def list_from_file(
cls,
motifs_file,
file_format,
threshold=None,
pseudocounts="jaspar",
relative_threshold=None,
):
"""Return a list of PSSM patterns from a file in JASPAR, MEME, etc.
Parameters
----------
motifs_file
Path to a motifs file, or file handle.
file_format
File format. one of "jaspar", "meme", "TRANSFAC".
pseudocounts
Either a dict {"A": 0.01, "T": ...} or "jaspar" for automatic
pseudocounts from the Biopython.motifs.jaspar module (recommended),
or None for no pseudocounts at all (not recommended!)
threshold
locations of the sequence with a PSSM score above this value will be
considered matches. For convenience, a relative_threshold can be
given instead.
relative_threshold
Value between 0 and 1 from which the threshold will be auto-computed.
0 means "match everything", 1 means "only match the one (or several)
sequence(s) with the absolute highest possible score".
"""
if isinstance(motifs_file, str):
with open("./jaspar.txt", "r") as f:
motifs_list = motifs.parse(f, format=file_format)
else:
motifs_list = motifs.parse(motifs_file, format=file_format)
if pseudocounts is not None:
for motif in motifs_list:
cls.apply_pseudocounts(motif, pseudocounts)
return [
MotifPssmPattern(
pssm,
threshold=threshold,
relative_threshold=relative_threshold,
) for pssm in motifs_list
]
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 get_summary(job_id, meme_file, peaks):
"""
Write summary in a json file
"""
summary = {}
# Number of occurences in peak
summary['motif_occurrences'] = {}
# Number of peaks
summary['original_peaks'] = peaks
summary['peaks'] = min(MAX_PEAKS_TO_KEEP, peaks)
records = motifs.parse(open(meme_file), 'meme')
num_occurrences = []
for index, record in enumerate(records):
num_occurrences.append(
int(getattr(record, 'num_occurrences', 'Unknown')))
sorted_occurences = sorted(enumerate(num_occurrences), key=lambda x: x[1])
summary['motif_occurrences'] = {
'motif{}'.format(index + 1): value
for index, value in sorted_occurences
}
fp = os.path.join(STATIC_PATH, job_id, 'summary.json')
with open(fp, 'w') as f:
json.dump(summary, f)
print summary
return summary
def jaspar2pfm(jasparFile, outDir):
with open(jasparFile) as handle:
for m in motifs.parse(handle, "jaspar"):
fileName = outDir + "/" + str(m.name).replace(":",
"_").upper() + ".pfm"
with open(fileName, "w") as output:
output.write(m.format("jaspar"))
def main(argv):
parser = argparse.ArgumentParser(description='Process meme files')
parser.add_argument('-i', '--meme', metavar='<meme_out>', help='Meme input file', required=True)
parser.add_argument('-m', '--motif', metavar='<motif_no>', help='Motif number', required=True, type=int)
parser.add_argument('-c', '--phylo', metavar='<phylo_out>', help='PhyloP conservation scores', required=True)
parsed = parser.parse_args(argv)
handle = open(parsed.meme)
records = motifs.parse(handle, 'meme')
record = records[parsed.motif-1]
phylo_data = csv.reader(open(parsed.phylo,'r'), delimiter='\t')
phylo_scores = []
for line in phylo_data:
phylo_scores.append(float(line[2]))
print "Motif length", record.length
print "phylo length", len(phylo_scores)
profile = position_wise_profile(record.counts, record.length)
max_occur = find_max_occurence(profile, max_count=1)
motif_scores = []
for position in max_occur:
motif_scores.append(position[0][1])
pr = pearsonr(np.array(motif_scores), np.array(phylo_scores))
print 'Pearson correlation: {}'.format(pr)
fig, ax = plt.subplots()
ax= sns.regplot(y=np.array(motif_scores), x=np.array(phylo_scores), scatter=True)
ax.set(ylabel="Count of most freq nucleotide", xlabel="PhyloP scores", title='CTCF | pearsonr = {}, p-val={}'.format(pr[0],pr[1]));
fig.savefig('{}_motif{}_scatter.png'.format(parsed.phylo, parsed.motif))
x = np.linspace(1,len(phylo_scores)+1,num=len(phylo_scores), endpoint=False)
f, (ax1, ax2) = plt.subplots(2, 1)
x1 = sns.barplot(x,y=np.array(motif_scores), ax=ax1)
x2 = sns.barplot(x,y=np.array(phylo_scores), ax=ax2)
x1.set(ylabel='Counts of most freq nucleotide', xlabel='Position in motif')
x2.set(ylabel='Phylop Score', xlabel='Position in motif')
f.tight_layout()
f.savefig('{}_motif{}_trend.png'.format(parsed.phylo, parsed.motif))
def get_jaspar_motif(tfName):
with open(Config.get("data", "pfm_db_jaspar")) as handle:
for m in motifs.parse(handle, "jaspar"):
if str(m.name).upper() == str(tfName).upper():
return m
# if not found
return None
def fit(self, fasta_file=''):
"""Save the output of MEME and parse it."""
if not fasta_file:
raise NameError('Input fasta file not specified')
cmd_params = self._make_param_string()
self._command_exec(fasta_file, cmd_params)
# parsing meme output file with Biopython
filename = os.path.join(self.output_dir, 'meme.txt')
handle = open(filename)
record = motifs.parse(handle, 'meme')
handle.close()
# store names of sequences given as input to fit()
self.seq_names = record.sequences[:]
self.n_seqs = len(record.sequences)
# create a list of motives, each represented by an object
motives_db = list()
for i in range(self.nmotifs):
motives_db.append(record[i])
self.motives_db = motives_db[:]
# store length, number of occurences and e-value of each motif
self._get_stats(self.nmotifs)
# get string representation of motives
self.motives_list = list(self._get_motives_list())
# create PWMs
motives_list = self.motives_list[:]
super(Meme, self).fit(motives=motives_list)
def read_memefile(meme_file):
"""Summariser for MEME file
Read meme file
Parameters
----------
meme_file: str
Location of MEME file
Returns
-------
summary: dict
A summary containing the following details:
- motif_occurences: dict contatining number of type each motif occurs. dict is indexed by key: 'motif1', 'motif2' etc
- motif_records: List of Biopython motif objects
summary:
"""
summary = {}
summary['motif_occurrences'] = {}
records = motifs.parse(open(os.path.abspath(meme_file)), 'meme')
summary['total_motifs'] = len(records)
num_occurrences = []
for index, record in enumerate(records):
num_occurrences.append(int(getattr(record,'num_occurrences','Unknown')))
sorted_occurences = sorted(enumerate(num_occurrences), key=lambda x: x[1])
summary['motif_occurrences'] = {'motif{}'.format(index+1):value for index,value in sorted_occurences}
summary['motif_records'] = records
### Read background frequenceies H since bioppython does not support them
bg_frequencies = get_motif_bg_freq(meme_file)
summary['bg_frequencies'] = bg_frequencies
return summary
def main(motif_file, motif_outfile, d_th, pc, bp, ow, fpr, pe):
thresholds = []
background = {'A': bp[0], 'C': bp[1], 'T': bp[2], 'G': bp[3]}
print(("Baseline nucleotide frequencies:\n\t" + str(background)))
print(("Calculating thresholds (" + timeString() + "). This could take a while."))
sys.stdout.flush()
idx = 0
print_exponent = 1
# Calculate thresholds using biopython
fh = open(motif_file)
for m in motifs.parse(fh, "jaspar"):
pwm = m.counts.normalize(pseudocounts=pc) # creates dictionary like representation
pssm = pwm.log_odds(background) # converts to log_odds vs background
# Precision argument of 4 was recommended by biopython's documentation (slow step)
# YYY - JA - 05/05/2017 - This could be sped up by using TFM-PVALUE's C++ functions for
# determining thresholds. May consider implementing, calculates precise p-values with no
# errors much more quickly and can also generate p-values from a score.
distribution = pssm.distribution(background=background, precision=10 ** pe)
m_th = distribution.threshold_fpr(fpr)
thresholds.append(m_th)
# print progress
idx += 1
if (idx >= 10 ** print_exponent):
print((str(idx) + " thresholds calculated... at " + timeString()))
print_exponent += 1
sys.stdout.flush()
print(("Total motifs read: " + str(len(thresholds))))
print("Outputing thresholds")
motif.get_put_motifs(motif_file, motif_outfile, d_th, ow, thresholds)
print(("Done (" + timeString() + ")"))
def suspect_site_extractor(example_seq, compute_motifs, num_sites, motifs_path, extension=''):
sites_collector = {}
df_recombination = find_recombination_sites(str(example_seq).upper(), num_sites)
print('finished finding RMD sites')
df_slippage = find_slippage_sites(str(example_seq).upper(), num_sites)
print('finished finding SSR sites')
sites_collector['df_recombination' + extension] = df_recombination
sites_collector['df_slippage' + extension] = df_slippage
### do methylation only if requested
if compute_motifs == True:
with open(motifs_path, "r") as handle:
relevant_motifs = motifs.parse(handle, "minimal")
df_motifs = find_motif_sites(example_seq, num_sites, relevant_motifs)
print('finished finding motif sites')
sites_collector['df_motifs' + extension] = df_motifs
return sites_collector
def motifs_list(jasp_motifs_file):
jasp_motifs = open(jasp_motifs_file, 'r')
motifs_list = []
for m in motifs.parse(jasp_motifs, "jaspar"):
motifs_list.append(m)
jasp_motifs.close()
return motifs_list
def tffm_from_meme(meme_output, kind, name="TFFM"):
"""
Construct a TFFM from the output of MEME on ChIP-seq data.
