
writer.writerow(items)
m = motifs.read(open(self.cache_dirname + '/data.pfm'), 'pfm')
my_seq = Seq(sequence, IUPAC.unambiguous_dna)
##TODO: Include selective threshold
# distribution = m.pssm.distribution().threshold_paster()
# print distribution.threshold_paster()
for position, score in m.pssm.search(my_seq, threshold=2):
ans.append((matricies.subunit, position, score))
return ans
def read_motif(motif_filename, verb=0):
"""Reads a motif as a collection of sites from a file
Reads a motif and uses the biopython.motifs class to store it. If the motif
is in FASTA format, it uses the parser directly. Otherwise, it loads and
reads a concatenated text file and creates the motif.
File type is determined by extension:
* FASTA for .fas, .fasta and .fa files
* One-per-line text file otherwise
Input:
* The motif filename; required
* Verbose mode (default=0)
Returns:
* the read motif
"""
#create file handler for reading file
try:
motif_file = open(motif_filename,"r")
except (IOError, OSError) as file_open_exception:
print "*** The file name provided:", motif_filename, " does not exist"
print "*** Error: ", file_open_exception.errno, " - ",\
file_open_exception.strerror
sys.exit()
#Figure out file type based on extension, read sites and create motif
extension = motif_filename.split('.')[-1]
if extension not in ['fas', 'fasta', 'fa']:
if verb: print 'Reading motif... raw text sequence mode assumed \
(one site per line, not FASTA parsing)'
sites = []
for line in motif_file:
sites.append(Seq(line.rstrip('\n\r'),IUPAC.unambiguous_dna))
mot = motifs.create(sites)
if verb: print mot.degenerate_consensus
else:
if verb: print 'Reading motif... attempting to parse FASTA file'
mot = motifs.read(motif_file,'sites')
motif_file.close()
return mot
def _get_profiles(profiles_dir, latest=False, profile=[], taxon=taxons):
# Initialize
profiles = []
profiles_dict = {}
# For each taxon...
for t in taxon:
# Initialize
taxon_dir = os.path.join(os.path.abspath(profiles_dir), t)
# For each profile...
for profile_file in sorted(os.listdir(taxon_dir), reverse=True):
# Skip wrong profiles
if len(profile) > 0:
if profile_file[:8] not in profile:
continue
# Load profile
with open(os.path.join(taxon_dir, profile_file)) as f:
p = motifs.read(f, "jaspar")
# Initialize key
key = profile_file[:6]
profiles_dict.setdefault(key, [])
# Skip profile if only using the latest version of each profile
if latest:
if len(profiles_dict[key]) == 1:
continue
# Add profile
profiles_dict[key].append(p)
# Create list of profiles
for value_list in profiles_dict.values():
for p in value_list:
profiles.append(p)
return (profiles)
def parse_cisBP_pwm(self):
pwms_original_dir = os.path.join(self.cisBP_rna_dir, "pwms_all_motifs")
pwms_jaspar_dir = os.path.join(self.cisBP_rna_dir, "pwms_all_motifs",
"jaspar")
pwms_info = os.path.join(self.cisBP_rna_dir,
"RBP_information_all_motifs.txt")
df = pd.read_csv(pwms_info, sep='\t', header=0)
if not os.path.isdir(pwms_jaspar_dir):
os.mkdir(pwms_jaspar_dir)
if len(os.listdir(pwms_jaspar_dir)) == 0:
self.create_jaspar_files(pwms_original_dir, pwms_jaspar_dir)
for file in os.listdir(pwms_jaspar_dir):
with open(os.path.join(pwms_jaspar_dir, file)) as handle:
motif_ID = os.path.splitext(file)[0]
pwm = motifs.read(handle, "pfm")
if not motif_ID in self.pwms_dict:
self.pwms_dict[motif_ID] = pwm
handle.close()
def from_fasta(fasta, motifid, name=None):
"""
Create motif from fasta.
Will use captital letters as motif sites (see JASPAR sites format).
Parameters:
fasta (string): Path to fasta file.
motifid (string): Unique id of the motif.
name (string): Name of the motif. Defaults to 'None'.
Returns:
OneMotif object
"""
with open(fasta) as handle:
motif = motifs.read(handle, "sites")
return OneMotif(
motifid=motifid,
counts=[motif.counts[base] for base in ["A", "C", "G", "T"]],
name=name)
def get_names(output_dir="./"):
"""
This function extracts the name of each JASPAR profile and saves them
in a JSON file.
"""
# Initialize
names = {}
# Skip if already done
json_file = os.path.join(output_dir, "names.json")
if not os.path.exists(json_file):
# For each taxon...
for taxon in taxons:
# Initialize
taxon_dir = os.path.join(os.path.abspath(output_dir), taxon)
