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def create(cls, name, ncpus=None):
"""Create a Moap instance based on the predictor name. Parameters name : str ncpus : int, optional Number of threads. Default is the number specified in the config. Returns ------- moap : Moap instance moap instance. """ |
try:
return cls._predictors[name.lower()](ncpus=ncpus)
except KeyError:
raise Exception("Unknown class") |
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def register_predictor(cls, name):
"""Register method to keep list of predictors.""" |
def decorator(subclass):
"""Register as decorator function."""
cls._predictors[name.lower()] = subclass
subclass.name = name.lower()
return subclass
return decorator |
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def list_classification_predictors(self):
"""List available classification predictors.""" |
preds = [self.create(x) for x in self._predictors.keys()]
return [x.name for x in preds if x.ptype == "classification"] |
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def _activate(self):
"""Activates the stream.""" |
if six.callable(self.streamer):
# If it's a function, create the stream.
self.stream_ = self.streamer(*(self.args), **(self.kwargs))
else:
# If it's iterable, use it directly.
self.stream_ = iter(self.streamer) |
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| def iterate(self, max_iter=None):
'''Instantiate an iterator.
Parameters
----------
max_iter : None or int > 0
Maximum number of iterations to yield.
If ``None``, exhaust the stream.
Yields
------
obj : Objects yielded by the streamer provided on init.
See Also
--------
cycle : force an infinite stream.
'''
# Use self as context manager / calls __enter__() => _activate()
with self as active_streamer:
for n, obj in enumerate(active_streamer.stream_):
if max_iter is not None and n >= max_iter:
break
yield obj |
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| def cycle(self, max_iter=None):
'''Iterate from the streamer infinitely.
This function will force an infinite stream, restarting
the streamer even if a StopIteration is raised.
Parameters
----------
max_iter : None or int > 0
Maximum number of iterations to yield.
If `None`, iterate indefinitely.
Yields
------
obj : Objects yielded by the streamer provided on init.
'''
count = 0
while True:
for obj in self.iterate():
count += 1
if max_iter is not None and count > max_iter:
return
yield obj |
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def rank_motifs(stats, metrics=("roc_auc", "recall_at_fdr")):
"""Determine mean rank of motifs based on metrics.""" |
rank = {}
combined_metrics = []
motif_ids = stats.keys()
background = list(stats.values())[0].keys()
for metric in metrics:
mean_metric_stats = [np.mean(
[stats[m][bg][metric] for bg in background]) for m in motif_ids]
ranked_metric_stats = rankdata(mean_metric_stats)
combined_metrics.append(ranked_metric_stats)
for motif, val in zip(motif_ids, np.mean(combined_metrics, 0)):
rank[motif] = val
return rank |
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def write_stats(stats, fname, header=None):
"""write motif statistics to text file.""" |
# Write stats output to file
for bg in list(stats.values())[0].keys():
f = open(fname.format(bg), "w")
if header:
f.write(header)
stat_keys = sorted(list(list(stats.values())[0].values())[0].keys())
f.write("{}\t{}\n".format("Motif", "\t".join(stat_keys)))
for motif in stats:
m_stats = stats.get(str(motif), {}).get(bg)
if m_stats:
f.write("{}\t{}\n".format(
"_".join(motif.split("_")[:-1]),
"\t".join([str(m_stats[k]) for k in stat_keys])
))
else:
logger.warn("No stats for motif {0}, skipping this motif!".format(motif.id))
#motifs.remove(motif)
f.close()
return |
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def get_roc_values(motif, fg_file, bg_file):
"""Calculate ROC AUC values for ROC plots.""" |
#print(calc_stats(motif, fg_file, bg_file, stats=["roc_values"], ncpus=1))
#["roc_values"])
try:
# fg_result = motif.pwm_scan_score(Fasta(fg_file), cutoff=0.0, nreport=1)
# fg_vals = [sorted(x)[-1] for x in fg_result.values()]
#
# bg_result = motif.pwm_scan_score(Fasta(bg_file), cutoff=0.0, nreport=1)
# bg_vals = [sorted(x)[-1] for x in bg_result.values()]
# (x, y) = roc_values(fg_vals, bg_vals)
stats = calc_stats(motif, fg_file, bg_file, stats=["roc_values"], ncpus=1)
(x,y) = list(stats.values())[0]["roc_values"]
return None,x,y
except Exception as e:
print(motif)
print(motif.id)
raise
error = e
return error,[],[] |
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def create_roc_plots(pwmfile, fgfa, background, outdir):
"""Make ROC plots for all motifs.""" |
motifs = read_motifs(pwmfile, fmt="pwm", as_dict=True)
ncpus = int(MotifConfig().get_default_params()['ncpus'])
pool = Pool(processes=ncpus)
jobs = {}
for bg,fname in background.items():
for m_id, m in motifs.items():
k = "{}_{}".format(str(m), bg)
jobs[k] = pool.apply_async(
get_roc_values,
(motifs[m_id], fgfa, fname,)
)
imgdir = os.path.join(outdir, "images")
if not os.path.exists(imgdir):
os.mkdir(imgdir)
roc_img_file = os.path.join(outdir, "images", "{}_roc.{}.png")
for motif in motifs.values():
for bg in background:
k = "{}_{}".format(str(motif), bg)
error, x, y = jobs[k].get()
if error:
logger.error("Error in thread: %s", error)
logger.error("Motif: %s", motif)
sys.exit(1)
roc_plot(roc_img_file.format(motif.id, bg), x, y) |
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def _create_text_report(inputfile, motifs, closest_match, stats, outdir):
"""Create text report of motifs with statistics and database match.""" |
my_stats = {}
for motif in motifs:
match = closest_match[motif.id]
my_stats[str(motif)] = {}
for bg in list(stats.values())[0].keys():
if str(motif) not in stats:
logger.error("####")
logger.error("{} not found".format(str(motif)))
for s in sorted(stats.keys()):
logger.error(s)
logger.error("####")
else:
my_stats[str(motif)][bg] = stats[str(motif)][bg].copy()
my_stats[str(motif)][bg]["best_match"] = "_".join(match[0].split("_")[:-1])
my_stats[str(motif)][bg]["best_match_pvalue"] = match[1][-1]
header = ("# GimmeMotifs version {}\n"
"# Inputfile: {}\n"
).format(__version__, inputfile)
write_stats(my_stats, os.path.join(outdir, "stats.{}.txt"), header=header) |
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def axes_off(ax):
"""Get rid of all axis ticks, lines, etc. """ |
ax.set_frame_on(False)
ax.axes.get_yaxis().set_visible(False)
ax.axes.get_xaxis().set_visible(False) |
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def motif_tree_plot(outfile, tree, data, circle=True, vmin=None, vmax=None, dpi=300):
""" Plot a "phylogenetic" tree """ |
try:
from ete3 import Tree, faces, AttrFace, TreeStyle, NodeStyle
except ImportError:
print("Please install ete3 to use this functionality")
sys.exit(1)
# Define the tree
t, ts = _get_motif_tree(tree, data, circle, vmin, vmax)
# Save image
t.render(outfile, tree_style=ts, w=100, dpi=dpi, units="mm");
# Remove the bottom (empty) half of the figure
if circle:
img = Image.open(outfile)
size = img.size[0]
spacer = 50
img.crop((0,0,size,size/2 + spacer)).save(outfile) |
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def check_bed_file(fname):
""" Check if the inputfile is a valid bed-file """ |
if not os.path.exists(fname):
logger.error("Inputfile %s does not exist!", fname)
sys.exit(1)
for i, line in enumerate(open(fname)):
if line.startswith("#") or line.startswith("track") or line.startswith("browser"):
# comment or BED specific stuff
pass
else:
vals = line.strip().split("\t")
if len(vals) < 3:
logger.error("Expecting tab-seperated values (chromosome<tab>start<tab>end) on line %s of file %s", i + 1, fname)
sys.exit(1)
try:
start, end = int(vals[1]), int(vals[2])
except ValueError:
logger.error("No valid integer coordinates on line %s of file %s", i + 1, fname)
sys.exit(1)
if len(vals) > 3:
try:
float(vals[3])
except ValueError:
pass |
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def check_denovo_input(inputfile, params):
""" Check if an input file is valid, which means BED, narrowPeak or FASTA """ |
background = params["background"]
input_type = determine_file_type(inputfile)
if input_type == "fasta":
valid_bg = FA_VALID_BGS
elif input_type in ["bed", "narrowpeak"]:
genome = params["genome"]
valid_bg = BED_VALID_BGS
if "genomic" in background or "gc" in background:
Genome(genome)
# is it a valid bed-file etc.
check_bed_file(inputfile) # bed-specific, will also work for narrowPeak
else:
sys.stderr.write("Format of inputfile {} not recognized.\n".format(inputfile))
sys.stderr.write("Input should be FASTA, BED or narrowPeak.\n")
sys.stderr.write("See https://genome.ucsc.edu/FAQ/FAQformat.html for specifications.\n")
sys.exit(1)
for bg in background:
if not bg in valid_bg:
logger.info("Input type is %s, ignoring background type '%s'",
input_type, bg)
background = [bg for bg in background if bg in valid_bg]
if len(background) == 0:
logger.error("No valid backgrounds specified!")