:arg meme_output: File containing the output of MEME.
:type meme_output: str
:arg kind: Type of TFFM to construct between '1st-order' and 'detailed'.
:type kind: str
:arg name: Name of the TFFM (default: "TFFM")
:type name: str
:returns: The TFFM initialized from MEME results.
:rtype: :class:`TFFM`
:note: As the PFM is used to initialize the TFFM, a pseudocount of 1 is
added to all the values in the PFM
"""
record = motifs.parse(open(meme_output), 'MEME')
if record.alphabet != IUPAC.unambiguous_dna:
sys.exit("### Wrong alphabet used in MEME ###")
motif = record[0]
nb_seq, nb_res, first_letters = utils.get_sequences_info(record.datafile)
if kind == TFFM_KIND.FIRST_ORDER:
hmm = create_1storder_hmm(nb_seq, nb_res, first_letters, motif)
elif kind == TFFM_KIND.DETAILED:
hmm = create_detailed_hmm(nb_seq, nb_res, first_letters, motif)
else: # 0-order HMM here
hmm = create_0order_hmm(nb_seq, nb_res, first_letters, motif)
return TFFM(hmm.emissionDomain, hmm.distribution, hmm.cmodel, kind, name)
def __init__(self, fasta, motifs_input, bg=None):
self.all_motifs = []
with open(motifs_input, "r") as infile:
self.all_motifs = list(motifs.parse(infile, "jaspar"))
# for large sequence header, only keep the text before the first space
self.genome_seq = pyfasta.Fasta(fasta, key_fn=lambda x: x.split()[0])
self.bg = bg
def read_jaspar_pwms(file='jaspar/pfm_vertebrates.txt', dir='d:/sequence'):
with open(os.path.join(dir, file), 'r') as h:
jaspar = {
m.name.strip().split(':')[0].upper():
m.counts.normalize().log_odds()
for m in motifs.parse(h, 'jaspar')
}
return jaspar
def process_meme_output(meme_out_folder, pfm_filename):
"""
Parse MEME search result into JASPAR .pfm files.
"""
with open(f'{meme_out_folder}/meme.txt') as meme:
ms = motifs.parse(meme, 'MINIMAL')
with open(pfm_filename, 'w+') as pfm:
pfm.write(motifs.write(ms, 'jaspar'))
def read_pfm(jaspar_motifs_file, tf_name):
motif = None
with open(jaspar_motifs_file) as handle:
for m in motifs.parse(handle, "jaspar"):
if m.name == tf_name:
motif = m
break
return motif
def getmotiflist(pwmfilename, filetype="TRANSFAC", prefix=None):
with open(pwmfilename, 'r') as mhandle:
motiflist = motifs.parse(mhandle, filetype)
result = []
for motifpwm in motiflist:
if prefix == None or \
motifpwm['ID'].strip().startswith(prefix):
result.append(motifpwm)
return result
def pwm2pval(motifName, seq):
with open(Config.get("data", "pfm_db_jaspar")) as handle:
for m in motifs.parse(handle, "jaspar"):
if str(m.name).upper() == str(motifName).upper():
ppm = m.counts.normalize(pseudocounts=C_PSEUDOCOUNTS)
pwm = ppm.log_odds(background=C_BACKGROUND)
writePWM(m)
break ### stop if motif is found
print "PWM:"
print pwm
### scale raw PWM scores
scaled_pwm = np.zeros(shape=(4, len(m)))
for i, nt in enumerate(['A', 'C', 'G', 'T']):
scaled_pwm[i] = pwm[nt]
# print scaled_pwm
# subtract by min PWM score, to make non-negative matrix
scale_const = np.min(scaled_pwm)
nonneg_pwm = scaled_pwm - scale_const
# scale
scale_factor = (C_PRECISION / np.max(nonneg_pwm))
scaled_pwm = nonneg_pwm * scale_factor
# round to nearest integer
scaled_pwm = np.rint(scaled_pwm).astype(int)
print "Scaled PWM:"
print scaled_pwm
### score distribution
score_distribution = np.zeros(shape=(len(m), len(m) * C_PRECISION + 1),
dtype=np.int)
print np.shape(score_distribution)
# init first row
for i, nt in enumerate(['A', 'C', 'G', 'T']):
score_distribution[0, scaled_pwm[i, 0]] += 1
# print "first row:",score_distr[0,:]
# proc rest of motif
for j in range(1, len(m)): ### j: 1 to length of motif
print "MOTIF pos:", j
for k in scaled_pwm[:, j]:
for idx, count in enumerate(score_distribution[j - 1, :]):
if count > 0:
score_distribution[j, idx + k] += count
scaled_score = int(seq2score(seq, scaled_pwm))
raw_score = seq2score(seq, pwm)
print "scaled score:", scaled_score
print "raw score:", raw_score
pval = score2pval(scaled_score, score_distribution)
print "pval:", pval
def build_pssm(meme):
with open(meme) as f:
record = motifs.parse(f, 'minimal')
motif = record[0]
motif.pseudocounts = motif.background
pssm = motif.pssm
return pssm
def get_motif(pcm_file):
try:
with open(pcm_file) as handle:
m = motifs.parse(handle, HOCOMOCO_PARSE_FORMAT)
if (m is not None and len(m) > 0):
return m[0]
else:
return None
except IOError as e:
logger.error(ERROR_MSG_IO.format(pcm_file, e.strerror))
def create_pfm_from_meme(self, memexml, nameforpfm, tf_name, species_name):
with open(memexml) as handle:
record = motifs.parse(handle, "meme")
motif = record[0]
motif_base = str(motif.counts)
with open(nameforpfm, 'w') as transition:
transition.write('>{}\t{}\n'.format(tf_name, species_name))
for line in motif_base.split('\n')[1:5]:
base, values = line.strip().split(':')
values = [str(round(float(i))) for i in values.split(' ') if i != '']
transition.write('{} [ {} ]\n'.format(base, ' '.join(values)))
def get_chipseq_ranges(chip_fimo, chip_all, motif_file):
chip_all_data = pd.read_csv(chip_all)
chip_fimo_data = pd.read_table(chip_fimo)
motif_id = set(chip_fimo_data['#pattern name']).pop()
with open(motif_file, 'r') as fid:
motif_record = motifs.parse(fid, 'MEME')
mot_len = int(motif_record[motif_id - 1].length)
mot_range = []
for pos in list(chip_all_data['Position']):
mot_range.append((pos - mot_len - 10, pos + mot_len + 10))
return mot_range
def __init__(self, ref_path, bg=None):
ref_manager = ReferenceManager(ref_path)
self.all_motifs = []
if ref_manager.motifs is not None:
with open(ref_manager.motifs, "r") as infile:
self.all_motifs = list(motifs.parse(infile, "jaspar"))
# for large sequence header, only keep the text before the first space
self.genome_seq = pyfasta.Fasta(ref_manager.fasta,
key_fn=lambda x: x.split()[0])
self.bg = bg
def build_motif_db():
motif_dict= {}
background = {'A': 0.3, 'C': 0.2, 'T': 0.3, 'G': 0.2}
jf = open("jaspar_curated.pfm")
for m in motifs.parse(jf,"jaspar"):
pwm = m.counts.normalize(pseudocounts={'A': 0.6, 'C': 0.4, 'T': 0.6, 'G': 0.4})
pssm = pwm.log_odds(background)
distribution = pssm.distribution(background=background, precision = 10**3)
# threshold = distribution.threshold_patser()
threshold = distribution.threshold_fpr(0.001)
motif_dict[m.name] = (pssm,threshold)
return motif_dict
def load_meme(filename):
"""
Return a parsed MEME output file using Biopython.