# For each profile...
for f in os.listdir(taxon_dir):
# Skip non-JASPAR profiles
if not f.endswith(".jaspar"):
continue
# Get profile name
with open(os.path.join(taxon_dir, f)) as handle:
m = motifs.read(handle, "jaspar")
if m.name.startswith(m.matrix_id):
name = m.name[len(m.matrix_id) + 1:]
else:
name = m.name
names.setdefault(m.matrix_id, name)
# Write JSON
with open(json_file, "w") as handle:
json.dump(names, handle, sort_keys=True, indent=4)
def line_plot(arguments):
(mpbs_name, num_fp, signal_1, signal_2, factor1, factor2, condition1, condition2,
pwm_dict, output_location, window_size, standardize) = arguments
mpbs_name = mpbs_name.replace("(", "_")
mpbs_name = mpbs_name.replace(")", "")
mean_signal_1 = (signal_1 / num_fp) / factor1
mean_signal_2 = (signal_2 / num_fp) / factor2
# output signal
signal_fname = os.path.join(output_location, "{}.txt".format(mpbs_name))
with open(signal_fname, "w") as f:
f.write(condition1 + "\t" + condition2 + "\n")
for i in range(window_size):
f.write(str(mean_signal_1[i]) + "\t" + str(mean_signal_2[i]) + "\n")
if standardize:
mean_signal_1, mean_signal_2 = standard(mean_signal_1, mean_signal_2)
# Output PWM and create logo
pwm_fname = os.path.join(output_location, "{}.pwm".format(mpbs_name))
pwm_file = open(pwm_fname, "w")
for e in ["A", "C", "G", "T"]:
pwm_file.write(" ".join([str(int(f)) for f in pwm_dict[e]]) + "\n")
pwm_file.close()
logo_fname = os.path.join(output_location, "{}.logo.eps".format(mpbs_name))
pwm = motifs.read(open(pwm_fname), "pfm")
pwm.weblogo(logo_fname, format="eps", stack_width="large", stacks_per_line=str(window_size),
color_scheme="color_classic", unit_name="", show_errorbars=False, logo_title="",
show_xaxis=False, xaxis_label="", show_yaxis=False, yaxis_label="",
show_fineprint=False, show_ends=False)
start = -(window_size / 2)
end = (window_size / 2) - 1
x = np.linspace(start, end, num=window_size)
plt.close('all')
fig, ax = plt.subplots()
ax.plot(x, mean_signal_2, color='red', label=condition2)
ax.plot(x, mean_signal_1, color='blue', label=condition1)
ax.text(0.15, 0.9, 'n = {}'.format(num_fp), verticalalignment='bottom', horizontalalignment='right',
transform=ax.transAxes, fontweight='bold')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_position(('outward', 15))
ax.tick_params(direction='out')
ax.set_xticks([start, 0, end])
ax.set_xticklabels([str(start), 0, str(end)])
min_signal = min(min(mean_signal_1), min(mean_signal_2))
max_signal = max(max(mean_signal_1), max(mean_signal_2))
ax.set_yticks([min_signal, max_signal])
ax.set_yticklabels([str(round(min_signal, 2)), str(round(max_signal, 2))], rotation=90)
ax.set_title(mpbs_name, fontweight='bold')
ax.set_xlim(start, end)
ax.set_ylim([min_signal, max_signal])
ax.legend(loc="upper right", frameon=False)
ax.spines['bottom'].set_position(('outward', 70))
figure_name = os.path.join(output_location, "{}.line.eps".format(mpbs_name))
fig.tight_layout()
fig.savefig(figure_name, format="eps", dpi=300)
# Creating canvas and printing eps / pdf with merged results
output_fname = os.path.join(output_location, "{}.eps".format(mpbs_name))
c = pyx.canvas.canvas()
c.insert(pyx.epsfile.epsfile(0, 0, figure_name, scale=1.0))
c.insert(pyx.epsfile.epsfile(0.45, 0.8, logo_fname, width=16.5, height=3))
c.writeEPSfile(output_fname)
os.system(" ".join(["epstopdf", output_fname]))
os.remove(figure_name)
os.remove(logo_fname)
os.remove(output_fname)
os.remove(pwm_fname)
output_logos_dir = path.join(curr_dir, "logos")
if not path.exists(output_logos_dir):
mkdir(output_logos_dir)
for dir_name, subdir_list, file_list in walk(path.join(curr_dir, "motifs")):
base_name = path.basename(dir_name)
if ((options.hocomoco and base_name == "hocomoco") or
(options.jaspar_vertebrates and base_name == "jaspar_vertebrates")
or (options.uniprobe_primary and base_name == "uniprobe_primary")
or
(options.uniprobe_secondary and base_name == "uniprobe_secondary")):
output_dir = path.join(curr_dir, "logos", base_name)
if not path.exists(output_dir):
mkdir(output_dir)
else:
continue
print("Creating logos for " + base_name)
for pwm_file_name in file_list:
pwm_full_file_name = path.join(dir_name, pwm_file_name)
if pwm_file_name.split(".")[-1] != "pwm":
continue
pwm_file = open(pwm_full_file_name, "r")
logo_file_name = path.join(
output_dir, ".".join(pwm_file_name.split(".")[:-1]) + ".png")
pwm = motifs.read(pwm_file, "pfm")
pwm.weblogo(logo_file_name,
format="png_print",
stack_width="medium",
color_scheme="color_classic")
pwm_file.close()