sys.exit(1)
return input_type, background |
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def scan_to_best_match(fname, motifs, ncpus=None, genome=None, score=False):
"""Scan a FASTA file with motifs. Scan a FASTA file and return a dictionary with the best match per motif. Parameters fname : str Filename of a sequence file in FASTA format. motifs : list List of motif instances. Returns ------- result : dict Dictionary with motif scanning results. """ |
# Initialize scanner
s = Scanner(ncpus=ncpus)
s.set_motifs(motifs)
s.set_threshold(threshold=0.0)
if genome:
s.set_genome(genome)
if isinstance(motifs, six.string_types):
motifs = read_motifs(motifs)
logger.debug("scanning %s...", fname)
result = dict([(m.id, []) for m in motifs])
if score:
it = s.best_score(fname)
else:
it = s.best_match(fname)
for scores in it:
for motif,score in zip(motifs, scores):
result[motif.id].append(score)
# Close the pool and reclaim memory
del s
return result |
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def set_background(self, fname=None, genome=None, length=200, nseq=10000):
"""Set the background to use for FPR and z-score calculations. Background can be specified either as a genome name or as the name of a FASTA file. Parameters fname : str, optional Name of FASTA file to use as background. genome : str, optional Name of genome to use to retrieve random sequences. length : int, optional Length of genomic sequences to retrieve. The default is 200. nseq : int, optional Number of genomic sequences to retrieve. """ |
length = int(length)
if genome and fname:
raise ValueError("Need either genome or filename for background.")
if fname:
if not os.path.exists(fname):
raise IOError("Background file {} does not exist!".format(fname))
self.background = Fasta(fname)
self.background_hash = file_checksum(fname)
return
if not genome:
if self.genome:
genome = self.genome
logger.info("Using default background: genome {} with length {}".format(
genome, length))
else:
raise ValueError("Need either genome or filename for background.")
logger.info("Using background: genome {} with length {}".format(genome, length))
with Cache(CACHE_DIR) as cache:
self.background_hash = "{}\{}".format(genome, int(length))
fa = cache.get(self.background_hash)
if not fa:
fa = RandomGenomicFasta(genome, length, nseq)
cache.set(self.background_hash, fa)
self.background = fa |
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def set_threshold(self, fpr=None, threshold=None):
"""Set motif scanning threshold based on background sequences. Parameters fpr : float, optional Desired FPR, between 0.0 and 1.0. threshold : float or str, optional Desired motif threshold, expressed as the fraction of the difference between minimum and maximum score of the PWM. Should either be a float between 0.0 and 1.0 or a filename with thresholds as created by 'gimme threshold'. """ |
if threshold and fpr:
raise ValueError("Need either fpr or threshold.")
if fpr:
fpr = float(fpr)
if not (0.0 < fpr < 1.0):
raise ValueError("Parameter fpr should be between 0 and 1")
if not self.motifs:
raise ValueError("please run set_motifs() first")
thresholds = {}
motifs = read_motifs(self.motifs)
if threshold is not None:
self.threshold = parse_threshold_values(self.motifs, threshold)
return
if not self.background:
try:
self.set_background()
except:
raise ValueError("please run set_background() first")
seqs = self.background.seqs
with Cache(CACHE_DIR) as cache:
scan_motifs = []
for motif in motifs:
k = "{}|{}|{:.4f}".format(motif.hash(), self.background_hash, fpr)
threshold = cache.get(k)
if threshold is None:
scan_motifs.append(motif)
else:
if np.isclose(threshold, motif.pwm_max_score()):
thresholds[motif.id] = None
elif np.isclose(threshold, motif.pwm_min_score()):
thresholds[motif.id] = 0.0
else:
thresholds[motif.id] = threshold
if len(scan_motifs) > 0:
logger.info("Determining FPR-based threshold")
for motif, threshold in self._threshold_from_seqs(scan_motifs, seqs, fpr):
k = "{}|{}|{:.4f}".format(motif.hash(), self.background_hash, fpr)
cache.set(k, threshold)
if np.isclose(threshold, motif.pwm_max_score()):
thresholds[motif.id] = None
elif np.isclose(threshold, motif.pwm_min_score()):
thresholds[motif.id] = 0.0
else:
thresholds[motif.id] = threshold
self.threshold_str = "{}_{}_{}".format(fpr, threshold, self.background_hash)
self.threshold = thresholds |
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def best_score(self, seqs, scan_rc=True, normalize=False):
""" give the score of the best match of each motif in each sequence returns an iterator of lists containing floats """ |
self.set_threshold(threshold=0.0)
if normalize and len(self.meanstd) == 0:
self.set_meanstd()
means = np.array([self.meanstd[m][0] for m in self.motif_ids])
stds = np.array([self.meanstd[m][1] for m in self.motif_ids])
for matches in self.scan(seqs, 1, scan_rc):
scores = np.array([sorted(m, key=lambda x: x[0])[0][0] for m in matches if len(m) > 0])
if normalize:
scores = (scores - means) / stds
yield scores |
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def roc(args):
""" Calculate ROC_AUC and other metrics and optionally plot ROC curve.""" |
outputfile = args.outfile
# Default extension for image
if outputfile and not outputfile.endswith(".png"):
outputfile += ".png"
motifs = read_motifs(args.pwmfile, fmt="pwm")
ids = []
if args.ids:
ids = args.ids.split(",")
else:
ids = [m.id for m in motifs]
motifs = [m for m in motifs if (m.id in ids)]
stats = [
"phyper_at_fpr",
"roc_auc",
"pr_auc",
"enr_at_fpr",
"recall_at_fdr",
"roc_values",
"matches_at_fpr",
]
plot_x = []
plot_y = []
legend = []
f_out = sys.stdout
if args.outdir:
if not os.path.exists(args.outdir):
os.makedirs(args.outdir)
f_out = open(args.outdir + "/gimme.roc.report.txt", "w")
# Print the metrics
f_out.write("Motif\t# matches\t# matches background\tP-value\tlog10 P-value\tROC AUC\tPR AUC\tEnr. at 1% FPR\tRecall at 10% FDR\n")
for motif_stats in calc_stats_iterator(motifs, args.sample, args.background,
genome=args.genome, stats=stats, ncpus=args.ncpus):
for motif in motifs:
if str(motif) in motif_stats:
if outputfile:
x, y = motif_stats[str(motif)]["roc_values"]
plot_x.append(x)
plot_y.append(y)
legend.append(motif.id)
log_pvalue = np.inf
if motif_stats[str(motif)]["phyper_at_fpr"] > 0:
log_pvalue = -np.log10(motif_stats[str(motif)]["phyper_at_fpr"])
f_out.write("{}\t{:d}\t{:d}\t{:.2e}\t{:.3f}\t{:.3f}\t{:.3f}\t{:.2f}\t{:0.4f}\n".format(
motif.id,
motif_stats[str(motif)]["matches_at_fpr"][0],
motif_stats[str(motif)]["matches_at_fpr"][1],
motif_stats[str(motif)]["phyper_at_fpr"],
log_pvalue,
motif_stats[str(motif)]["roc_auc"],
motif_stats[str(motif)]["pr_auc"],
motif_stats[str(motif)]["enr_at_fpr"],
motif_stats[str(motif)]["recall_at_fdr"],
))
f_out.close()
if args.outdir:
html_report(
args.outdir,
args.outdir + "/gimme.roc.report.txt",
args.pwmfile,
0.01,
)
# Plot the ROC curve
if outputfile:
roc_plot(outputfile, plot_x, plot_y, ids=legend) |
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def seqcor(m1, m2, seq=None):
"""Calculates motif similarity based on Pearson correlation of scores. Based on Kielbasa (2015) and Grau (2015). Scores are calculated based on scanning a de Bruijn sequence of 7-mers. This sequence is taken from ShortCAKE (Orenstein & Shamir, 2015). Optionally another sequence can be given as an argument. Parameters m1 : Motif instance Motif 1 to compare. m2 : Motif instance Motif 2 to compare. seq : str, optional Sequence to use for scanning instead of k=7 de Bruijn sequence. Returns ------- score, position, strand """ |
l1 = len(m1)
l2 = len(m2)
l = max(l1, l2)
if seq is None:
seq = RCDB
L = len(seq)
# Scan RC de Bruijn sequence
result1 = pfmscan(seq, m1.pwm, m1.pwm_min_score(), len(seq), False, True)
result2 = pfmscan(seq, m2.pwm, m2.pwm_min_score(), len(seq), False, True)
# Reverse complement of motif 2
result3 = pfmscan(seq, m2.rc().pwm, m2.rc().pwm_min_score(), len(seq), False, True)
result1 = np.array(result1)
result2 = np.array(result2)
result3 = np.array(result3)
# Return maximum correlation
c = []
for i in range(l1 - l1 // 3):
c.append([1 - distance.correlation(result1[:L-l-i],result2[i:L-l]), i, 1])
c.append([1 - distance.correlation(result1[:L-l-i],result3[i:L-l]), i, -1])
for i in range(l2 - l2 // 3):
c.append([1 - distance.correlation(result1[i:L-l],result2[:L-l-i]), -i, 1])
c.append([1 - distance.correlation(result1[i:L-l],result3[:L-l-i]), -i, -1])
return sorted(c, key=lambda x: x[0])[-1] |