"""
gapped = Gapped(ExtendedIUPACDNA(), '-')
with open(filename, "r") as f:
records = []
records = motifs.parse(f, "MEME")
records.sort(key=lambda x: x.evalue) # Sort entries by evalue
records = convert(records, gapped)
return records
def load_meme(filename):
"""
Return a parsed MEME output file using Biopython.
"""
gapped = Gapped(ExtendedIUPACDNA(), '-')
with open(filename, "r") as f:
records = []
records = motifs.parse(f, "MEME")
records.sort(key=lambda x: x.evalue) # Sort entries by evalue
records = convert(records, gapped)
return records
def MEMERIS(args):
from Bio import motifs
records = []
fastaPath = os.path.join(args.indir, "sequences.fa")
with open(os.path.join(args.indir, 'results.txt')) as f:
for motif in motifs.parse(f, 'MEME', strict=False):
for instance in motif.instances:
start = instance.start - 1
end = start + len(str(instance))
records.append((instance.sequence_name, start, end))
locations = pd.DataFrame.from_records(records)
locations.columns = ["sequence_id", "start", "end"]
locations = locations.set_index("sequence_id")
nameMapping = pd.Series(extendSeqName(list(locations.index), fastaPath))
locations.index = nameMapping.loc[locations.index]
return locations
def motif_search_calc(input_jaspar,lincRNA_seq,output_motif):
with open (input_jaspar,"r") as fm:
for m in motifs.parse(fm,"jaspar"):
with open(lincRNA_seq,"r") as lincs:
for lincseq in SeqIO.parse(lincs,'fasta',alphabet=IUPAC.unambiguous_dna):
for pos, score in m.pssm.search(lincseq.seq, threshold=7.0):
d=lincseq.id.split("|")
linc_id = d[1].strip("\" ")
position = abs(pos)
final_position = (int(position)/int(d[-2]))
all_positions.append(final_position)
with open(output_motif,"w") as f:
f.write("For lincRNA" + "\t" + str(d[0]) + "\t" + str(d[1]) + "\t" + "total length:" + "\t" + str(d[-2]) + "\n")
f.write("Motif" + "\t" + str(m.name) + "\t" + "binds at position" + "\t" + str(pos) + "\t" + " with score: " + "\t" + str(score) + "\n")
return all_positions
def plot_pwm(filename, output=None):
"""
Plots a mononuc probability matrix from a JASPAR file
Caution: only ONE motif in the file !!!
The output must be given as the name of a PDF file, otherwise automatic from the input name
"""
if output is None:
output = filename.split("/")[-1].split(".")[0] + ".pdf"
# Uses WebLogo from BioPython to plot the matrix in pdf format
# Requires an internet connection
fh = open(filename)
for m in motifs.parse(fh, "jaspar"):
try:
m.weblogo(output, format="PDF")
except:
print "ERROR trying to plot the sequence logo using the online website WebLogo (http://weblogo.berkeley.edu/). A possible cause is the absence of a working internet connection, otherwise the pwm file %s may be corrupted. If you wish to plot the sequence logo, consider using the alternate website STAMP (http://www.benoslab.pitt.edu/stamp/)" % filename
fh.close()
return 0
def motif_identifier(tf, meme_folder):
meme_file = meme_folder + '/meme.txt'
with open(meme_file, 'r') as fid:
try:
records = motifs.parse(fid, 'MEME')
except ValueError:
return '1'
motif_list = []
evalue_list = []
for record in records:
curr_evalue = record.evalue
for curr_motif in record.instances:
# if curr_motif.sequence_name == tf.lower():
motif_list.append(curr_motif.motif_name)
evalue_list.append(curr_evalue)
motif_list = zip(motif_list, evalue_list)
sorted_list = sorted(motif_list, key=lambda x: x[1])
motif_name = sorted_list[0][0]
return motif_name[-1]
def find_pwm_hits(narrow_peak, reference, pfm, output, treat_cov):
"""
Search each peak for the best match against the specified position
frequency matrix
Args:
narrow_peak (str) - path to the narrowPeak file output by MACS2
reference (str) - file path to the reference genome
pfm (str) - file path to the position frequency matrix
output (str) - prefix for the output file
"""
# Open the peaks and reference genome files
with open(narrow_peak, "r") as peaks, open(reference, "r") as ref:
# Parse the reference genome into a dictionary
records = SeqIO.parse(ref, "fasta", alphabet=IUPAC.unambiguous_dna)
ref_seq = {record.id: record for record in records}
# Open and parse the position frequency matrix
with open(pfm, "r") as pfm:
matrix = motifs.parse(pfm, "jaspar")[0]
pwm = matrix.counts.normalize(pseudocounts=.5)
pssm = pwm.log_odds()
# Open the output file
with open(output + "_centeredpeaks.bed", "w") as out_bed, \
open(output + "_centeredpeaks.fasta", "w") as out_fasta:
# Write a line for each centered peak in the output file
for peak in peaks:
split_peak = peak.strip().split("\t")
peak_chrom = split_peak[0]
peak_start = int(split_peak[1])
peak_end = int(split_peak[2])
seq = ref_seq[peak_chrom].seq[peak_start:peak_end]
hits = [(pos, score) for pos, score in pssm.search(seq)]
hits.sort(key=lambda hit: hit[1], reverse=True)
recenter_peak(out_bed, out_fasta, ref_seq, peak_chrom,
peak_start, peak_end, 100, hits, matrix,
treat_cov)
def motif_search(input_jaspar, lincRNA_seq):
with open(input_jaspar, "r") as fm, open(lincRNA_seq, "r") as lincs:
for m in motifs.parse(fm, "jaspar"):
for lincseq in SeqIO.parse(lincs,
'fasta',
alphabet=IUPAC.unambiguous_dna):
d = lincseq.id.split("|")
linc_id = d[0].strip("\" ")
#print(linc_id)
for pos, score in m.pssm.search(lincseq.seq):
one_position = int(abs(pos))
list_positions = [one_position]
#print(pos)
if linc_id in motif_positions.keys():
motif_positions[linc_id].append(one_position)
else:
empty_values = motif_positions.get(linc_id, None)
motif_positions[linc_id] = list_positions
print(motif_positions)
return motif_positions
def get_summary(job_id, meme_file, peaks):
"""
Write summary in a json file
"""
summary = {}
# Number of occurences in peak
summary['motif_occurrences'] = {}
# Number of peaks
summary['original_peaks'] = peaks
summary['peaks'] = min(MAX_PEAKS_TO_KEEP, peaks)
records = motifs.parse(open(meme_file), 'meme')
num_occurrences = []
for index, record in enumerate(records):
num_occurrences.append(int(getattr(record,'num_occurrences','Unknown')))
sorted_occurences = sorted(enumerate(num_occurrences), key=lambda x: x[1])
summary['motif_occurrences'] = {'motif{}'.format(index+1):value for index,value in sorted_occurences}
fp = os.path.join(STATIC_PATH, job_id, 'summary.json')
with open(fp, 'w') as f:
json.dump(summary, f)
print summary
return summary
def build_pssm(meme):
with open(meme) as f:
record = motifs.parse(f, 'minimal')
motif = record[0]
motif.pseudocounts = motif.background
pssm = motif.pssm
mean = motif.pssm.mean()
std = motif.pssm.std()
consensus = motif.consensus
#distribution = motif.pssm.distribution(background = motif.background)
max_value = motif.pssm.max
min_value = motif.pssm.min
#print(pssm, mean, std,consensus, max_value, min_value)
cutoff_1 = [mean - std, 0][(mean - std) < 0]
cutoff_2 = [mean - 2 * std, 0][(mean - 2 * std) < 0]
cutoff_3 = [mean + std, 0][(mean + std) < 0]
cutoff_4 = [mean + 2 * std, 0][(mean + 2 * std) < 0]
return pssm, mean, std, max_value, cutoff_1, cutoff_2, cutoff_3, cutoff_4
def get_motifscores(df):
with open('../additional/ATtRACT/pwm_transposed.txt', 'r') as f:
records = parse(f, 'jaspar')
Xs_sel = pd.read_pickle(
'/net/mraid08/export/genie/Runs/Martin/ATtRACT/irmotifs_scores.pkl')
mtfs = []
for i in Xs_sel.columns:
if i.split('__')[0] not in mtfs:
mtfs.append(i.split('__')[0])
def find_motifs_ir(varid):
motifs = pd.Series()
for pos, seq in mm.counts.log_odds().search(Seq(df.sequence[varid], \
alphabet=IUPAC.IUPACUnambiguousDNA()), threshold=0, both=False):
motifs.loc[pos] = seq
motifs_up = motifs[motifs.index < df.intronstart[varid]]
motifs_alt = motifs[(motifs.index > df.intronstart[varid])
& (motifs.index < df.intronend[varid])]
motifs_down = motifs[motifs.index > df.intronend[varid]]
return list([motifs_up.sum(), motifs_alt.sum(), motifs_down.sum()])
junirmotifs = pd.DataFrame(index=df.index)
database = pd.read_table('../additional/ATtRACT/ATtRACT_db.txt')
database.drop_duplicates('Matrix_id', inplace=True)
database.set_index('Matrix_id', inplace=True)
for mm in records:
# if (mm.name in database[database.Organism=='Homo_sapiens'].index):
if (mm.name in mtfs):
mm.counts.__class__ = matrix.PositionWeightMatrix
junirmotifs['motifs'] = df.index.map(lambda x: find_motifs_ir(x))
junirmotifs[[str(mm.name) + '__score_motifs_up',str(mm.name) + '__score_motifs_alt',\
str(mm.name) + '__score_motifs_down']]=junirmotifs.motifs.apply(lambda x: pd.Series(x))
junirmotifs.drop('motifs', axis=1, inplace=True)
return junirmotifs[Xs_sel.columns]
def motif_format_checker(motif_infile):
from Bio import motifs
if motif_infile is None:
return None
try:
all_motifs = motifs.parse(open(motif_infile), "jaspar")
except:
sys.exit("Motif file is not in JASPAR format.")