print("OK")
def estimate_bias_pwm(args):
# Parameters
max_duplicates = 100
# Initializing bam and fasta
bamFile = Samfile(args.reads_file, "rb")
genome_data = GenomeData(args.organism)
fastaFile = Fastafile(genome_data.get_genome())
regions = GenomicRegionSet("regions")
regions.read(args.regions_file)
obs_f_pwm_dict = dict([("A", [0.0] * args.k_nb), ("C", [0.0] * args.k_nb),
("G", [0.0] * args.k_nb), ("T", [0.0] * args.k_nb), ("N", [0.0] * args.k_nb)])
exp_f_pwm_dict = dict([("A", [0.0] * args.k_nb), ("C", [0.0] * args.k_nb),
("G", [0.0] * args.k_nb), ("T", [0.0] * args.k_nb), ("N", [0.0] * args.k_nb)])
obs_r_pwm_dict = dict([("A", [0.0] * args.k_nb), ("C", [0.0] * args.k_nb),
("G", [0.0] * args.k_nb), ("T", [0.0] * args.k_nb), ("N", [0.0] * args.k_nb)])
exp_r_pwm_dict = dict([("A", [0.0] * args.k_nb), ("C", [0.0] * args.k_nb),
("G", [0.0] * args.k_nb), ("T", [0.0] * args.k_nb), ("N", [0.0] * args.k_nb)])
# Iterating on HS regions
for region in regions:
# Initialization
prev_pos = -1
true_counter = 0
# Evaluating observed frequencies
# Fetching reads
for r in bamFile.fetch(region.chrom, region.initial, region.final):
# Calculating positions
if not r.is_reverse:
cut_site = r.pos + args.forward_shift - 1
p1 = cut_site - int(floor(args.k_nb / 2))
else:
cut_site = r.aend + args.reverse_shift + 1
p1 = cut_site - int(floor(args.k_nb / 2))
p2 = p1 + args.k_nb
# Verifying PCR artifacts
if p1 == prev_pos:
true_counter += 1
else:
prev_pos = p1
true_counter = 0
if true_counter > max_duplicates: continue
# Fetching k-mer
try:
currStr = str(fastaFile.fetch(region.chrom, p1, p2)).upper()
except Exception:
continue
if r.is_reverse: currStr = AuxiliaryFunctions.revcomp(currStr)
# Counting k-mer in dictionary
if not r.is_reverse:
for i in range(0, len(currStr)):
obs_f_pwm_dict[currStr[i]][i] += 1
else:
for i in range(0, len(currStr)):
obs_r_pwm_dict[currStr[i]][i] += 1
# Evaluating expected frequencies
# Fetching whole sequence
try:
currStr = str(fastaFile.fetch(region.chrom, region.initial, region.final)).upper()
except Exception:
continue
# Iterating on each sequence position
s = None
for i in range(0, len(currStr) - args.k_nb):
# Counting k-mer in dictionary
s = currStr[i:i + args.k_nb]
for i in range(0, len(s)):
exp_f_pwm_dict[s[i]][i] += 1
# Counting k-mer in dictionary for reverse complement
s = AuxiliaryFunctions.revcomp(s)
for i in range(0, len(s)):
exp_r_pwm_dict[s[i]][i] += 1
# Closing files
bamFile.close()
fastaFile.close()
# Output pwms
os.system("mkdir -p " + os.path.join(args.output_location, "pfm"))
pwm_dict_list = [obs_f_pwm_dict, obs_r_pwm_dict, exp_f_pwm_dict, exp_r_pwm_dict]
pwm_file_list = []
pwm_obs_f = os.path.join(args.output_location, "pfm", "obs_{}_f.pfm".format(str(args.k_nb)))
pwm_obs_r = os.path.join(args.output_location, "pfm", "obs_{}_r.pfm".format(str(args.k_nb)))
pwm_exp_f = os.path.join(args.output_location, "pfm", "exp_{}_f.pfm".format(str(args.k_nb)))
pwm_exp_r = os.path.join(args.output_location, "pfm", "exp_{}_r.pfm".format(str(args.k_nb)))
pwm_file_list.append(pwm_obs_f)
pwm_file_list.append(pwm_obs_r)
pwm_file_list.append(pwm_exp_f)
pwm_file_list.append(pwm_exp_r)
for i in range(len(pwm_dict_list)):
with open(pwm_file_list[i], "w") as pwm_file:
for e in ["A", "C", "G", "T"]:
pwm_file.write(" ".join([str(int(f)) for f in pwm_dict_list[i][e]]) + "\n")
motif_obs_f = motifs.read(open(pwm_obs_f), "pfm")
motif_obs_r = motifs.read(open(pwm_obs_r), "pfm")
motif_exp_f = motifs.read(open(pwm_exp_f), "pfm")
motif_exp_r = motifs.read(open(pwm_exp_r), "pfm")
# Output logos
os.system("mkdir -p " + os.path.join(args.output_location, "logo"))
logo_obs_f = os.path.join(args.output_location, "logo", "obs_{}_f.pdf".format(str(args.k_nb)))
logo_obs_r = os.path.join(args.output_location, "logo", "obs_{}_r.pdf".format(str(args.k_nb)))
logo_exp_f = os.path.join(args.output_location, "logo", "exp_{}_f.pdf".format(str(args.k_nb)))
logo_exp_r = os.path.join(args.output_location, "logo", "exp_{}_r.pdf".format(str(args.k_nb)))
motif_obs_f.weblogo(logo_obs_f, format="pdf", stack_width="large", color_scheme="color_classic",
yaxis_scale=0.2, yaxis_tic_interval=0.1)
motif_obs_r.weblogo(logo_obs_r, format="pdf", stack_width="large", color_scheme="color_classic",
yaxis_scale=0.2, yaxis_tic_interval=0.1)
motif_exp_f.weblogo(logo_exp_f, format="pdf", stack_width="large", color_scheme="color_classic",
yaxis_scale=0.02, yaxis_tic_interval=0.01)
motif_exp_r.weblogo(logo_exp_r, format="pdf", stack_width="large", color_scheme="color_classic",