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def compare_motifs(self, m1, m2, match="total", metric="wic", combine="mean", pval=False):
"""Compare two motifs. The similarity metric can be any of seqcor, pcc, ed, distance, wic, chisq, akl or ssd. If match is 'total' the similarity score is calculated for the whole match, including positions that are not present in both motifs. If match is partial or subtotal, only the matching psotiions are used to calculate the score. The score of individual position is combined using either the mean or the sum. Note that the match and combine parameters have no effect on the seqcor similarity metric. Parameters m1 : Motif instance Motif instance 1. m2 : Motif instance Motif instance 2. match : str, optional Match can be "partial", "subtotal" or "total". Not all metrics use this. metric : str, optional Distance metric. combine : str, optional Combine positional scores using "mean" or "sum". Not all metrics use this. pval : bool, optional Calculate p-vale of match. Returns ------- score, position, strand """ |
if metric == "seqcor":
return seqcor(m1, m2)
elif match == "partial":
if pval:
return self.pvalue(m1, m2, "total", metric, combine, self.max_partial(m1.pwm, m2.pwm, metric, combine))
elif metric in ["pcc", "ed", "distance", "wic", "chisq", "ssd"]:
return self.max_partial(m1.pwm, m2.pwm, metric, combine)
else:
return self.max_partial(m1.pfm, m2.pfm, metric, combine)
elif match == "total":
if pval:
return self.pvalue(m1, m2, match, metric, combine, self.max_total(m1.pwm, m2.pwm, metric, combine))
elif metric in ["pcc", 'akl']:
# Slightly randomize the weight matrix
return self.max_total(m1.wiggle_pwm(), m2.wiggle_pwm(), metric, combine)
elif metric in ["ed", "distance", "wic", "chisq", "pcc", "ssd"]:
return self.max_total(m1.pwm, m2.pwm, metric, combine)
else:
return self.max_total(m1.pfm, m2.pfm, metric, combine)
elif match == "subtotal":
if metric in ["pcc", "ed", "distance", "wic", "chisq", "ssd"]:
return self.max_subtotal(m1.pwm, m2.pwm, metric, combine)
else:
return self.max_subtotal(m1.pfm, m2.pfm, metric, combine) |
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def get_all_scores(self, motifs, dbmotifs, match, metric, combine, pval=False, parallel=True, trim=None, ncpus=None):
"""Pairwise comparison of a set of motifs compared to reference motifs. Parameters motifs : list List of Motif instances. dbmotifs : list List of Motif instances. match : str Match can be "partial", "subtotal" or "total". Not all metrics use this. metric : str Distance metric. combine : str Combine positional scores using "mean" or "sum". Not all metrics use this. pval : bool , optional Calculate p-vale of match. parallel : bool , optional Use multiprocessing for parallel execution. True by default. trim : float or None If a float value is specified, motifs are trimmed used this IC cutoff before comparison. ncpus : int or None Specifies the number of cores to use for parallel execution. Returns ------- scores : dict Dictionary with scores. """ |
# trim motifs first, if specified
if trim:
for m in motifs:
m.trim(trim)
for m in dbmotifs:
m.trim(trim)
# hash of result scores
scores = {}
if parallel:
# Divide the job into big chunks, to keep parallel overhead to minimum
# Number of chunks = number of processors available
if ncpus is None:
ncpus = int(MotifConfig().get_default_params()["ncpus"])
pool = Pool(processes=ncpus, maxtasksperchild=1000)
batch_len = len(dbmotifs) // ncpus
if batch_len <= 0:
batch_len = 1
jobs = []
for i in range(0, len(dbmotifs), batch_len):
# submit jobs to the job server
p = pool.apply_async(_get_all_scores,
args=(self, motifs, dbmotifs[i: i + batch_len], match, metric, combine, pval))
jobs.append(p)
pool.close()
for job in jobs:
# Get the job result
result = job.get()
# and update the result score
for m1,v in result.items():
for m2, s in v.items():
if m1 not in scores:
scores[m1] = {}
scores[m1][m2] = s
pool.join()
else:
# Do the whole thing at once if we don't want parallel
scores = _get_all_scores(self, motifs, dbmotifs, match, metric, combine, pval)
return scores |
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def get_closest_match(self, motifs, dbmotifs=None, match="partial", metric="wic",combine="mean", parallel=True, ncpus=None):
"""Return best match in database for motifs. Parameters motifs : list or str Filename of motifs or list of motifs. dbmotifs : list or str, optional Database motifs, default will be used if not specified. match : str, optional metric : str, optional combine : str, optional ncpus : int, optional Number of threads to use. Returns ------- closest_match : dict """ |
if dbmotifs is None:
pwm = self.config.get_default_params()["motif_db"]
pwmdir = self.config.get_motif_dir()
dbmotifs = os.path.join(pwmdir, pwm)
motifs = parse_motifs(motifs)
dbmotifs = parse_motifs(dbmotifs)
dbmotif_lookup = dict([(m.id, m) for m in dbmotifs])
scores = self.get_all_scores(motifs, dbmotifs, match, metric, combine, parallel=parallel, ncpus=ncpus)
for motif in scores:
scores[motif] = sorted(
scores[motif].items(),
key=lambda x:x[1][0]
)[-1]
for motif in motifs:
dbmotif, score = scores[motif.id]
pval, pos, orient = self.compare_motifs(
motif, dbmotif_lookup[dbmotif], match, metric, combine, True)
scores[motif.id] = [dbmotif, (list(score) + [pval])]
return scores |
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def list_regions(service):
""" List regions for the service """ |
for region in service.regions():
print '%(name)s: %(endpoint)s' % {
'name': region.name,
'endpoint': region.endpoint,
} |
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Description:
def elb_table(balancers):
""" Print nice looking table of information from list of load balancers """ |
t = prettytable.PrettyTable(['Name', 'DNS', 'Ports', 'Zones', 'Created'])
t.align = 'l'
for b in balancers:
ports = ['%s: %s -> %s' % (l[2], l[0], l[1]) for l in b.listeners]
ports = '\n'.join(ports)
zones = '\n'.join(b.availability_zones)
t.add_row([b.name, b.dns_name, ports, zones, b.created_time])
return t |
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def ec2_table(instances):
""" Print nice looking table of information from list of instances """ |
t = prettytable.PrettyTable(['ID', 'State', 'Monitored', 'Image', 'Name', 'Type', 'SSH key', 'DNS'])
t.align = 'l'
for i in instances:
name = i.tags.get('Name', '')
t.add_row([i.id, i.state, i.monitored, i.image_id, name, i.instance_type, i.key_name, i.dns_name])
return t |
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def ec2_image_table(images):
""" Print nice looking table of information from images """ |
t = prettytable.PrettyTable(['ID', 'State', 'Name', 'Owner', 'Root device', 'Is public', 'Description'])
t.align = 'l'
for i in images:
t.add_row([i.id, i.state, i.name, i.ownerId, i.root_device_type, i.is_public, i.description])
return t |
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def ec2_fab(service, args):
""" Run Fabric commands against EC2 instances """ |
instance_ids = args.instances
instances = service.list(elb=args.elb, instance_ids=instance_ids)
hosts = service.resolve_hosts(instances)
fab.env.hosts = hosts
fab.env.key_filename = settings.get('SSH', 'KEY_FILE')
fab.env.user = settings.get('SSH', 'USER', getpass.getuser())
fab.env.parallel = True
fabfile = find_fabfile(args.file)
if not fabfile:
print 'Couldn\'t find any fabfiles!'
return
fab.env.real_fabile = fabfile
docstring, callables, default = load_fabfile(fabfile)
fab_state.commands.update(callables)
commands_to_run = parse_arguments(args.methods)
for name, args, kwargs, arg_hosts, arg_roles, arg_exclude_hosts in commands_to_run:
fab.execute(name,
hosts=arg_hosts,
roles=arg_roles,
exclude_hosts=arg_exclude_hosts,
*args, **kwargs) |
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| def buffer_stream(stream, buffer_size, partial=False, axis=None):
'''Buffer "data" from an stream into one data object.
Parameters
----------
stream : stream
The stream to buffer
buffer_size : int > 0
The number of examples to retain per batch.
partial : bool, default=False
If True, yield a final partial batch on under-run.
axis : int or None
If `None` (default), concatenate data along a new 0th axis.
Otherwise, concatenation is performed along the specified axis.
This is primarily useful when combining data that already has a
dimension for buffer index, e.g., when buffering buffers.
Yields
------
batch
A batch of size at most `buffer_size`
Raises
------
DataError
If the stream contains items that are not data-like.