nmotif = 0
with open(motif_infile) as motif_in:
for i, line in enumerate(motif_in):
if line.startswith('>'):
nmotif += 1
if len(line.split('\t')) > 1:
sys.exit(
"Motif name cannot contain tabs('\t') at line {} in {}.".format(i+1, motif_infile)
)
if nmotif != len(all_motifs):
sys.exit("Motif file is not in JASPAR format.")
def get_motifscores(df):
with open('../additional/ATtRACT/pwm_transposed.txt', 'r') as f:
records = parse(f, 'jaspar')
Xs_sel = pd.read_pickle(
'./dataframes/ml/three/Xs_three_motifs_rna_sel_.pkl')
mtfs = []
for i in Xs_sel.columns:
if i.split('__')[0] not in mtfs:
mtfs.append(i.split('__')[0])
def find_motifs(varid):
motifs = pd.Series()
for pos, seq in mm.counts.log_odds().search(Seq(df.sequence[varid], \
alphabet=IUPAC.IUPACUnambiguousDNA()), threshold=0, both=False):
motifs.loc[pos] = seq
motifs_up = motifs[motifs.index < df.acceptor1[varid]]
motifs_alt = motifs[(motifs.index > df.acceptor1[varid])
& (motifs.index < df.acceptor2[varid])]
motifs_down = motifs[motifs.index > df.acceptor2[varid]]
return list([motifs_up.sum(), motifs_alt.sum(), motifs_down.sum()])
motifscores = pd.DataFrame(index=df.index)
database = pd.read_table('../additional/ATtRACT/ATtRACT_db.txt')
database.drop_duplicates('Matrix_id', inplace=True)
database.set_index('Matrix_id', inplace=True)
for mm in records:
if (mm.name in mtfs):
mm.counts.__class__ = matrix.PositionWeightMatrix
motifscores['motifs'] = df.index.map(lambda x: find_motifs(x))
motifscores[[str(mm.name) + '__score_motifs_up',str(mm.name) + '__score_motifs_alt',\
str(mm.name) + '__score_motifs_down']]=motifscores.motifs.apply(lambda x: pd.Series(x))
motifscores.drop('motifs', axis=1, inplace=True)
return motifscores[Xs_sel.columns]
def parseMastOut(mastOut):
from Bio import motifs
handle = StringIO.StringIO(mastOut)
record = motifs.parse(handle, "mast")
handle.close()
return record
def main(argv):
handle = open(argv[0], 'r')
records = motifs.parse(handle, 'meme')
print "Total motifs present: {}".format(len(records))
for i, record in enumerate(records):
print "Motif {} \t Length: {} \t, Seq: {}".format(i, len(record.consensus), record.consensus)
# concatenate all files
all_states = "all_states_all_lines.bed"
os.system("cat *.bed > {0}".format(all_states))
# Get CD19 perypheral blood HMM state annotation
roadmap_15statesHMM_CD19 = "E032_15_coreMarks_mnemonics.bed.gz"
os.system("tar zxvf {0} {1}".format(roadmap_15statesHMM, roadmap_15statesHMM_CD19))
os.system("gzip -d {0}".format(roadmap_15statesHMM_CD19))
os.system("mv E032_15_coreMarks_mnemonics.bed ../data/E032_15_coreMarks_mnemonics.bed")
# Footprinting
# get all jaspar motifs
"wget http://jaspar.genereg.net/html/DOWNLOAD/JASPAR_CORE/pfm/nonredundant/pfm_all.txt"
jaspar = motifs.parse(open("data/external/pfm_all.txt", 'r'), "jaspar")
# motif annotation
"wget http://jaspar.genereg.net/html/DOWNLOAD/database/MATRIX.txt"
annot = pd.read_table("data/external/MATRIX.txt", names=["index", "db", "id", 0, "TF"])
# get species annotation
"wget http://jaspar.genereg.net/html/DOWNLOAD/database/MATRIX_SPECIES.txt"
spec = pd.read_table("data/external/MATRIX_SPECIES.txt", names=["index", "species_id"])
# merge both
annot = pd.merge(annot, spec, on=['index'])
# get ids of only human motifs
human_annot = annot[annot['species_id'] == "9606"]
# filter out any not uniquely mappable gene name
human_annot = human_annot[
(~human_annot['TF'].str.contains("\(")) &
def read_jaspar_pwms(file='jaspar/pfm_vertebrates.txt', dir='d:/sequence'):
with open(os.path.join(dir, file), 'r') as h:
jaspar = {m.name.strip().split(':')[0].upper(): m.counts.normalize().log_odds() for m in motifs.parse(h, 'jaspar')}
return jaspar
def create_plot(
meme_file,
motif_number,
flanking_sites,
sample_phylop_file,
control_phylop_file,
sample_gerp_file,
control_gerp_file,
peak_file,
fimo_file,
annotate,
):
handle = open(meme_file)
records = motifs.parse(handle, "meme")
record = records[motif_number - 1]
num_occurrences = getattr(record, "num_occurrences", "Unknown")
sample_phylo_data = None
control_phylo_data = None
sample_gerp_data = None
control_gerp_data = None
annotate_dict = None
if annotate == "" or annotate == " ":
annotate = None
elif annotate:
with open(annotate) as f:
annotate_dict = json.load(f)
handle = open(sample_phylop_file, "r")
sample_phylo_data = csv.reader(handle, delimiter="\t")
handle = open(control_phylop_file, "r")
control_phylo_data = csv.reader(handle, delimiter="\t")
if sample_gerp_file and control_gerp_file:
handle = open(sample_gerp_file, "r")
sample_gerp_data = csv.reader(handle, delimiter="\t")
handle = open(control_gerp_file, "r")
control_gerp_data = csv.reader(handle, delimiter="\t")
sample_phylo_scores = []
for line in sample_phylo_data:
sample_phylo_scores.append(float(line[1]))
control_phylo_scores = []
for line in control_phylo_data:
control_phylo_scores.append(float(line[1]))
if sample_gerp_data:
sample_gerp_scores = []
for line in sample_gerp_data:
sample_gerp_scores.append(float(line[1]))
control_gerp_scores = []
for line in control_gerp_data:
control_gerp_scores.append(float(line[1]))
assert len(sample_phylo_scores) == len(control_phylo_scores)
handle.close()
profile = position_wise_profile(getattr(record, score_type), record.length)
max_occur = find_max_occurence(profile, max_count=1)
## motif_scores is tn array of scores of the max occuring base at each position of the motif
motif_scores = []
for position in max_occur:
motif_scores.append(position[0][1])
motif_scores = np.asarray(motif_scores)
sample_phylo_scores = np.asarray(sample_phylo_scores)
control_phylo_scores = np.asarray(control_phylo_scores)
if sample_gerp_data:
sample_gerp_scores = np.asarray(sample_gerp_scores)
control_gerp_scores = np.asarray(control_gerp_scores)
motif_junk = [0 for i in range(0, flanking_sites)]
motif_junk = np.asarray(motif_junk)
motif_concat = np.concatenate((motif_junk, motif_scores))
motif_concat = np.concatenate((motif_concat, motif_junk))
##Mean of flanking sites
ms_p = np.mean(np.concatenate((sample_phylo_scores[0:flanking_sites], sample_phylo_scores[-flanking_sites:])))
mc_p = np.mean(np.concatenate((control_phylo_scores[0:flanking_sites], control_phylo_scores[-flanking_sites:])))