yaxis_scale=0.02, yaxis_tic_interval=0.01)
# Creating bias dictionary
alphabet = ["A", "C", "G", "T"]
k_mer_comb = ["".join(e) for e in product(alphabet, repeat=args.k_nb)]
bias_table_F = dict([(e, 0.0) for e in k_mer_comb])
bias_table_R = dict([(e, 0.0) for e in k_mer_comb])
for k_mer in k_mer_comb:
obs_f = get_ppm_score(k_mer, motif_obs_f.pwm, args.k_nb)
exp_f = get_ppm_score(k_mer, motif_exp_f.pwm, args.k_nb)
bias_table_F[k_mer] = round(obs_f / exp_f, 6)
obs_r = get_ppm_score(k_mer, motif_obs_r.pwm, args.k_nb)
exp_r = get_ppm_score(k_mer, motif_exp_r.pwm, args.k_nb)
bias_table_R[k_mer] = round(obs_r / exp_r, 6)
write_table(args.output_location, args.output_prefix, [bias_table_F, bias_table_R])
#threshold= math.pow(10,-float(sys.argv[2])) # P-val, 5 means -log(n,10)
out = open(out_file, 'w')
out.write("Sequence\tMotif\tMotif_consensus\tHit_Seq\tHit_position\tHit_score\tThreshold\n")
# Specify if you only want to include certain pwm's in the directory
if include == "all":
include_if = os.listdir(f)
else:
include_if = open(include, 'r').read().splitlines()
if f[-4:] == ".pwm":
#Make PSSM for mapping
motif_name = f.strip().split("/")[-1]
m = motifs.read(open(f), "pfm")
motif_len = len(m)
pssm, consensus, threshold = make_pssm(m, ATbias, GCbias, p)
hits = mapping(seq_file, pssm, threshold, consensus, motif_name, motif_len)
else:
for motif_file in os.listdir(f):
if motif_file in include_if:
motif_name = motif_file.strip().split("/")[-1]
print(motif_name)
path = f + motif_file
m = motifs.read(open(path), "pfm")
motif_len = len(m)
pssm, consensus, threshold = make_pssm(m, ATbias, GCbias, p)
hits = mapping(seq_file, pssm, threshold, consensus, motif_name, motif_len)
else:
def line(self):
signal = GenomicSignal(self.bam_file)
signal.load_sg_coefs(slope_window_size=9)
bias_table = BiasTable()
bias_table_list = self.bias_table.split(",")
table = bias_table.load_table(table_file_name_F=bias_table_list[0],
table_file_name_R=bias_table_list[1])
genome_data = GenomeData(self.organism)
fasta = Fastafile(genome_data.get_genome())
pwm_dict = dict([("A", [0.0] * self.window_size), ("C", [0.0] * self.window_size),
("G", [0.0] * self.window_size), ("T", [0.0] * self.window_size),
("N", [0.0] * self.window_size)])
mean_raw_signal = np.zeros(self.window_size)
mean_bc_signal = np.zeros(self.window_size)
mean_raw_signal_f = np.zeros(self.window_size)
mean_bc_signal_f = np.zeros(self.window_size)
mean_raw_signal_r = np.zeros(self.window_size)
mean_bc_signal_r = np.zeros(self.window_size)
mean_bias_signal_f = np.zeros(self.window_size)
mean_bias_signal_r = np.zeros(self.window_size)
num_sites = 0
mpbs_regions = GenomicRegionSet("Motif Predicted Binding Sites")
mpbs_regions.read_bed(self.motif_file)
total_nc_signal = 0
total_nl_signal = 0
total_nr_signal = 0
for region in mpbs_regions:
if str(region.name).split(":")[-1] == "Y":
num_sites += 1
# Extend by 50 bp
mid = (region.initial + region.final) / 2
p1 = mid - (self.window_size / 2)
p2 = mid + (self.window_size / 2)
if not self.strands_specific:
# Fetch raw signal
raw_signal, _ = signal.get_signal(ref=region.chrom, start=p1, end=p2,
downstream_ext=self.atac_downstream_ext,
upstream_ext=self.atac_upstream_ext,
forward_shift=self.atac_forward_shift,
reverse_shift=self.atac_reverse_shift,
genome_file_name=genome_data.get_genome())
mean_raw_signal = np.add(mean_raw_signal, raw_signal)
# Fetch bias correction signal
bc_signal, _ = signal.get_signal(ref=region.chrom, start=p1, end=p2,
bias_table=table,
downstream_ext=self.atac_downstream_ext,
upstream_ext=self.atac_upstream_ext,
forward_shift=self.atac_forward_shift,
reverse_shift=self.atac_reverse_shift,
genome_file_name=genome_data.get_genome())
mean_bc_signal = np.add(mean_bc_signal, bc_signal)
else:
raw_signal_f, _, raw_signal_r, _ = signal.get_signal_per_strand(ref=region.chrom, start=p1, end=p2,
downstream_ext=self.atac_downstream_ext,
upstream_ext=self.atac_upstream_ext,
forward_shift=self.atac_forward_shift,
reverse_shift=self.atac_reverse_shift,
genome_file_name=genome_data.get_genome())
mean_raw_signal_f = np.add(mean_raw_signal_f, raw_signal_f)
mean_raw_signal_r = np.add(mean_raw_signal_r, raw_signal_r)
bc_signal_f, _, bc_signal_r, _ = signal.get_signal_per_strand(ref=region.chrom, start=p1, end=p2,
bias_table=table,
downstream_ext=self.atac_downstream_ext,
upstream_ext=self.atac_upstream_ext,
forward_shift=self.atac_forward_shift,
reverse_shift=self.atac_reverse_shift,
genome_file_name=genome_data.get_genome())