'''
data = []
count = 0
for item in stream:
data.append(item)
count += 1
if count < buffer_size:
continue
try:
yield __stack_data(data, axis=axis)
except (TypeError, AttributeError):
raise DataError("Malformed data stream: {}".format(data))
finally:
data = []
count = 0
if data and partial:
yield __stack_data(data, axis=axis) |
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def tuples(stream, *keys):
"""Reformat data as tuples. Parameters stream : iterable Stream of data objects. *keys : strings Keys to use for ordering data. Yields ------ items : tuple of np.ndarrays Data object reformated as a tuple. Raises ------ DataError If the stream contains items that are not data-like. KeyError If a data object does not contain the requested key. """ |
if not keys:
raise PescadorError('Unable to generate tuples from '
'an empty item set')
for data in stream:
try:
yield tuple(data[key] for key in keys)
except TypeError:
raise DataError("Malformed data stream: {}".format(data)) |
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def keras_tuples(stream, inputs=None, outputs=None):
"""Reformat data objects as keras-compatible tuples. For more detail: https://keras.io/models/model/#fit Parameters stream : iterable Stream of data objects. inputs : string or iterable of strings, None Keys to use for ordered input data. If not specified, returns `None` in its place. outputs : string or iterable of strings, default=None Keys to use for ordered output data. If not specified, returns `None` in its place. Yields ------ x : np.ndarray, list of np.ndarray, or None If `inputs` is a string, `x` is a single np.ndarray. If `inputs` is an iterable of strings, `x` is a list of np.ndarrays. If `inputs` is a null type, `x` is None. y : np.ndarray, list of np.ndarray, or None If `outputs` is a string, `y` is a single np.ndarray. If `outputs` is an iterable of strings, `y` is a list of np.ndarrays. If `outputs` is a null type, `y` is None. Raises ------ DataError If the stream contains items that are not data-like. """ |
flatten_inputs, flatten_outputs = False, False
if inputs and isinstance(inputs, six.string_types):
inputs = [inputs]
flatten_inputs = True
if outputs and isinstance(outputs, six.string_types):
outputs = [outputs]
flatten_outputs = True
inputs, outputs = (inputs or []), (outputs or [])
if not inputs + outputs:
raise PescadorError('At least one key must be given for '
'`inputs` or `outputs`')
for data in stream:
try:
x = list(data[key] for key in inputs) or None
if len(inputs) == 1 and flatten_inputs:
x = x[0]
y = list(data[key] for key in outputs) or None
if len(outputs) == 1 and flatten_outputs:
y = y[0]
yield (x, y)
except TypeError:
raise DataError("Malformed data stream: {}".format(data)) |
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def location(args):
""" Creates histrogram of motif location. Parameters args : argparse object Command line arguments. """ |
fastafile = args.fastafile
pwmfile = args.pwmfile
lwidth = args.width
if not lwidth:
f = Fasta(fastafile)
lwidth = len(f.items()[0][1])
f = None
jobs = []
motifs = pwmfile_to_motifs(pwmfile)
ids = [motif.id for motif in motifs]
if args.ids:
ids = args.ids.split(",")
n_cpus = int(MotifConfig().get_default_params()["ncpus"])
pool = Pool(processes=n_cpus, maxtasksperchild=1000)
for motif in motifs:
if motif.id in ids:
outfile = os.path.join("%s_histogram" % motif.id)
jobs.append(
pool.apply_async(
motif_localization,
(fastafile,motif,lwidth,outfile, args.cutoff)
))
for job in jobs:
job.get() |
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def which(fname):
"""Find location of executable.""" |
if "PATH" not in os.environ or not os.environ["PATH"]:
path = os.defpath
else:
path = os.environ["PATH"]
for p in [fname] + [os.path.join(x, fname) for x in path.split(os.pathsep)]:
p = os.path.abspath(p)
if os.access(p, os.X_OK) and not os.path.isdir(p):
return p
p = sp.Popen("locate %s" % fname, shell=True, stdout=sp.PIPE, stderr=sp.PIPE)
(stdout, stderr) = p.communicate()
if not stderr:
for p in stdout.decode().split("\n"):
if (os.path.basename(p) == fname) and (
os.access(p, os.X_OK)) and (
not os.path.isdir(p)):
return p |
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def find_by_ext(dirname, ext):
"""Find all files in a directory by extension.""" |
# Get all fasta-files
try:
files = os.listdir(dirname)
except OSError:
if os.path.exists(dirname):
cmd = "find {0} -maxdepth 1 -name \"*\"".format(dirname)
p = sp.Popen(cmd, shell=True, stdout=sp.PIPE, stderr=sp.PIPE)
stdout, _stderr = p.communicate()
files = [os.path.basename(fname) for fname in stdout.decode().splitlines()]
else:
raise
retfiles = [os.path.join(dirname, fname) for fname in files if
os.path.splitext(fname)[-1] in ext]
return retfiles |
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def default_motifs():
"""Return list of Motif instances from default motif database.""" |
config = MotifConfig()
d = config.get_motif_dir()
m = config.get_default_params()['motif_db']
if not d or not m:
raise ValueError("default motif database not configured")
fname = os.path.join(d, m)
with open(fname) as f:
motifs = read_motifs(f)
return motifs |
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def motif_from_align(align):
"""Convert alignment to motif. Converts a list with sequences to a motif. Sequences should be the same length. Parameters align : list List with sequences (A,C,G,T). Returns ------- m : Motif instance Motif created from the aligned sequences. """ |
width = len(align[0])
nucs = {"A":0,"C":1,"G":2,"T":3}
pfm = [[0 for _ in range(4)] for _ in range(width)]
for row in align:
for i in range(len(row)):
pfm[i][nucs[row[i]]] += 1
m = Motif(pfm)
m.align = align[:]
return m |
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def motif_from_consensus(cons, n=12):
"""Convert consensus sequence to motif. Converts a consensus sequences using the nucleotide IUPAC alphabet to a motif. Parameters cons : str Consensus sequence using the IUPAC alphabet. n : int , optional Count used to convert the sequence to a PFM. Returns ------- m : Motif instance Motif created from the consensus. """ |
width = len(cons)
nucs = {"A":0,"C":1,"G":2,"T":3}
pfm = [[0 for _ in range(4)] for _ in range(width)]
m = Motif()
for i,char in enumerate(cons):
for nuc in m.iupac[char.upper()]:
pfm[i][nucs[nuc]] = n / len(m.iupac[char.upper()])
m = Motif(pfm)
m.id = cons
return m |
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def parse_motifs(motifs):
"""Parse motifs in a variety of formats to return a list of motifs. Parameters motifs : list or str Filename of motif, list of motifs or single Motif instance. Returns ------- motifs : list List of Motif instances. """ |
if isinstance(motifs, six.string_types):
with open(motifs) as f:
if motifs.endswith("pwm") or motifs.endswith("pfm"):
motifs = read_motifs(f, fmt="pwm")
elif motifs.endswith("transfac"):
motifs = read_motifs(f, fmt="transfac")
else:
motifs = read_motifs(f)
elif isinstance(motifs, Motif):
motifs = [motifs]
else:
if not isinstance(list(motifs)[0], Motif):
raise ValueError("Not a list of motifs")
return list(motifs) |
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def read_motifs(infile=None, fmt="pwm", as_dict=False):
""" Read motifs from a file or stream or file-like object. Parameters infile : string or file-like object, optional Motif database, filename of motif file or file-like object. If infile is not specified the default motifs as specified in the config file will be returned. fmt : string, optional Motif format, can be 'pwm', 'transfac', 'xxmotif', 'jaspar' or 'align'. as_dict : boolean, optional Return motifs as a dictionary with motif_id, motif pairs. Returns ------- motifs : list List of Motif instances. If as_dict is set to True, motifs is a dictionary. """ |
if infile is None or isinstance(infile, six.string_types):
infile = pwmfile_location(infile)
with open(infile) as f:
motifs = _read_motifs_from_filehandle(f, fmt)
else:
motifs = _read_motifs_from_filehandle(infile, fmt)
if as_dict:
motifs = {m.id:m for m in motifs}
return motifs |
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def information_content(self):
"""Return the total information content of the motif. Return ------ ic : float Motif information content. """ |
ic = 0
for row in self.pwm:
ic += 2.0 + np.sum([row[x] * log(row[x])/log(2) for x in range(4) if row[x] > 0])
return ic |
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def pwm_min_score(self):
"""Return the minimum PWM score. Returns ------- score : float Minimum PWM score. """ |
if self.min_score is None:
score = 0
for row in self.pwm:
score += log(min(row) / 0.25 + 0.01)
self.min_score = score
return self.min_score |
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def pwm_max_score(self):
"""Return the maximum PWM score. Returns ------- score : float Maximum PWM score. """ |
if self.max_score is None:
score = 0
for row in self.pwm:
score += log(max(row) / 0.25 + 0.01)
self.max_score = score
return self.max_score |
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def score_kmer(self, kmer):
"""Calculate the log-odds score for a specific k-mer. Parameters kmer : str String representing a kmer. Should be the same length as the motif. Returns ------- score : float Log-odd score. """ |
if len(kmer) != len(self.pwm):
raise Exception("incorrect k-mer length")
score = 0.0
d = {"A":0, "C":1, "G":2, "T":3}
for nuc, row in zip(kmer.upper(), self.pwm):
score += log(row[d[nuc]] / 0.25 + 0.01)
return score |
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def pfm_to_pwm(self, pfm, pseudo=0.001):
"""Convert PFM with counts to a PFM with fractions. Parameters pfm : list 2-dimensional list with counts. pseudo : float Pseudocount used in conversion. Returns ------- pwm : list 2-dimensional list with fractions. """ |
return [[(x + pseudo)/(float(np.sum(row)) + pseudo * 4) for x in row] for row in pfm] |
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def ic_pos(self, row1, row2=None):
"""Calculate the information content of one position. Returns ------- score : float Information content. """ |
if row2 is None:
row2 = [0.25,0.25,0.25,0.25]
score = 0
for a,b in zip(row1, row2):
if a > 0:
score += a * log(a / b) / log(2)
return score |
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def pcc_pos(self, row1, row2):
"""Calculate the Pearson correlation coefficient of one position compared to another position. Returns ------- score : float Pearson correlation coefficient. """ |
mean1 = np.mean(row1)
mean2 = np.mean(row2)
a = 0
x = 0
y = 0
for n1, n2 in zip(row1, row2):
a += (n1 - mean1) * (n2 - mean2)
x += (n1 - mean1) ** 2
y += (n2 - mean2) ** 2
if a == 0:
return 0
else:
return a / sqrt(x * y) |
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def rc(self):
"""Return the reverse complemented motif. Returns ------- m : Motif instance New Motif instance with the reverse complement of the input motif. """ |
m = Motif()
m.pfm = [row[::-1] for row in self.pfm[::-1]]
m.pwm = [row[::-1] for row in self.pwm[::-1]]
m.id = self.id + "_revcomp"
return m |
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def trim(self, edge_ic_cutoff=0.4):
"""Trim positions with an information content lower than the threshold. The default threshold is set to 0.4. The Motif will be changed in-place. Parameters edge_ic_cutoff : float, optional Information content threshold. All motif positions at the flanks with an information content lower thab this will be removed. Returns ------- m : Motif instance """ |
pwm = self.pwm[:]
while len(pwm) > 0 and self.ic_pos(pwm[0]) < edge_ic_cutoff:
pwm = pwm[1:]
self.pwm = self.pwm[1:]
self.pfm = self.pfm[1:]
while len(pwm) > 0 and self.ic_pos(pwm[-1]) < edge_ic_cutoff:
pwm = pwm[:-1]
self.pwm = self.pwm[:-1]
self.pfm = self.pfm[:-1]
self.consensus = None
self.min_score = None
self.max_score = None
self.wiggled_pwm = None
return self |
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def consensus_scan(self, fa):
"""Scan FASTA with the motif as a consensus sequence. Parameters fa : Fasta object Fasta object to scan Returns ------- matches : dict Dictionaru with matches. """ |
regexp = "".join(["[" + "".join(self.iupac[x.upper()]) + "]" for x in self.to_consensusv2()])
p = re.compile(regexp)
matches = {}
for name,seq in fa.items():
matches[name] = []
for match in p.finditer(seq):
middle = (match.span()[1] + match.span()[0]) / 2
matches[name].append(middle)
return matches |
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def pwm_scan_to_gff(self, fa, gfffile, cutoff=0.9, nreport=50, scan_rc=True, append=False):
"""Scan sequences with this motif and save to a GFF file. Scan sequences from a FASTA object with this motif. Less efficient than using a Scanner object. By setting the cutoff to 0.0 and nreport to 1, the best match for every sequence will be returned. The output is save to a file in GFF format. Parameters fa : Fasta object Fasta object to scan. gfffile : str Filename of GFF output file. cutoff : float , optional Cutoff to use for motif scanning. This cutoff is not specifically optimized and the strictness will vary a lot with motif lengh. nreport : int , optional Maximum number of matches to report. scan_rc : bool , optional Scan the reverse complement. True by default. append : bool , optional Append to GFF file instead of overwriting it. False by default. """ |
if append:
out = open(gfffile, "a")
else:
out = open(gfffile, "w")
c = self.pwm_min_score() + (self.pwm_max_score() - self.pwm_min_score()) * cutoff
pwm = self.pwm
strandmap = {-1:"-","-1":"-","-":"-","1":"+",1:"+","+":"+"}
gff_line = ("{}\tpfmscan\tmisc_feature\t{}\t{}\t{:.3f}\t{}\t.\t"
"motif_name \"{}\" ; motif_instance \"{}\"\n")
for name, seq in fa.items():
result = pfmscan(seq.upper(), pwm, c, nreport, scan_rc)
for score, pos, strand in result:
out.write(gff_line.format(
name,
pos,
pos + len(pwm),
score,
strandmap[strand],
self.id,
seq[pos:pos + len(pwm)]
))
out.close() |
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Description:
def average_motifs(self, other, pos, orientation, include_bg=False):
"""Return the average of two motifs. Combine this motif with another motif and return the average as a new Motif object. The position and orientatien need to be supplied. The pos parameter is the position of the second motif relative to this motif. For example, take the following two motifs: Motif 1: CATGYT Motif 2: GGCTTGY With position -2, the motifs are averaged as follows: xxCATGYT GGCTTGYx Parameters other : Motif object Other Motif object. pos : int Position of the second motif relative to this motif. orientation : int Orientation, should be 1 or -1. If the orientation is -1 then the reverse complement of the other motif is used for averaging. include_bg : bool , optional Extend both motifs with background frequencies (0.25) before averaging. False by default. Returns ------- motif : motif object New Motif object containing average motif. """ |
# xxCATGYT
# GGCTTGYx
# pos = -2
pfm1 = self.pfm[:]
pfm2 = other.pfm[:]
if orientation < 0:
pfm2 = [row[::-1] for row in pfm2[::-1]]
pfm1_count = float(np.sum(pfm1[0]))
pfm2_count = float(np.sum(pfm2[0]))
if include_bg:
if len(pfm1) > len(pfm2) + pos:
pfm2 += [[pfm2_count / 4.0 for x in range(4)] for i in range(-(len(pfm1) - len(pfm2) - pos), 0)]
elif len(pfm2) + pos > len(pfm1):
pfm1 += [[pfm1_count / 4.0 for x in range(4)] for i in range(-(len(pfm2) - len(pfm1) + pos), 0)]
if pos < 0:
pfm1 = [[pfm1_count / 4.0 for x in range(4)] for i in range(-pos)] + pfm1
elif pos > 0:
pfm2 = [[pfm2_count / 4.0 for x in range(4)] for i in range(pos)] + pfm2
else:
if len(pfm1) > len(pfm2) + pos:
pfm2 += [[pfm1[i][x] / pfm1_count * (pfm2_count) for x in range(4)] for i in range(-(len(pfm1) - len(pfm2) - pos), 0)]
elif len(pfm2) + pos > len(pfm1):
pfm1 += [[pfm2[i][x] / pfm2_count * (pfm1_count) for x in range(4)] for i in range(-(len(pfm2) - len(pfm1) + pos), 0)]
if pos < 0:
pfm1 = [[pfm2[i][x] / pfm2_count * (pfm1_count) for x in range(4)] for i in range(-pos)] + pfm1
elif pos > 0:
pfm2 = [[pfm1[i][x] / pfm1_count * (pfm2_count) for x in range(4)] for i in range(pos)] + pfm2
pfm = [[a + b for a,b in zip(x,y)] for x,y in zip(pfm1, pfm2)]
m = Motif(pfm)
m.id = m.to_consensus()
return m |
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Description:
def _pwm_to_str(self, precision=4):
"""Return string representation of pwm. Parameters precision : int, optional, default 4 Floating-point precision. Returns ------- pwm_string : str """ |
if not self.pwm:
return ""
fmt = "{{:.{:d}f}}".format(precision)
return "\n".join(
["\t".join([fmt.format(p) for p in row])
for row in self.pwm]
) |
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def to_pwm(self, precision=4, extra_str=""):
"""Return pwm as string. Parameters precision : int, optional, default 4 Floating-point precision. extra_str |: str, optional Extra text to include with motif id line. Returns ------- motif_str : str Motif formatted in PWM format. """ |
motif_id = self.id
if extra_str:
motif_id += "_%s" % extra_str
if not self.pwm:
self.pwm = [self.iupac_pwm[char]for char in self.consensus.upper()]
return ">%s\n%s" % (
motif_id,
self._pwm_to_str(precision)
) |
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def to_img(self, fname, fmt="PNG", add_left=0, seqlogo=None, height=6):
"""Create a sequence logo using seqlogo. Create a sequence logo and save it to a file. Valid formats are: PNG, EPS, GIF and PDF. Parameters fname : str Output filename. fmt : str , optional Output format (case-insensitive). Valid formats are PNG, EPS, GIF and PDF. add_left : int , optional Pad motif with empty positions on the left side. seqlogo : str Location of the seqlogo executable. By default the seqlogo version that is included with GimmeMotifs is used. height : float Height of the image """ |