if sample_gerp_data:
ms_g = np.mean(np.concatenate((sample_gerp_scores[0:flanking_sites], sample_gerp_scores[-flanking_sites:])))
mc_g = np.mean(np.concatenate((control_gerp_scores[0:flanking_sites], control_gerp_scores[-flanking_sites:])))
flanking_sample_gerp_scores = np.concatenate(
(sample_gerp_scores[0:flanking_sites], sample_gerp_scores[-flanking_sites:])
)
flanking_control_gerp_scores = np.concatenate(
(control_gerp_scores[0:flanking_sites], control_gerp_scores[-flanking_sites:])
)
motif_control_gerp_scores = control_gerp_scores[flanking_sites:-flanking_sites]
motif_sample_gerp_scores = sample_gerp_scores[flanking_sites:-flanking_sites]
flanking_sample_phylo_scores = np.concatenate(
(sample_phylo_scores[0:flanking_sites], sample_phylo_scores[-flanking_sites:])
)
flanking_control_phylo_scores = np.concatenate(
(control_phylo_scores[0:flanking_sites], control_phylo_scores[-flanking_sites:])
)
motif_control_phylo_scores = control_phylo_scores[flanking_sites:-flanking_sites]
motif_sample_phylo_scores = sample_phylo_scores[flanking_sites:-flanking_sites]
if flanking_sites > 0:
shifted_sample_phylo_scores = sample_phylo_scores[flanking_sites:-flanking_sites] - ms_p
shifted_control_phylo_scores = control_phylo_scores[flanking_sites:-flanking_sites] - mc_p
if sample_gerp_data:
shifted_sample_gerp_scores = sample_gerp_scores[flanking_sites:-flanking_sites] - ms_g
shifted_control_gerp_scores = control_gerp_scores[flanking_sites:-flanking_sites] - mc_g
else:
shifted_sample_phylo_scores = sample_phylo_scores
shifted_control_phylo_scores = control_phylo_scores
if sample_gerp_data:
shifted_sample_gerp_scores = sample_gerp_scores
shifted_control_gerp_scores = control_gerp_scores
pr_p = pearsonr(motif_scores, motif_sample_phylo_scores)
if sample_gerp_data:
pr_g = pearsonr(motif_scores, motif_sample_gerp_scores)
## H_0: Mean phylop scores for motif sites and flanking sites are the same
## H_!: Mean phylop score for motif sites > Mean phylop score of flanking sites
## NOTE: the perform_t_test functions returns a 2 tailed p-value forn independet t-test with unequal sample size, eqaul variances
T_deltaphylop, p_deltaphylop = perform_t_test(motif_sample_phylo_scores, flanking_sample_phylo_scores)
delta_phylop = np.mean(motif_sample_phylo_scores) - np.mean(
flanking_sample_phylo_scores
) # -shifted_control_phylo_scores)
if sample_gerp_data:
T_deltagerp, p_deltagerp = perform_t_test(motif_sample_gerp_scores, flanking_sample_gerp_scores)
delta_gerp = np.mean(motif_sample_gerp_scores) - np.mean(flanking_sample_gerp_scores)
if T_deltagerp < 0:
p_deltagerp = 1 - p_deltagerp * 0.5
else:
p_deltagerp = p_deltagerp * 0.5
if T_deltaphylop < 0:
p_deltaphylop = 1 - p_deltaphylop * 0.5
else:
p_deltaphylop = p_deltaphylop * 0.5
## Ordinary least square fit for phylop scores and motif_scores
reg_phylop_sample = sm.OLS(motif_sample_phylo_scores, sm.add_constant(motif_scores)).fit()
if len(reg_phylop_sample.params) < 2:
y_reg_phylop_sample = motif_scores
else:
y_reg_phylop_sample = motif_scores * reg_phylop_sample.params[1] + reg_phylop_sample.params[0]
reg_phylop_control = sm.OLS(motif_control_phylo_scores, sm.add_constant(motif_scores)).fit()
if len(reg_phylop_control.params) < 2:
y_reg_phylop_control = motif_scores
else:
y_reg_phylop_control = motif_scores * reg_phylop_control.params[1] + reg_phylop_control.params[0]
if sample_gerp_data:
reg_gerp_sample = sm.OLS(motif_sample_gerp_scores, sm.add_constant(motif_scores)).fit()
if len(reg_gerp_sample.params) == 1:
y_reg_gerp_sample = motif_scores
else:
y_reg_gerp_sample = motif_scores * reg_gerp_sample.params[1] + reg_gerp_sample.params[0]
reg_gerp_control = sm.OLS(motif_control_gerp_scores, sm.add_constant(motif_scores)).fit()
if len(reg_gerp_control.params) == 1:
y_reg_gerp_control = motif_scores
else:
y_reg_gerp_control = motif_scores * reg_gerp_control.params[1] + reg_gerp_control.params[0]
motif = record
motif_length = motif.length
meme_dir = os.path.dirname(meme_file)
X = [40 + 15] ## this is by trial and error, the position for the first base logo
logo = plt.imread(os.path.join(meme_dir, "logo{}.png".format(motif_number)))
## Generate all other X coordinates
fs = flanking_sites
for j in range(1, len(motif) + 2 * fs):
t = X[j - 1] + a + 1.9
X.append(t)
motif_bits = []
for i in range(0, motif.length):
s = 0
for base in bases:
s = s + -motif.pwm[base][i] * log(motif.pwm[base][i], 2) if motif.pwm[base][i] != 0 else s
s = 2 - s
motif_bits.append(s)
y_phylop_pixels = [__scale__ * x for x in sample_phylo_scores] # [fs:-fs]]#[flanking_sites:-flanking_sites]]
##FIXME This is a big dirty hacl to get thegenerate plots for the Reverse complement logo too
logo_name = ["logo{}.png".format(motif_number), "logo_rc{}.png".format(motif_number)]
for ln in logo_name:
if "rc" in ln:
y_phylop_pixels.reverse()
logo = plt.imread(os.path.join(meme_dir, ln))
height_px = logo.shape[0] # Should be 212
if sample_gerp_data:
if annotate:
total_px = X[-1] + 8 * height_px + 140
right = (8 * height_px + 10 + 140 - 0.2 * height_px) / total_px
else:
total_px = X[-1] + 6 * height_px + 140
right = (6 * height_px + 10 + 140 - 0.2 * height_px) / total_px
else:
if annotate:
total_px = X[-1] + 6 * height_px + 140
right = (6 * height_px + 10 + 140 - 0.2 * height_px) / total_px
else:
total_px = X[-1] + 4 * height_px + 140
right = (4 * height_px + 10 + 140 - 0.2 * height_px) / total_px
figsize = (total_px / 100, (2 * height_px) / 100 + 0.6)
gs = gridspec.GridSpec(2, 1) # , width_ratios=[1, right], height_ratios=[1,1])
gs.update(
top=1.0, bottom=0.14, left=0.08, right=1 - right
) # , right=0.8)#, left=0.06)#, right=right, wspace=0.025, hspace=0.03, wd)
f = plt.figure(figsize=figsize, dpi=dpi, facecolor="w", edgecolor="k")
# ax => Logo
# stem_plot => Trend
# gerp_scatter_plot => Phylop
# enrichment_plot => Gerp
logo_plot = plt.Subplot(f, gs[0])
##TODO Check this
if motif_length > 45:
XSCALE_FACTOR = motif_length / 1.9
z = 2
elif motif_length > 40:
XSCALE_FACTOR = motif_length / 2.25
z = 2.5
elif motif_length > 36:
XSCALE_FACTOR = motif_length / 1.95
z = 2
elif motif_length > 21:
XSCALE_FACTOR = motif_length / 5
z = 3
else:
XSCALE_FACTOR = 4.5
z = 3
logo_plot.imshow(