mean_bc_signal_f = np.add(mean_bc_signal_f, bc_signal_f)
mean_bc_signal_r = np.add(mean_bc_signal_r, bc_signal_r)
# Update pwm
aux_plus = 1
dna_seq = str(fasta.fetch(region.chrom, p1, p2)).upper()
if (region.final - region.initial) % 2 == 0:
aux_plus = 0
dna_seq_rev = AuxiliaryFunctions.revcomp(str(fasta.fetch(region.chrom,
p1 + aux_plus, p2 + aux_plus)).upper())
if region.orientation == "+":
for i in range(0, len(dna_seq)):
pwm_dict[dna_seq[i]][i] += 1
elif region.orientation == "-":
for i in range(0, len(dna_seq_rev)):
pwm_dict[dna_seq_rev[i]][i] += 1
# Create bias signal
bias_table_f = table[0]
bias_table_r = table[1]
self.k_nb = len(bias_table_f.keys()[0])
bias_signal_f = []
bias_signal_r = []
p1_wk = p1 - int(self.k_nb / 2)
p2_wk = p2 + int(self.k_nb / 2)
dna_seq = str(fasta.fetch(region.chrom, p1_wk, p2_wk - 1)).upper()
dna_seq_rev = AuxiliaryFunctions.revcomp(str(fasta.fetch(region.chrom, p1_wk, p2_wk + 1)).upper())
for i in range(int(self.k_nb / 2), len(dna_seq) - int(self.k_nb / 2) + 1):
fseq = dna_seq[i - int(self.k_nb / 2):i + int(self.k_nb / 2)]
rseq = dna_seq_rev[len(dna_seq) - int(self.k_nb / 2) - i:len(dna_seq) + int(self.k_nb / 2) - i]
try:
bias_signal_f.append(bias_table_f[fseq])
except Exception:
bias_signal_f.append(1)
try:
bias_signal_r.append(bias_table_r[rseq])
except Exception:
bias_signal_r.append(1)
mean_bias_signal_f = np.add(mean_bias_signal_f, np.array(bias_signal_f))
mean_bias_signal_r = np.add(mean_bias_signal_r, np.array(bias_signal_r))
if self.protection_score:
# signal in the center of the MPBS
p1 = region.initial
p2 = region.final
nc_signal, _ = signal.get_signal(ref=region.chrom, start=p1, end=p2,
bias_table=table,
downstream_ext=self.atac_downstream_ext,
upstream_ext=self.atac_upstream_ext,
forward_shift=self.atac_forward_shift,
reverse_shift=self.atac_reverse_shift,
genome_file_name=genome_data.get_genome())
total_nc_signal += sum(nc_signal)
p1 = region.final
p2 = 2 * region.final - region.initial
nr_signal, _ = signal.get_signal(ref=region.chrom, start=p1, end=p2,
bias_table=table,
downstream_ext=self.atac_downstream_ext,
upstream_ext=self.atac_upstream_ext,
forward_shift=self.atac_forward_shift,
reverse_shift=self.atac_reverse_shift,
genome_file_name=genome_data.get_genome())
total_nr_signal += sum(nr_signal)
p1 = 2 * region.initial - region.final
p2 = region.final
nl_signal, _ = signal.get_signal(ref=region.chrom, start=p1, end=p2,
bias_table=table,
downstream_ext=self.atac_downstream_ext,
upstream_ext=self.atac_upstream_ext,
forward_shift=self.atac_forward_shift,
reverse_shift=self.atac_reverse_shift,
genome_file_name=genome_data.get_genome())
total_nl_signal += sum(nl_signal)
mean_raw_signal = mean_raw_signal / num_sites
mean_bc_signal = mean_bc_signal / num_sites
mean_raw_signal_f = mean_raw_signal_f / num_sites
mean_raw_signal_r = mean_raw_signal_r / num_sites
mean_bc_signal_f = mean_bc_signal_f / num_sites
mean_bc_signal_r = mean_bc_signal_r / num_sites
mean_bias_signal_f = mean_bias_signal_f / num_sites
mean_bias_signal_r = mean_bias_signal_r / num_sites
protection_score = (total_nl_signal + total_nr_signal - 2 * total_nc_signal) / (2 * num_sites)
# Output PWM and create logo
pwm_fname = os.path.join(self.output_loc, "{}.pwm".format(self.motif_name))
pwm_file = open(pwm_fname,"w")
for e in ["A","C","G","T"]:
pwm_file.write(" ".join([str(int(f)) for f in pwm_dict[e]])+"\n")
pwm_file.close()
logo_fname = os.path.join(self.output_loc, "{}.logo.eps".format(self.motif_name))
pwm = motifs.read(open(pwm_fname), "pfm")
pwm.weblogo(logo_fname, format="eps", stack_width="large", stacks_per_line="100",
color_scheme="color_classic", unit_name="", show_errorbars=False, logo_title="",
show_xaxis=False, xaxis_label="", show_yaxis=False, yaxis_label="",
show_fineprint=False, show_ends=False)
# Output the raw, bias corrected signal and protection score
output_fname = os.path.join(self.output_loc, "{}.txt".format(self.motif_name))
output_file = open(output_fname, "w")
if not self.strands_specific:
output_file.write("raw signal: \n" + np.array_str(mean_raw_signal) + "\n")
output_file.write("bias corrected signal: \n" + np.array_str(mean_bc_signal) + "\n")
else:
output_file.write("raw forward signal: \n" + np.array_str(mean_raw_signal_f) + "\n")
output_file.write("bias corrected forward signal: \n" + np.array_str(mean_bc_signal_f) + "\n")
output_file.write("raw reverse signal: \n" + np.array_str(mean_raw_signal_r) + "\n")
output_file.write("bias reverse corrected signal: \n" + np.array_str(mean_bc_signal_r) + "\n")