if not seqlogo:
seqlogo = self.seqlogo
if not seqlogo:
raise ValueError("seqlogo not specified or configured")
#TODO: split to_align function
VALID_FORMATS = ["EPS", "GIF", "PDF", "PNG"]
N = 1000
fmt = fmt.upper()
if not fmt in VALID_FORMATS:
sys.stderr.write("Invalid motif format\n")
return
if fname[-4:].upper() == (".%s" % fmt):
fname = fname[:-4]
seqs = []
if add_left == 0:
seqs = ["" for i in range(N)]
else:
for nuc in ["A", "C", "T", "G"]:
seqs += [nuc * add_left for i in range(N // 4)]
for pos in range(len(self.pwm)):
vals = [self.pwm[pos][0] * N]
for i in range(1,4):
vals.append(vals[i-1] + self.pwm[pos][i] * N)
if vals[3] - N != 0:
#print "Motif weights don't add up to 1! Error of %s%%" % ((vals[3] - n)/ n * 100)
vals[3] = N
for i in range(N):
if i <= vals[0]:
seqs[i] += "A"
elif i <= vals[1]:
seqs[i] += "C"
elif i <= vals[2]:
seqs[i] += "G"
elif i <= vals[3]:
seqs[i] += "T"
f = NamedTemporaryFile(mode="w", dir=mytmpdir())
for seq in seqs:
f.write("%s\n" % seq)
f.flush()
makelogo = "{0} -f {1} -F {2} -c -a -h {3} -w {4} -o {5} -b -n -Y"
cmd = makelogo.format(
seqlogo,
f.name,
fmt,
height,
len(self) + add_left,
fname)
sp.call(cmd, shell=True) |
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def randomize(self):
"""Create a new motif with shuffled positions. Shuffle the positions of this motif and return a new Motif instance. Returns ------- m : Motif instance Motif instance with shuffled positions. """ |
random_pfm = [[c for c in row] for row in self.pfm]
random.shuffle(random_pfm)
m = Motif(pfm=random_pfm)
m.id = "random"
return m |
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Description:
def maelstrom(args):
"""Run the maelstrom method.""" |
infile = args.inputfile
genome = args.genome
outdir = args.outdir
pwmfile = args.pwmfile
methods = args.methods
ncpus = args.ncpus
if not os.path.exists(infile):
raise ValueError("file {} does not exist".format(infile))
if methods:
methods = [x.strip() for x in methods.split(",")]
run_maelstrom(infile, genome, outdir, pwmfile, methods=methods, ncpus=ncpus) |
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def zmq_recv_data(socket, flags=0, copy=True, track=False):
"""Receive data over a socket.""" |
data = dict()
msg = socket.recv_multipart(flags=flags, copy=copy, track=track)
headers = json.loads(msg[0].decode('ascii'))
if len(headers) == 0:
raise StopIteration
for header, payload in zip(headers, msg[1:]):
data[header['key']] = np.frombuffer(buffer(payload),
dtype=header['dtype'])
data[header['key']].shape = header['shape']
if six.PY2:
# Legacy python won't let us preserve alignment, skip this step
continue
data[header['key']].flags['ALIGNED'] = header['aligned']
return data |
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Description:
def hardmask(self):
""" Mask all lowercase nucleotides with N's """ |
p = re.compile("a|c|g|t|n")
for seq_id in self.fasta_dict.keys():
self.fasta_dict[seq_id] = p.sub("N", self.fasta_dict[seq_id])
return self |
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def get_random(self, n, l=None):
""" Return n random sequences from this Fasta object """ |
random_f = Fasta()
if l:
ids = self.ids[:]
random.shuffle(ids)
i = 0
while (i < n) and (len(ids) > 0):
seq_id = ids.pop()
if (len(self[seq_id]) >= l):
start = random.randint(0, len(self[seq_id]) - l)
random_f["random%s" % (i + 1)] = self[seq_id][start:start+l]
i += 1
if len(random_f) != n:
sys.stderr.write("Not enough sequences of required length")
return
else:
return random_f
else:
choice = random.sample(self.ids, n)
for i in range(n):
random_f[choice[i]] = self[choice[i]]
return random_f |
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def writefasta(self, fname):
""" Write sequences to FASTA formatted file""" |
f = open(fname, "w")
fa_str = "\n".join([">%s\n%s" % (id, self._format_seq(seq)) for id, seq in self.items()])
f.write(fa_str)
f.close() |
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| def batch_length(batch):
'''Determine the number of samples in a batch.
Parameters
----------
batch : dict
A batch dictionary. Each value must implement `len`.
All values must have the same `len`.
Returns
-------
n : int >= 0 or None
The number of samples in this batch.
If the batch has no fields, n is None.
Raises
------
PescadorError
If some two values have unequal length
'''
n = None
for value in six.itervalues(batch):
if n is None:
n = len(value)
elif len(value) != n:
raise PescadorError('Unequal field lengths')
return n |
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Description:
def _activate(self):
"""Activates a number of streams""" |
self.distribution_ = 1. / self.n_streams * np.ones(self.n_streams)
self.valid_streams_ = np.ones(self.n_streams, dtype=bool)
self.streams_ = [None] * self.k
self.stream_weights_ = np.zeros(self.k)
self.stream_counts_ = np.zeros(self.k, dtype=int)
# Array of pointers into `self.streamers`
self.stream_idxs_ = np.zeros(self.k, dtype=int)
for idx in range(self.k):
if not (self.distribution_ > 0).any():
break
self.stream_idxs_[idx] = self.rng.choice(
self.n_streams, p=self.distribution_)
self.streams_[idx], self.stream_weights_[idx] = (
self._new_stream(self.stream_idxs_[idx]))
self.weight_norm_ = np.sum(self.stream_weights_) |
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Description:
def iterate(self, max_iter=None):
"""Yields items from the mux, and handles stream exhaustion and replacement. """ |
if max_iter is None:
max_iter = np.inf
# Calls Streamer's __enter__, which calls activate()
with self as active_mux:
# Main sampling loop
n = 0
while n < max_iter and active_mux._streamers_available():
# Pick a stream from the active set
idx = active_mux._next_sample_index()
# Can we sample from it?
try:
# Then yield the sample
yield six.advance_iterator(active_mux.streams_[idx])
# Increment the sample counter
n += 1
active_mux.stream_counts_[idx] += 1
except StopIteration:
# Oops, this stream is exhausted.
# Call child-class exhausted-stream behavior
active_mux._on_stream_exhausted(idx)
# Setup a new stream for this index
active_mux._replace_stream(idx) |
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Description:
def _next_sample_index(self):
"""StochasticMux chooses its next sample stream randomly""" |
return self.rng.choice(self.n_active,
p=(self.stream_weights_ /
self.weight_norm_)) |
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def _activate(self):
"""ShuffledMux's activate is similar to StochasticMux, but there is no 'n_active', since all the streams are always available. """ |
self.streams_ = [None] * self.n_streams
# Weights of the active streams.
# Once a stream is exhausted, it is set to 0.
# Upon activation, this is just a copy of self.weights.
self.stream_weights_ = np.array(self.weights, dtype=float)
# How many samples have been drawn from each (active) stream.
self.stream_counts_ = np.zeros(self.n_streams, dtype=int)
# Initialize each active stream.
for idx in range(self.n_streams):
# Setup a new streamer at this index.
self._new_stream(idx)
self.weight_norm_ = np.sum(self.stream_weights_) |
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def _next_sample_index(self):
"""ShuffledMux chooses its next sample stream randomly, conditioned on the stream weights. """ |
return self.rng.choice(self.n_streams,
p=(self.stream_weights_ /
self.weight_norm_)) |
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def _next_sample_index(self):
"""Rotates through each active sampler by incrementing the index""" |
# Return the next streamer index where the streamer is not None,
# wrapping around.
idx = self.active_index_
self.active_index_ += 1
if self.active_index_ >= len(self.streams_):
self.active_index_ = 0
# Continue to increment if this streamer is exhausted (None)
# This should never be infinite looping;
# the `_streamers_available` check happens immediately
# before this, so there should always be at least one not-None
# streamer.
while self.streams_[idx] is None:
idx = self.active_index_
self.active_index_ += 1
if self.active_index_ >= len(self.streams_):
self.active_index_ = 0
return idx |
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def _new_stream(self, idx):
"""Activate a new stream, given the index into the stream pool. BaseMux's _new_stream simply chooses a new stream and activates it. For special behavior (ie Weighted streams), you must override this in a child class. Parameters idx : int, [0:n_streams - 1] The stream index to replace """ |
# Get the stream index from the candidate pool
stream_index = self.stream_idxs_[idx]
# Activate the Streamer, and get the weights
self.streams_[idx] = self.streamers[stream_index].iterate()
# Reset the sample count to zero
self.stream_counts_[idx] = 0 |
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| def _new_stream(self):
'''Grab the next stream from the input streamers, and start it.