logo, extent=[40 + 15 + z * (a + 1.9), logo.shape[1] + 15 + XSCALE_FACTOR * (a + 1.9), 0, logo.shape[0]]
)
logo_plot.set_axis_off()
f.add_subplot(logo_plot)
stem_plot = plt.Subplot(f, gs[1], sharex=logo_plot)
markerline, stemlines, baseline = stem_plot.stem(
X[:fs],
[y for y in y_phylop_pixels[:fs]],
markerfmt="_",
linefmt="-",
markerfacecolor=flankingstemcolor,
color=greycolor,
)
setp(stemlines, "color", flankingstemcolor)
setp(markerline, "markerfacecolor", flankingstemcolor)
setp(markerline, "color", flankingstemcolor)
setp(stemlines, "linewidth", linewidth)
setp(markerline, "markersize", markersize)
setp(baseline, "linewidth", linewidth - 0.5)
setp(markerline, "markeredgewidth", markeredgewidth)
markerline, stemlines, baseline = stem_plot.stem(
X[fs:-fs], [y for y in y_phylop_pixels[fs:-fs]], markerfmt="g_", linefmt="g-", basefmt="r-"
)
setp(stemlines, "linewidth", linewidth)
setp(markerline, "markersize", markersize)
setp(markerline, "markeredgewidth", markeredgewidth)
setp(baseline, "linewidth", linewidth - 0.5)
markerline, stemlines, baseline = stem_plot.stem(
X[-fs:],
[y for y in y_phylop_pixels[-fs:]],
markerfmt="_",
linefmt="-",
markerfacecolor=flankingstemcolor,
color=greycolor,
)
setp(stemlines, "color", flankingstemcolor)
setp(markerline, "markerfacecolor", flankingstemcolor)
setp(stemlines, "linewidth", linewidth)
setp(markerline, "markersize", markersize)
setp(markerline, "markeredgewidth", markeredgewidth)
setp(markerline, "color", flankingstemcolor)
setp(baseline, "linewidth", linewidth - 0.5)
indices_str = []
indices1 = np.linspace(-fs, -1, 2)
for i in indices1:
indices_str.append("")
indices2 = np.arange(0, len(X) - 2 * fs, 5)
for i in indices2:
indices_str.append("${}$".format(int(i) + 1))
indices3 = np.linspace(motif_length, motif_length + fs - 1, 2)
for i in indices3:
indices_str.append("")
indices12 = np.concatenate((indices1, indices2))
indices = np.concatenate((indices12, indices3))
xticks = [X[int(i) + fs] for i in indices]
max_yticks = 3
yloc = plt.MaxNLocator(max_yticks)
stem_plot.yaxis.set_major_locator(yloc)
# ticks_and_labels = np.linspace(1.02*min(min(y_phylop_pixels), -0.1), 1.02*max(y_phylop_pixels), num = 5, endpoint=True)
# stem_plot.set_yticks(ticks_and_labels)
# stem_plot.set_yticklabels(['$%.2f$' %x for x in ticks_and_labels])#(["%0.2f"%(min(y_phylop_pixels)/__scale__), "%0.2f"%(np.mean(y_phylop_pixels)/__scale__), "%0.2f"%(max(y_phylop_pixels)/__scale__)], fontsize=fontsize)
stem_plot.set_xlabel("$\mathrm{Base}\ \mathrm{Position}$", fontsize=fontsize, fontweight="bold")
stem_plot.set_xlim([1.2 * a, X[-1] + linewidth * 1.8])
stem_plot.set_ylim([min(np.min(y_phylop_pixels), -0.01) - 0.03, np.max(y_phylop_pixels, 0.01)])
stem_plot.get_xaxis().tick_bottom()
stem_plot.get_yaxis().tick_left()
stem_plot.set_xticks(xticks)
stem_plot.set_xticklabels(indices_str, fontsize=fontsize)
stem_plot.spines["top"].set_visible(False)
stem_plot.spines["right"].set_visible(False)
stem_plot.yaxis.set_ticks_position("left")
stem_plot.xaxis.set_ticks_position("bottom")
stem_plot.spines["left"].set_position("zero")
# stem_plot.spines['bottom'].set_position(matplotlib.transforms.Bbox(array([[0.125,0.63],[0.25,0.25]])))
stem_plot.get_yaxis().set_tick_params(direction="out")
stem_plot.get_xaxis().set_tick_params(direction="out")
stem_plot.tick_params(axis="y", which="major", pad=tickpad)
stem_plot.tick_params(axis="x", which="major", pad=tickpad)
stem_plot.tick_params("both", length=ticklength, width=2, which="major")
stem_plot.set_ylabel("$\mathrm{PhyloP}\ \mathrm{Score}$", fontsize=fontsize)
f.add_subplot(stem_plot)
if sample_gerp_data:
if annotate:
gs1 = gridspec.GridSpec(2, 4, height_ratios=[1, 4], width_ratios=[1, 1, 1, 1])
gerp_header_subplot_gs = gs1[0, 1]
gerp_subplot_gs = gs1[1, 1]
histogram_header_subplot_gs = gs1[0, 2]
histogram_subplot_gs = gs1[1, 2]
ann_header_subplot_gs = gs1[0, 3]
ann_subplot_gs = gs1[1, 3]
else:
gs1 = gridspec.GridSpec(2, 3, height_ratios=[1, 4], width_ratios=[1, 1, 1])
gerp_header_subplot_gs = gs1[0, 1]
gerp_subplot_gs = gs1[1, 1]
histogram_header_subplot_gs = gs1[0, 2]
histogram_subplot_gs = gs1[1, 2]
else:
if annotate:
gs1 = gridspec.GridSpec(2, 3, height_ratios=[1, 4], width_ratios=[1, 1, 1])
histogram_header_subplot_gs = gs1[0, 1]
histogram_subplot_gs = gs1[1, 1]
ann_header_subplot_gs = gs1[0, 2]
ann_subplot_gs = gs1[1, 2]
else:
gs1 = gridspec.GridSpec(2, 2, height_ratios=[1, 4], width_ratios=[1, 1])
histogram_header_subplot_gs = gs1[0, 1]
histogram_subplot_gs = gs1[1, 1]
gs1.update(bottom=0.14, right=0.95, left=1 - right * 0.85, wspace=0.5)
phlyop_plots_leg = plt.Subplot(f, gs1[0, 0], autoscale_on=True)
pearsonr_pval = str("%.1g" % pr_p[1])
if "e" in pearsonr_pval:
pearsonr_pval += "}"
pearsonr_pval = pearsonr_pval.replace("e", "*10^{").replace("-0", "-")
score_pval = str("%.1g" % p_deltaphylop)
if "e" in score_pval:
score_pval += "}"
score_pval = score_pval.replace("e", "*10^{").replace("-0", "-")
textstr = r"\noindent$R_{pearson}=%.2f$($p=%s$)\\~\\$\Delta_{Phylop}=%.2f$($p=%s$)\\~\\" % (
pr_p[0],
pearsonr_pval,
delta_phylop,
score_pval,
) # , reg_phylop_control.rsquared, num_occurrences*reg_phylop_control.params[1])
txtx = 1 - legend_xmultiplier * len(textstr) / 100.0
phlyop_plots_leg.set_frame_on(False)
phlyop_plots_leg.set_xticks([])
phlyop_plots_leg.set_yticks([])
phlyop_plots_leg.text(txtx, txty, textstr, fontsize=legend_fontsize)
f.add_subplot(phlyop_plots_leg)
phylop_scatter_plot = plt.Subplot(f, gs1[1, 0], autoscale_on=True)
fit = np.polyfit(motif_scores, motif_sample_phylo_scores, 1)
fit_fn = np.poly1d(fit)
phylop_scatter_plot.scatter(
motif_scores, motif_sample_phylo_scores, color="g", s=[pointsize for i in motif_scores]
)
phylop_scatter_plot.plot(
motif_scores,
y_reg_phylop_sample,
"g",
motif_scores,
fit_fn(motif_scores),
color="g",
linewidth=plot_linewidth,
)
phylop_scatter_plot.scatter(
motif_scores, motif_control_phylo_scores, color=greycolor, s=[pointsize for i in motif_scores]
)
phylop_scatter_plot.plot(motif_scores, y_reg_phylop_control, color=greycolor, linewidth=plot_linewidth)
ticks_and_labels = np.linspace(1.02 * min(motif_scores), 1.02 * max(motif_scores), num=5, endpoint=True)
phylop_scatter_plot.set_xticks(ticks_and_labels)