output_file.write("forward bias signal: \n" + np.array_str(mean_bias_signal_f) + "\n")
output_file.write("reverse bias signal: \n" + np.array_str(mean_bias_signal_r) + "\n")
if self.protection_score:
output_file.write("protection score: \n" + str(protection_score) + "\n")
output_file.close()
if self.strands_specific:
fig, (ax1, ax2, ax3) = plt.subplots(3, figsize=(12.0, 10.0))
else:
fig, (ax1, ax2) = plt.subplots(2)
x = np.linspace(-50, 49, num=self.window_size)
ax1.plot(x, mean_bias_signal_f, color='red', label='Forward')
ax1.plot(x, mean_bias_signal_r, color='blue', label='Reverse')
ax1.xaxis.set_ticks_position('bottom')
ax1.yaxis.set_ticks_position('left')
ax1.spines['top'].set_visible(False)
ax1.spines['right'].set_visible(False)
ax1.spines['left'].set_position(('outward', 15))
ax1.spines['bottom'].set_position(('outward', 5))
ax1.tick_params(direction='out')
ax1.set_xticks([-50, -40, -30, -20, -10, 0, 10, 20, 30, 40, 49])
ax1.set_xticklabels(['-50', '-40', '-30', '-20', '-10', '0', '10', '20', '30', '40', '49'])
min_bias_signal = min(min(mean_bias_signal_f), min(mean_bias_signal_r))
max_bias_signal = max(max(mean_bias_signal_f), max(mean_bias_signal_r))
ax1.set_yticks([min_bias_signal, max_bias_signal])
ax1.set_yticklabels([str(round(min_bias_signal,2)), str(round(max_bias_signal,2))], rotation=90)
ax1.text(-48, max_bias_signal, '# Sites = {}'.format(str(num_sites)), fontweight='bold')
ax1.set_title(self.motif_name, fontweight='bold')
ax1.set_xlim(-50, 49)
ax1.set_ylim([min_bias_signal, max_bias_signal])
ax1.legend(loc="upper right", frameon=False)
ax1.set_ylabel("Average Bias \nSignal", rotation=90, fontweight='bold')
if not self.strands_specific:
mean_raw_signal = self.standardize(mean_raw_signal)
mean_bc_signal = self.standardize(mean_bc_signal)
ax2.plot(x, mean_raw_signal, color='red', label='Uncorrected')
ax2.plot(x, mean_bc_signal, color='green', label='Corrected')
else:
mean_raw_signal_f = self.standardize(mean_raw_signal_f)
mean_raw_signal_r = self.standardize(mean_raw_signal_r)
mean_bc_signal_f = self.standardize(mean_bc_signal_f)
mean_bc_signal_r = self.standardize(mean_bc_signal_r)
ax2.plot(x, mean_raw_signal_f, color='red', label='Forward')
ax2.plot(x, mean_raw_signal_r, color='green', label='Reverse')
ax3.plot(x, mean_bc_signal_f, color='red', label='Forward')
ax3.plot(x, mean_bc_signal_r, color='green', label='Reverse')
ax2.xaxis.set_ticks_position('bottom')
ax2.yaxis.set_ticks_position('left')
ax2.spines['top'].set_visible(False)
ax2.spines['right'].set_visible(False)
ax2.spines['left'].set_position(('outward', 15))
ax2.tick_params(direction='out')
ax2.set_xticks([-50, -40, -30, -20, -10, 0, 10, 20, 30, 40, 49])
ax2.set_xticklabels(['-50', '-40', '-30', '-20', '-10', '0', '10', '20', '30', '40', '49'])
ax2.set_yticks([0, 1])
ax2.set_yticklabels([str(0), str(1)], rotation=90)
ax2.set_xlim(-50, 49)
ax2.set_ylim([0, 1])
if not self.strands_specific:
ax2.spines['bottom'].set_position(('outward', 40))
ax2.set_xlabel("Coordinates from Motif Center", fontweight='bold')
ax2.set_ylabel("Average ATAC-seq \nSignal", rotation=90, fontweight='bold')
ax2.legend(loc="center", frameon=False, bbox_to_anchor=(0.85, 0.06))
else:
ax2.spines['bottom'].set_position(('outward', 5))
ax2.set_ylabel("Average ATAC-seq \n Uncorrected Signal", rotation=90, fontweight='bold')
ax2.legend(loc="lower right", frameon=False)
ax3.xaxis.set_ticks_position('bottom')
ax3.yaxis.set_ticks_position('left')
ax3.spines['top'].set_visible(False)
ax3.spines['right'].set_visible(False)
ax3.spines['left'].set_position(('outward', 15))
ax3.tick_params(direction='out')
ax3.set_xticks([-50, -40, -30, -20, -10, 0, 10, 20, 30, 40, 49])
ax3.set_xticklabels(['-50', '-40', '-30', '-20', '-10', '0', '10', '20', '30', '40', '49'])
ax3.set_yticks([0, 1])
ax3.set_yticklabels([str(0), str(1)], rotation=90)
ax3.set_xlim(-50, 49)
ax3.set_ylim([0, 1])
ax3.legend(loc="lower right", frameon=False)
ax3.spines['bottom'].set_position(('outward', 40))
ax3.set_xlabel("Coordinates from Motif Center", fontweight='bold')
ax3.set_ylabel("Average ATAC-seq \n Corrected Signal", rotation=90, fontweight='bold')
ax3.text(-48, 0.05, '# K-mer = {}\n# Forward Shift = {}'.format(str(self.k_nb), str(self.atac_forward_shift)),
fontweight='bold')
figure_name = os.path.join(self.output_loc, "{}.line.eps".format(self.motif_name))
fig.subplots_adjust(bottom=.2, hspace=.5)
fig.tight_layout()
fig.savefig(figure_name, format="eps", dpi=300)