Raises
------
StopIteration
When the input list or generator of streamers is complete,
will raise a StopIteration. If `mode == cycle`, it
will instead restart iterating from the beginning of the sequence.
'''
try:
# Advance the stream_generator_ to get the next available stream.
# If successful, this will make self.chain_streamer_.active True
next_stream = six.advance_iterator(self.stream_generator_)
except StopIteration:
# If running with cycle, restart the chain_streamer_ after
# exhaustion.
if self.mode == "cycle":
self.stream_generator_ = self.chain_streamer_.iterate()
# Try again to get the next stream;
# if it fails this time, just let it raise the StopIteration;
# this means the streams are probably dead or empty.
next_stream = six.advance_iterator(self.stream_generator_)
# If running in exhaustive mode
else:
# self.chain_streamer_ should no longer be active, so
# the outer loop should fall out without running.
next_stream = None
if next_stream is not None:
# Start that stream, and return it.
streamer = next_stream.iterate()
# Activate the Streamer
self.streams_[0] = streamer
# Reset the sample count to zero
self.stream_counts_[0] = 0 |
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def npz_generator(npz_path):
"""Generate data from an npz file.""" |
npz_data = np.load(npz_path)
X = npz_data['X']
# Y is a binary maxtrix with shape=(n, k), each y will have shape=(k,)
y = npz_data['Y']
n = X.shape[0]
while True:
i = np.random.randint(0, n)
yield {'X': X[i], 'Y': y[i]} |
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def phyper(k, good, bad, N):
""" Current hypergeometric implementation in scipy is broken, so here's the correct version """ |
pvalues = [phyper_single(x, good, bad, N) for x in range(k + 1, N + 1)]
return np.sum(pvalues) |
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def calc_motif_enrichment(sample, background, mtc=None, len_sample=None, len_back=None):
"""Calculate enrichment based on hypergeometric distribution""" |
INF = "Inf"
if mtc not in [None, "Bonferroni", "Benjamini-Hochberg", "None"]:
raise RuntimeError("Unknown correction: %s" % mtc)
sig = {}
p_value = {}
n_sample = {}
n_back = {}
if not(len_sample):
len_sample = sample.seqn()
if not(len_back):
len_back = background.seqn()
for motif in sample.motifs.keys():
p = "NA"
s = "NA"
q = len(sample.motifs[motif])
m = 0
if(background.motifs.get(motif)):
m = len(background.motifs[motif])
n = len_back - m
k = len_sample
p = phyper(q - 1, m, n, k)
if p != 0:
s = -(log(p)/log(10))
else:
s = INF
else:
s = INF
p = 0.0
sig[motif] = s
p_value[motif] = p
n_sample[motif] = q
n_back[motif] = m
if mtc == "Bonferroni":
for motif in p_value.keys():
if p_value[motif] != "NA":
p_value[motif] = p_value[motif] * len(p_value.keys())
if p_value[motif] > 1:
p_value[motif] = 1
elif mtc == "Benjamini-Hochberg":
motifs = sorted(p_value.keys(), key=lambda x: -p_value[x])
l = len(p_value)
c = l
for m in motifs:
if p_value[m] != "NA":
p_value[m] = p_value[m] * l / c
c -= 1
return (sig, p_value, n_sample, n_back) |
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def parse_cutoff(motifs, cutoff, default=0.9):
""" Provide either a file with one cutoff per motif or a single cutoff returns a hash with motif id as key and cutoff as value """ |
cutoffs = {}
if os.path.isfile(str(cutoff)):
for i,line in enumerate(open(cutoff)):
if line != "Motif\tScore\tCutoff\n":
try:
motif,_,c = line.strip().split("\t")
c = float(c)
cutoffs[motif] = c
except Exception as e:
sys.stderr.write("Error parsing cutoff file, line {0}: {1}\n".format(e, i + 1))
sys.exit(1)
else:
for motif in motifs:
cutoffs[motif.id] = float(cutoff)
for motif in motifs:
if not motif.id in cutoffs:
sys.stderr.write("No cutoff found for {0}, using default {1}\n".format(motif.id, default))
cutoffs[motif.id] = default
return cutoffs |
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def determine_file_type(fname):
""" Detect file type. The following file types are supported: BED, narrowPeak, FASTA, list of chr:start-end regions If the extension is bed, fa, fasta or narrowPeak, we will believe this without checking! Parameters fname : str File name. Returns ------- filetype : str Filename in lower-case. """ |
if not (isinstance(fname, str) or isinstance(fname, unicode)):
raise ValueError("{} is not a file name!", fname)
if not os.path.isfile(fname):
raise ValueError("{} is not a file!", fname)
ext = os.path.splitext(fname)[1].lower()
if ext in ["bed"]:
return "bed"
elif ext in ["fa", "fasta"]:
return "fasta"
elif ext in ["narrowpeak"]:
return "narrowpeak"
try:
Fasta(fname)
return "fasta"
except:
pass
# Read first line that is not a comment or an UCSC-specific line
p = re.compile(r'^(#|track|browser)')
with open(fname) as f:
for line in f.readlines():
line = line.strip()
if not p.search(line):
break
region_p = re.compile(r'^(.+):(\d+)-(\d+)$')
if region_p.search(line):
return "region"
else:
vals = line.split("\t")
if len(vals) >= 3:
try:
_, _ = int(vals[1]), int(vals[2])
except ValueError:
return "unknown"
if len(vals) == 10:
try:
_, _ = int(vals[4]), int(vals[9])
return "narrowpeak"
except ValueError:
# As far as I know there is no 10-column BED format
return "unknown"
pass
return "bed"
# Catch-all
return "unknown" |
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def file_checksum(fname):
"""Return md5 checksum of file. Note: only works for files < 4GB. Parameters filename : str File used to calculate checksum. Returns ------- checkum : str """ |
size = os.path.getsize(fname)
with open(fname, "r+") as f:
checksum = hashlib.md5(mmap.mmap(f.fileno(), size)).hexdigest()
return checksum |
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def download_annotation(genomebuild, gene_file):
""" Download gene annotation from UCSC based on genomebuild. Will check UCSC, Ensembl and RefSeq annotation. Parameters genomebuild : str UCSC genome name. gene_file : str Output file name. """ |
pred_bin = "genePredToBed"
pred = find_executable(pred_bin)
if not pred:
sys.stderr.write("{} not found in path!\n".format(pred_bin))
sys.exit(1)
tmp = NamedTemporaryFile(delete=False, suffix=".gz")
anno = []
f = urlopen(UCSC_GENE_URL.format(genomebuild))
p = re.compile(r'\w+.Gene.txt.gz')
for line in f.readlines():
m = p.search(line.decode())
if m:
anno.append(m.group(0))
sys.stderr.write("Retrieving gene annotation for {}\n".format(genomebuild))
url = ""
for a in ANNOS:
if a in anno:
url = UCSC_GENE_URL.format(genomebuild) + a
break
if url:
sys.stderr.write("Using {}\n".format(url))
urlretrieve(
url,
tmp.name
)
with gzip.open(tmp.name) as f:
cols = f.readline().decode(errors='ignore').split("\t")
start_col = 1
for i,col in enumerate(cols):
if col == "+" or col == "-":
start_col = i - 1
break
end_col = start_col + 10
cmd = "zcat {} | cut -f{}-{} | {} /dev/stdin {}"
print(cmd.format(tmp.name, start_col, end_col, pred, gene_file))
sp.call(cmd.format(
tmp.name, start_col, end_col, pred, gene_file),
shell=True)
else:
sys.stderr.write("No annotation found!") |
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def _make_index(self, fasta, index):
""" Index a single, one-sequence fasta-file""" |
out = open(index, "wb")
f = open(fasta)
# Skip first line of fasta-file
line = f.readline()
offset = f.tell()
line = f.readline()
while line:
out.write(pack(self.pack_char, offset))
offset = f.tell()
line = f.readline()
f.close()
out.close() |
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def _read_index_file(self):
"""read the param_file, index_dir should already be set """ |
param_file = os.path.join(self.index_dir, self.param_file)
with open(param_file) as f:
for line in f.readlines():
(name, fasta_file, index_file, line_size, total_size) = line.strip().split("\t")
self.size[name] = int(total_size)
self.fasta_file[name] = fasta_file
self.index_file[name] = index_file
self.line_size[name] = int(line_size) |
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def _read_seq_from_fasta(self, fasta, offset, nr_lines):
""" retrieve a number of lines from a fasta file-object, starting at offset""" |
fasta.seek(offset)
lines = [fasta.readline().strip() for _ in range(nr_lines)]
return "".join(lines) |
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def get_sequence(self, chrom, start, end, strand=None):
""" Retrieve a sequence """ |
# Check if we have an index_dir
if not self.index_dir:
print("Index dir is not defined!")