ticks_and_labels = ["$%.2f$" % (x / num_occurrences) for x in ticks_and_labels]
phylop_scatter_plot.set_xticklabels(ticks_and_labels)
##max_xticks = 5
##xloc = plt.MaxNLocator(max_xticks)
##print xloc
##phylop_scatter_plot.xaxis.set_major_locator(xloc)
# ticks_and_labels = np.linspace(1.02*min(min(shifted_sample_phylo_scores), min(shifted_control_phylo_scores)), 1.02*max(max(shifted_sample_phylo_scores),max(shifted_control_phylo_scores)),
# num = 4, endpoint=True)
# phylop_scatter_plot.set_yticks(ticks_and_labels)
# phylop_scatter_plot.set_yticklabels(["$%0.2f$"%x for x in ticks_and_labels])
max_yticks = 4
yloc = plt.MaxNLocator(max_yticks)
phylop_scatter_plot.yaxis.set_major_locator(yloc)
phylop_scatter_plot.set_xlabel("$\mathrm{Base}\ \mathrm{Frequency}$", fontsize=fontsize, fontweight="bold")
phylop_scatter_plot.get_xaxis().tick_bottom()
phylop_scatter_plot.get_yaxis().tick_left()
phylop_scatter_plot.set_ylabel("$\mathrm{PhyloP}\ \mathrm{Score}$", fontsize=fontsize, fontweight="bold")
phylop_scatter_plot.tick_params(axis="y", which="major", pad=tickpad)
phylop_scatter_plot.tick_params(axis="x", which="major", pad=tickpad)
phylop_scatter_plot.get_yaxis().set_tick_params(direction="out")
phylop_scatter_plot.get_xaxis().set_tick_params(direction="out")
phylop_scatter_plot.tick_params("both", length=ticklength, width=2, which="major")
f.add_subplot(phylop_scatter_plot)
gerp_plots_leg = plt.Subplot(f, gerp_header_subplot_gs, autoscale_on=True)
gerp_plots_leg.set_frame_on(False)
gerp_plots_leg.set_xticks([])
gerp_plots_leg.set_yticks([])
pearsonr_pval = str("%.1g" % pr_p[1])
if "e" in pearsonr_pval:
pearsonr_pval += "}"
pearsonr_pval = pearsonr_pval.replace("e", "*10^{").replace("-0", "-")
if sample_gerp_data:
score_pval = str("%.1g" % p_deltagerp)
if "e" in score_pval:
score_pval += "}"
score_pval = score_pval.replace("e", "*10^{").replace("-0", "-")
textstr = r"\noindent$R_{pearson}=%.2f$($p=%s$)\\~\\$\Delta_{{Gerp}}=%.2f$($p=%s$)\\~\\" % (
pr_g[0],
pearsonr_pval,
delta_gerp,
score_pval,
)
txtx = 1 - legend_xmultiplier * len(textstr) / 100.0
gerp_plots_leg.text(txtx, txty, textstr, fontsize=legend_fontsize)
f.add_subplot(gerp_plots_leg)
gerp_scatter_plot = plt.Subplot(f, gerp_subplot_gs, autoscale_on=True)
gerp_scatter_plot.scatter(
motif_scores, motif_sample_gerp_scores, color="g", s=[pointsize for i in motif_scores]
)
gerp_scatter_plot.plot(motif_scores, y_reg_gerp_sample, color="g", linewidth=plot_linewidth)
gerp_scatter_plot.scatter(
motif_scores, motif_control_gerp_scores, color=greycolor, s=[pointsize for i in motif_scores]
)
gerp_scatter_plot.plot(motif_scores, y_reg_gerp_control, color=greycolor, linewidth=plot_linewidth)
ticks_and_labels = np.linspace(1.02 * min(motif_scores), 1.02 * max(motif_scores), num=5, endpoint=True)
gerp_scatter_plot.set_xticks(ticks_and_labels)
ticks_and_labels = ["$%.2f$" % (x / num_occurrences) for x in ticks_and_labels]
gerp_scatter_plot.set_xticklabels(ticks_and_labels)
##max_xticks = 5
##xloc = plt.MaxNLocator(max_xticks)
##gerp_scatter_plot.xaxis.set_major_locator(xloc)
max_yticks = 4
yloc = plt.MaxNLocator(max_yticks)
gerp_scatter_plot.yaxis.set_major_locator(yloc)
gerp_scatter_plot.set_xlabel("$\mathrm{Base}\ \mathrm{Frequency}$", fontsize=fontsize, fontweight="bold")
gerp_scatter_plot.set_ylabel("$\mathrm{GERP}\ \mathrm{Score}$", fontsize=fontsize, fontweight="bold")
gerp_scatter_plot.get_xaxis().tick_bottom()
gerp_scatter_plot.get_yaxis().tick_left()
gerp_scatter_plot.get_yaxis().set_tick_params(direction="out")
gerp_scatter_plot.get_xaxis().set_tick_params(direction="out")
gerp_scatter_plot.tick_params(axis="y", which="major", pad=tickpad)
gerp_scatter_plot.tick_params(axis="x", which="major", pad=tickpad)
gerp_scatter_plot.tick_params("both", length=ticklength, width=2, which="major")
f.add_subplot(gerp_scatter_plot)
enrichment_plot4 = plt.Subplot(f, histogram_header_subplot_gs, autoscale_on=True)
enrichment_plot4.set_frame_on(False)
enrichment_plot4.set_xticks([])
enrichment_plot4.set_yticks([])
all_distances = get_motif_distances(peak_file, fimo_file)
fimo_dir = os.path.dirname(fimo_file)
motifs_within_100 = filter(lambda x: x <= 100 and x >= -100, all_distances)
motifs_within_100_200 = filter(lambda x: (x < 200 and x > 100) or (x > -200 and x < -100), all_distances)
if len(motifs_within_100_200) > 0:
enrichment = len(motifs_within_100) / (len(motifs_within_100_200)) # +len(motifs_within_100))
else:
enrichment = 1
enrichment_pval = 0
number_of_sites = len(motifs_within_100) + len(motifs_within_100_200) # fimo_sites_intersect(parsed.fimo_file)
probability = 200 / (ENRICHMENT_SEQ_LENGTH - motif_length)
enrichment_pval = binom.sf(len(motifs_within_100), number_of_sites, probability)
enrichment_pval = str("%.1g" % enrichment_pval)
if "e" in enrichment_pval:
enrichment_pval += "}"
enrichment_pval = enrichment_pval.replace("e", "*10^{").replace("-0", "-")
textstr = r"\noindent$Enrichment={0:.2f}$\\~\\$(p={1})$".format(enrichment, enrichment_pval)
txtx = 0.1 * len(textstr) / 100.0
enrichment_plot4.text(txtx, txty, textstr, fontsize=legend_fontsize)
f.add_subplot(enrichment_plot4)
enrichment_plot = plt.Subplot(f, histogram_subplot_gs, autoscale_on=True)
enrichment_plot.hist(all_distances, histogram_nbins, color="white", alpha=0.8, range=[-200, 200])
enrichment_plot.set_xticks([-200, -100, 0, 100, 200])
max_yticks = 3
yloc = plt.MaxNLocator(max_yticks)
enrichment_plot.yaxis.set_major_locator(yloc)
# enrichment_plot.set_yticks(range(1,6))
ticks_and_labels = [-200, -100, 0, 100, 200]
all_distances = np.asarray(all_distances)
enrichment_plot.set_xticklabels(["${}$".format(x) for x in ticks_and_labels])
enrichment_plot.tick_params(axis="y", which="major", pad=tickpad)
enrichment_plot.tick_params(axis="x", which="major", pad=tickpad)
enrichment_plot.tick_params("both", length=ticklength, width=2, which="major")
enrichment_plot.get_xaxis().tick_bottom()
enrichment_plot.get_yaxis().tick_left()
enrichment_plot.get_yaxis().set_tick_params(direction="out")
enrichment_plot.get_xaxis().set_tick_params(direction="out")
enrichment_plot.axvline(x=-100, linewidth=3, color="red", linestyle="-.")
enrichment_plot.axvline(x=100, linewidth=3, color="red", linestyle="-.")