# Creating canvas and printing eps / pdf with merged results
output_fname = os.path.join(self.output_loc, "{}.eps".format(self.motif_name))
c = pyx.canvas.canvas()
c.insert(pyx.epsfile.epsfile(0, 0, figure_name, scale=1.0))
if self.strands_specific:
c.insert(pyx.epsfile.epsfile(2.76, 1.58, logo_fname, width=27.2, height=2.45))
else:
c.insert(pyx.epsfile.epsfile(2.5, 1.54, logo_fname, width=16, height=1.75))
c.writeEPSfile(output_fname)
os.system("epstopdf " + figure_name)
os.system("epstopdf " + logo_fname)
os.system("epstopdf " + output_fname)
test_seq=Seq(str(record.seq), m.alphabet)
out.write(str(record.id) + "\t" + file1 + "\t" + str(record.seq) + "\n")
if(check == True):
#list = open(sys.argv[7]).readlines()
#print list
for l in list:
for filename in glob.glob("/home/ngs/vivek/python/scripts/matrices/converted/Homo_sapiens/"+ l.rstrip() +"_top10align_pfm.txt"):
#print filename
fname = os.path.basename(filename)
fname = re.sub(r"_top10align_pfm.txt","",str(fname))
test_pwm = motifs.read(open(filename), "pfm")
pwm = test_pwm.counts.normalize(pseudocounts=0.5)
pssm = pwm.log_odds(background)
IUPAC = test_pwm.counts.degenerate_consensus
for position, score in pssm.search (test_seq, threshold = float(sys.argv[2])):
print str(record.id) + "\t" + str(abs(position)) +"\t"+ str(abs(position)+len(IUPAC)) +"\t"+ str("+" if position > 0 else "-") +"\t"+ str(fname) +"\t"+ str(data[fname]) +"\t"+ str(IUPAC) +"\t"+ str(score) +"\t"+ str(file1) +"\t"+ str(now) +"\t"+ str(file1)
else:
test_pwm = motifs.read(open(sys.argv[7]), "pfm")
#print sys.argv[7]
pwm = test_pwm.counts.normalize(pseudocounts=0.5)
pssm = pwm.log_odds(background)
IUPAC = test_pwm.counts.degenerate_consensus
# Import
import sys
from Bio import motifs
from glob import glob
from copy import deepcopy
# Reading input
inList = ["./ZBT7B_M00405.pfm", "./MA0138.2.REST.pfm", "./MA0527.1.ZBTB33.pfm"]
# Execution
for inFileName in inList:
inFile = open(inFileName, "r")
outFileName = inFileName[:-3] + "pdf"
pwm = motifs.read(inFile, "pfm")
# Revert complement
tempA = deepcopy(pwm.counts["A"][::-1])
pwm.counts["A"] = pwm.counts["T"][::-1]
pwm.counts["T"] = tempA
tempC = deepcopy(pwm.counts["C"][::-1])
pwm.counts["C"] = pwm.counts["G"][::-1]
pwm.counts["G"] = tempC
# Complement
#tempA = deepcopy(pwm.counts["A"])
#pwm.counts["A"] = pwm.counts["T"]
#pwm.counts["T"] = tempA
#tempC = deepcopy(pwm.counts["C"])
#pwm.counts["C"] = pwm.counts["G"]
#pwm.counts["G"] = tempC
from Bio import SeqIO
from Bio.Alphabet import IUPAC
chp_list = list(SeqIO.parse("srf_chip.fasta", "fasta", IUPAC.unambiguous_dna))
count = 0
for dna in chp_list:
match = dna.seq.upper().count('GCCCATATATGG') # .upper vs. .upper()
count = count+match
import re
chp_match = 0
for dna in chp_list:
if re.search(r'[GT][CA]CC[AT]TATA[AT]GG', str(dna.seq)): # dna vs. str(dna.seq))
chp_match = chp_match+1
from Bio import motifs
srf_m = motifs.read(open("MA0083.1.sites"), "sites")
srf_m.pseudocounts = 1
srf_m.background = 0.4
for dna in chp_list: # put into for loop
for pos, score in srf_m.pssm.search(dna.seq, threshold=7.0): # use .seq atrribute
print "Position %d: score = %5.2f" % (pos, score)
#! /usr/bin/env python3
from Bio import SeqIO
from Bio.Alphabet import IUPAC
from Bio.Seq import Seq
from Bio import motifs
from Bio import SeqUtils
with open("sites/MA0106.1.sites") as handle:
p53 = motifs.read(handle, "sites")
motif = p53.degenerate_consensus
with open("output/motif_result_p53.txt","w") as f:
for seq_record in SeqIO.parse('input/gencode.v26.lncRNA_transcripts.fa','fasta'):
f.write(">" + str(seq_record.id) + "\n")
result=SeqUtils.nt_search(str(seq_record), m)
f.write(str(result) + "\n")
##
with open("sites/MA0001.1.sites") as handle:
AGL3 = motifs.read(handle, "sites")
motif = AGL3.degenerate_consensus
with open("output/motif_result_AGL3.txt","w") as f:
for seq_record in SeqIO.parse('input/gencode.v26.lncRNA_transcripts.fa','fasta'):
f.write(">" + str(seq_record.id) + "\n")
result=SeqUtils.nt_search(str(seq_record), motif)
from Bio.SeqRecord import SeqRecord
from Bio.Alphabet import IUPAC, generic_dna, generic_protein
from collections import defaultdict
#####################
## sys Inputs - to do
#####################
## read in alignment and motif
try:
alignment = list(SeqIO.parse(sys.argv[1], "fasta"))
except:
print ("ERROR This is not a fasta alignment file")
sys.exit()
try:
motif = motifs.read(open(sys.argv[2]), "pfm")
except:
print ("ERROR This is not a pfm file")
sys.exit()
try:
threshold = sys.argv[3]
except IndexError:
threshold = -10000
# Used later when marking output file
alignment_file_name = os.path.basename(sys.argv[1])
motif_file_name = os.path.basename(sys.argv[2])
print ("alignment file: " + alignment_file_name)
print ("motif file: " + motif_file_name)
#threshold= math.pow(10,-float(sys.argv[2])) # P-val, 5 means -log(n,10)
out = open(out_file, 'w')
out.write(
"Sequence\tMotif\tMotif_consensus\tHit_Seq\tHit_position\tHit_score\tThreshold\n"
)
# Specify if you only want to include certain pwm's in the directory
if include == "all":
include_if = os.listdir(f)
else:
include_if = open(include, 'r').read().splitlines()
if f[-4:] == ".pwm":
#Make PSSM for mapping
motif_name = f.strip().split("/")[-1]
m = motifs.read(open(f), "pfm")
motif_len = len(m)
pssm, consensus, threshold = make_pssm(m, ATbias, GCbias, p)
hits = mapping(seq_file, pssm, threshold, consensus, motif_name,
motif_len)
else:
for motif_file in os.listdir(f):
if motif_file in include_if:
motif_name = motif_file.strip().split("/")[-1]
print(motif_name)
path = f + motif_file
m = motifs.read(open(path), "pfm")
motif_len = len(m)
pssm, consensus, threshold = make_pssm(m, ATbias, GCbias, p)
hits = mapping(seq_file, pssm, threshold, consensus,
repositories = set(repositories)
query = set(args.folders)
if not repositories.issuperset(query):
print("ERROR: query repositories %s do not exist" % str(list(query.difference(repositories))))
exit(1)
repositories = args.folders
print(">>> CREATING logos for", repositories)
for repo in repositories:
dir_name = path.join(curr_dir, "motifs", repo)
for _, _, file_list in walk(dir_name):
output_dir = path.join(curr_dir, "logos", repo)
if not path.exists(output_dir):
mkdir(output_dir)
print(">>", repo)
for pwm_file_name in file_list:
pwm_full_file_name = path.join(dir_name, pwm_file_name)
if pwm_file_name.split(".")[-1] != "pwm":
continue
pwm_file = open(pwm_full_file_name, "r")
logo_file_name = path.join(output_dir, ".".join(pwm_file_name.split(".")[:-1]) + ".png")
pwm = motifs.read(pwm_file, "pfm")
pwm.weblogo(logo_file_name, format="png_print", stack_width="medium", color_scheme="color_classic")
pwm_file.close()
fprList = [0.005, 0.001, 0.0005, 0.0001, 0.00005, 0.00001]
pseudocounts = 0.1
background = {'A':0.25,'C':0.25,'G':0.25,'T':0.25}
precision = 10000
# Creating output file
outFile = open(outFileName,"w")
outFile.write("\t".join(["MOTIF"]+[str(e) for e in fprList])+"\n")
# Iterating on all PWMs
for pwmFileName in glob(inFolder+"*.pwm"):
# Creating PSSM
name = ".".join(basename(pwmFileName).split(".")[:-1])
input_file = open(pwmFileName,"r")
pfm = motifs.read(input_file, "pfm")
pwm = pfm.counts.normalize(pseudocounts)
input_file.close()
pssm = pwm.log_odds(background)
pssm_list = [pssm[e] for e in ["A","C","G","T"]]
distribution = pssm.distribution(background=background, precision=precision)
# Evaluating thresholds
resVec = [name]
for fpr in fprList:
resVec.append(str(distribution.threshold_fpr(fpr)))
# Writing results
outFile.write("\t".join(resVec)+"\n")
# tf_class, tf_family: structuras class & family of motif
# species: as taxonomy IDs
# tax_group: taxonomic supergroup of motif
# acc: accesion number of transcription factor protein
# data_type: type of data used to construct the motif
# medline: Pubmed ID or literature supporting motif
# pazar_id: reference ID to PAZAR DB
# comment: text, notes about motif
# stores motifs in 3 main formats: 2 flat files & SQL DB
# JASPAR sites format
#>ID name count
#seqseqMOTIFMOTIFseqseq
# no added meta info
with open('Arnt.sites') as handle:
arnt = motifs.read(handle, 'sites') #motif format 'sites'
print len(arnt.instances), arnt.instances
print arnt.counts
# JASPAR pfm format
#2 9 0 1 32 4
#1 33 4 51 1 0
#9 3 10 0 0 0
#20 0 31 0 0 50
# only count profile matrix
with open('SRF.pfm') as handle:
srf = motifs.read(handle, 'pfm') # motif format 'pfm'
print srf.counts
print srf.instances # direct matrix, it didn't save instances
print arnt.counts.consensus, srf.counts.consensus
from sys import argv
import numpy as np
from Bio import motifs
name = str(argv[1]).split('.')[0]
name_pfm = name + '.pfm'
name_transfac = name + '.transfac'
mat = np.genfromtxt(argv[1])
mat_trans = mat.transpose()
t = ''
for i in range(len(mat_trans)):
for j in mat_trans[i]:
t += str(int(j))
t += ' '
t += '\n'
with open(name_pfm, 'w') as pfm_out:
pfm_out.write(t)
mot = motifs.read(open(name_pfm), 'pfm')
print mot.format("transfac")
# with open(name_transfac, 'w') as transfac_out:
# transfac_out.write(mot.format("transfac"))