f.add_subplot(enrichment_plot)
if "rc" not in ln:
out_file = os.path.join(fimo_dir, "motif{}Combined_plots.png".format(motif_number))
out_file = "motif{}Combined_plots.png".format(motif_number)
else:
out_file = os.path.join(fimo_dir, "motif{}Combined_plots_rc.png".format(motif_number))
out_file = "motif{}Combined_plots_rc.png".format(motif_number)
if annotate:
filename = r"$" + annotate[0] + "$"
try:
a_motif = r"$" + annotate[1] + "$"
except IndexError:
a_motif = ""
try:
cell_line = r"$" + annotate[2] + "$"
except IndexError:
cell_line = ""
try:
assay = r"$" + annotate[3] + "$"
except IndexError:
assay = ""
# data = [[r'$Filename$', filename], [r'$Motif$', a_motif], [r'$Cell\ Line$', cell_line], [r'Assay', assay]]
keys = ["title", "gene_name", "dataset", "assembly"]
data = [[r"$" + key.replace("_", " ").upper() + "$", r"$" + annotate_dict[key] + "$"] for key in keys]
ann_header = plt.Subplot(f, ann_header_subplot_gs, autoscale_on=True)
ann_header.set_frame_on(False)
ann_header.set_xticks([])
ann_header.set_yticks([])
f.add_subplot(ann_header)
textstr = r"$Metadata$"
txtx = 1.7 * len(textstr) / 100.0
ann_header.text(txtx, txty, textstr, fontsize=legend_fontsize)
ann_plot = plt.Subplot(f, ann_subplot_gs, autoscale_on=True)
ann_plot.set_xticks([])
ann_plot.set_yticks([])
ann_plot.set_frame_on(False)
table = ann_plot.table(cellText=data, loc="center")
table.scale(1, 2)
fontproperties = FontProperties(size=legend_fontsize * 8) # , family='serif' )
for key, cell in table.get_celld().items():
row, col = key
if row > 0 and col > 0:
cell.set_text_props(fontproperties=fontproperties)
table.set_fontsize(legend_fontsize * 8)
f.add_subplot(ann_plot)
f.savefig(out_file, figsize=figsize, dpi=dpi)
if line.strip():
fixed_pfm_file.write(line)
fixed_pfm_file.close()
# Output is printed to stdout to enable pipes to other process, etc. The output gives the effect of each possible mutation
# for each jaspar pfm. The file is tab delimited, with a header to make for easy reading into R or other downstream analyses.
# Each line of the output has the following fields:
#
# 1) name -- the name of the pfm in JASPAR
# 2) pos -- the relative position within the matrix. The value is from -1 to 1, where 0 is the center of the motif.
# 3-8) NN -- the change in pssm score associated with each possible mutation at that position in the motif.
#
print "name\tpos\tIC\tDegCons\tAG\tCT\tAC\tAT\tCG\tGT"
with open("pfm_all.fixed.txt") as handle:
for m in motifs.parse(handle, "jaspar"):
#
# Get the counts and the consensus motif for the pfm
#
counts = m.counts
cons = m.consensus
deg_cons = m.degenerate_consensus
#
# convert to pssm, adding a pseudocount of 0.1 to each base.
#
pssm = m.counts.normalize(pseudocounts=0.1).log_odds()
cons_score = pssm.calculate(cons)
cons_list = list(cons)
cons_str = str(cons)
# read in name of input newick file and motif file
inputnewick_file = sys.argv[1]
inputmotifs = sys.argv[2]
# read in name of output file
outputfile = sys.argv[3]
# read in name of mRNA
gene = sys.argv[4]
# open file
handle = open(inputmotifs)
# read in motif information
results = motifs.parse(handle, "meme")
# close file after reading in motif info
handle.close()
# create list to store motifs
motif_branch_lengths_list = []
# read in tree
tree = Phylo.read(inputnewick_file, "newick")
# create and print cutoff value
cutoff = (60/ (math.log10(0.01) + 100))
print ("cutoff = " + str(cutoff))
# function to calculate branch length of Drosophila with motifs
def get_motifs(meme_file):
handle = open(meme_file, 'r')
records = motifs.parse(handle, 'meme')
total_motifs = len(records)
return total_motifs
from uuid import uuid4
from celery import Celery
from peaks_processor_celery import run_conservation_analysis, run_motif_analysis, run_analysis
from flask.ext.sqlalchemy import SQLAlchemy
import shutil
import json
from celery import group
from config_processor import read_config
from encode_peak_file_downloader import get_encode_peakfiles, get_metadata_for_peakfile
import subprocess
from bed_operations.format_peakfile import convert_to_scorefile
from query import get_async_id, encode_job_exists, insert_encode_job, update_job_status, insert_new_job, get_encode_metadata, get_filename, get_job_status, job_exists, encode_job_status, get_encode_jobid, is_job_type_encode,get_encode_from_jobid, get_all_encode_results
from database import SqlAlchemyTask
import operator
from Bio import motifs
jaspar_motifs = motifs.parse(open('../data/pfm_vertebrates.txt'), 'jaspar')
server_config = read_config('Server')
path_config = read_config('StaticPaths')
app = Flask(__name__)
app.config['CELERY_BROKER_URL'] = server_config['celery_broker_url']
app.config['CELERY_RESULT_BACKEND'] = server_config['celery_result_backend']
app.config['SQLALCHEMY_DATABASE_URI'] = server_config['sqlalchemy_database_uri']
app.config['CELERYD_MAX_TASKS_PER_CHILD'] = server_config['celery_max_tasks_per_child']
app.config['CELERY_IMPORTS'] = ('app',)
app.config['CELERYD_TASK_TIME_LIMIT'] = 1000000
app.url_map.strict_slashes = False
import Bio
sys.path.insert(0,'/mnt/lustre/home/cusanovich/Programs/lib/python2.6/site-packages/Bio')
from Bio import motifs
from Bio.Seq import Seq
from Bio.Alphabet import IUPAC
import numpy
from datetime import datetime
fasta = pybedtools.BedTool('/mnt/lustre/data/share/HumanGenome/allhg18_norandom.fasta')
matrices = open('/data/share/TRANSFAC/2011.3_nb/dat/matrix.dat','r')
bed = open('/mnt/lustre/home/cusanovich/centipede/jack_centipede_sorted.bed','r')
outbed = open('/mnt/lustre/home/cusanovich/centipede/jack_centipede_sorted_pwms_timedright2.bed','w')
#liner = ['chr1','847521','847534','M01066','0.9675','-']
d = pybedtools.BedTool("""chr1 840146 840165""", from_string=True)
genome = d.sequence(fi=fasta)
test = motifs.parse(matrices,"TRANSFAC")
motifers = []
for i in range(len(test)):
test[i].pseudocounts = 0.5
motifers.append(test[i]['AC'])
matrices.close()
x=1
pssms = {}
for line in bed:
liner = line.strip().split()
motifed = motifers.index(liner[3])
#For some reason, Jack and Roger's bed files seem to be off in coordinates?!?!?!
testbed = liner[0] + ':' + str(int(liner[1])-2) + '-' + str(int(liner[2])+2)
testseq = Seq(genome.seq(testbed,fasta),IUPAC.unambiguous_dna)
if liner[5] == '-':
## fetch_motifs method: fetch those which match some criteria
## any of the meta, min_ic (minimum information content), minimum length of matrix, minimum num of sites to construct it
#motfs = jdb.fetch_motifs( collection='CORE', tax_group=['vertebrates','insects'], min_ic=12)
#for motif in motfs:
#print 'do something with the motif'
print motifs.jaspar.calculate_pseudocounts(arnt) # create new calculated pseudocounts
print arnt.pseudocounts # usually zeros
arnt.pseudocounts = motifs.jaspar.calculate_pseudocounts(arnt)
print arnt.pseudocounts
print arnt.counts#, arnt.pssm()
#MEME
# input DNA or protein seqs, output number of motifs requested
with open('meme.txt','r') as handle:
motfs = motifs.parse(handle,'meme') # motif meme format
#motfs is an object of Bio.motifs.meme.Record class, list of Motif objects
#attributes
print motfs.version, motfs.datafile, motfs.command, motfs.alphabet
print motfs.sequences #list of names
print len(motfs) # number of motifs
for motif in motfs:
#attributes
print motif.name, motif.num_occurrences, motif.length, motif.evalue
print motif.consensus
print motif.degenerate_consensus
print len(motif.instances), motif.instances[0], motif.instances[0].start
print motif.instances[0].pvalue
print motfs[0] # by index
print motfs['Motif 2'] # by name