sys.exit()
# retrieve all information for this specific sequence
fasta_file = self.fasta_file[chrom]
index_file = self.index_file[chrom]
line_size = self.line_size[chrom]
total_size = self.size[chrom]
#print fasta_file, index_file, line_size, total_size
if start > total_size:
raise ValueError(
"Invalid start {0}, greater than sequence length {1} of {2}!".format(start, total_size, chrom))
if start < 0:
raise ValueError("Invalid start, < 0!")
if end > total_size:
raise ValueError(
"Invalid end {0}, greater than sequence length {1} of {2}!".format(end, total_size, chrom))
index = open(index_file, "rb")
fasta = open(fasta_file)
seq = self._read(index, fasta, start, end, line_size)
index.close()
fasta.close()
if strand and strand == "-":
seq = rc(seq)
return seq |
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def get_size(self, chrom=None):
""" Return the sizes of all sequences in the index, or the size of chrom if specified as an optional argument """ |
if len(self.size) == 0:
raise LookupError("no chromosomes in index, is the index correct?")
if chrom:
if chrom in self.size:
return self.size[chrom]
else:
raise KeyError("chromosome {} not in index".format(chrom))
total = 0
for size in self.size.values():
total += size
return total |
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def get_tool(name):
""" Returns an instance of a specific tool. Parameters name : str Name of the tool (case-insensitive). Returns ------- tool : MotifProgram instance """ |
tool = name.lower()
if tool not in __tools__:
raise ValueError("Tool {0} not found!\n".format(name))
t = __tools__[tool]()
if not t.is_installed():
sys.stderr.write("Tool {0} not installed!\n".format(tool))
if not t.is_configured():
sys.stderr.write("Tool {0} not configured!\n".format(tool))
return t |
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def locate_tool(name, verbose=True):
""" Returns the binary of a tool. Parameters name : str Name of the tool (case-insensitive). Returns ------- tool_bin : str Binary of tool. """ |
m = get_tool(name)
tool_bin = which(m.cmd)
if tool_bin:
if verbose:
print("Found {} in {}".format(m.name, tool_bin))
return tool_bin
else:
print("Couldn't find {}".format(m.name)) |
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def bin(self):
""" Get the command used to run the tool. Returns ------- command : str The tool system command. """ |
if self.local_bin:
return self.local_bin
else:
return self.config.bin(self.name) |
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def run(self, fastafile, params=None, tmp=None):
""" Run the tool and predict motifs from a FASTA file. Parameters fastafile : str Name of the FASTA input file. params : dict, optional Optional parameters. For some of the tools required parameters are passed using this dictionary. tmp : str, optional Directory to use for creation of temporary files. Returns ------- motifs : list of Motif instances The predicted motifs. stdout : str Standard out of the tool. stderr : str Standard error of the tool. """ |
if not self.is_configured():
raise ValueError("%s is not configured" % self.name)
if not self.is_installed():
raise ValueError("%s is not installed or not correctly configured" % self.name)
self.tmpdir = mkdtemp(prefix="{0}.".format(self.name), dir=tmp)
fastafile = os.path.abspath(fastafile)
try:
return self._run_program(self.bin(), fastafile, params)
except KeyboardInterrupt:
return ([], "Killed", "Killed") |
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def _run_program(self, bin, fastafile, params=None):
""" Run XXmotif and predict motifs from a FASTA file. Parameters bin : str Command used to run the tool. fastafile : str Name of the FASTA input file. params : dict, optional Optional parameters. For some of the tools required parameters are passed using this dictionary. Returns ------- motifs : list of Motif instances The predicted motifs. stdout : str Standard out of the tool. stderr : str Standard error of the tool. """ |
params = self._parse_params(params)
outfile = os.path.join(
self.tmpdir,
os.path.basename(fastafile.replace(".fa", ".pwm")))
stdout = ""
stderr = ""
cmd = "%s %s %s --localization --batch %s %s" % (
bin,
self.tmpdir,
fastafile,
params["background"],
params["strand"],
)
p = Popen(cmd, shell=True, stdout=PIPE, stderr=PIPE)
out,err = p.communicate()
stdout += out.decode()
stderr += err.decode()
motifs = []
if os.path.exists(outfile):
motifs = read_motifs(outfile, fmt="xxmotif")
for m in motifs:
m.id = "{0}_{1}".format(self.name, m.id)
else:
stdout += "\nMotif file {0} not found!\n".format(outfile)
stderr += "\nMotif file {0} not found!\n".format(outfile)
return motifs, stdout, stderr |
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def _run_program(self, bin, fastafile, params=None):
""" Run Homer and predict motifs from a FASTA file. Parameters bin : str Command used to run the tool. fastafile : str Name of the FASTA input file. params : dict, optional Optional parameters. For some of the tools required parameters are passed using this dictionary. Returns ------- motifs : list of Motif instances The predicted motifs. stdout : str Standard out of the tool. stderr : str Standard error of the tool. """ |
params = self._parse_params(params)
outfile = NamedTemporaryFile(
mode="w",
dir=self.tmpdir,
prefix= "homer_w{}.".format(params["width"])
).name
cmd = "%s denovo -i %s -b %s -len %s -S %s %s -o %s -p 8" % (
bin,
fastafile,
params["background"],
params["width"],
params["number"],
params["strand"],
outfile)
stderr = ""
stdout = "Running command:\n{}\n".format(cmd)
p = Popen(cmd, shell=True, stdout=PIPE, stderr=PIPE, cwd=self.tmpdir)
out,err = p.communicate()
stdout += out.decode()
stderr += err.decode()
motifs = []
if os.path.exists(outfile):
motifs = read_motifs(outfile, fmt="pwm")
for i, m in enumerate(motifs):
m.id = "{}_{}_{}".format(self.name, params["width"], i + 1)
return motifs, stdout, stderr |
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def _run_program(self, bin, fastafile, params=None):
""" Run HMS and predict motifs from a FASTA file. Parameters bin : str Command used to run the tool. fastafile : str Name of the FASTA input file. params : dict, optional Optional parameters. For some of the tools required parameters are passed using this dictionary. Returns ------- motifs : list of Motif instances The predicted motifs. stdout : str Standard out of the tool. stderr : str Standard error of the tool. """ |
params = self._parse_params(params)
default_params = {"width":10}
if params is not None:
default_params.update(params)
fgfile, summitfile, outfile = self._prepare_files(fastafile)
current_path = os.getcwd()
os.chdir(self.tmpdir)
cmd = "{} -i {} -w {} -dna 4 -iteration 50 -chain 20 -seqprop -0.1 -strand 2 -peaklocation {} -t_dof 3 -dep 2".format(
bin,
fgfile,
params['width'],
summitfile)
p = Popen(cmd, shell=True, stdout=PIPE, stderr=PIPE)
stdout,stderr = p.communicate()
os.chdir(current_path)
motifs = []
if os.path.exists(outfile):
with open(outfile) as f:
motifs = self.parse(f)
for i,m in enumerate(motifs):
m.id = "HMS_w{}_{}".format(params['width'], i + 1)
return motifs, stdout, stderr |
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def _run_program(self, bin, fastafile, params=None):
""" Run AMD and predict motifs from a FASTA file. Parameters bin : str Command used to run the tool. fastafile : str Name of the FASTA input file. params : dict, optional Optional parameters. For some of the tools required parameters are passed using this dictionary. Returns ------- motifs : list of Motif instances The predicted motifs. stdout : str Standard out of the tool. stderr : str Standard error of the tool. """ |
params = self._parse_params(params)
fgfile = os.path.join(self.tmpdir, "AMD.in.fa")
outfile = fgfile + ".Matrix"
shutil.copy(fastafile, fgfile)
current_path = os.getcwd()
os.chdir(self.tmpdir)
stdout = ""
stderr = ""
cmd = "%s -F %s -B %s" % (
bin,
fgfile,
params["background"],
)
p = Popen(cmd, shell=True, stdout=PIPE, stderr=PIPE)
out,err = p.communicate()
stdout += out.decode()
stderr += err.decode()
os.chdir(current_path)
motifs = []
if os.path.exists(outfile):
f = open(outfile)
motifs = self.parse(f)
f.close()
return motifs, stdout, stderr |
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def _run_program(self, bin, fastafile, params=None):
""" Run Trawler and predict motifs from a FASTA file. Parameters bin : str Command used to run the tool. fastafile : str Name of the FASTA input file. params : dict, optional Optional parameters. For some of the tools required parameters are passed using this dictionary. Returns ------- motifs : list of Motif instances The predicted motifs. stdout : str Standard out of the tool. stderr : str Standard error of the tool. """ |
params = self._parse_params(params)
tmp = NamedTemporaryFile(mode="w", dir=self.tmpdir, delete=False)
shutil.copy(fastafile, tmp.name)
fastafile = tmp.name
current_path = os.getcwd()
os.chdir(self.dir())
motifs = []
stdout = ""
stderr = ""
for wildcard in [0,1,2]:
cmd = "%s -sample %s -background %s -directory %s -strand %s -wildcard %s" % (
bin,
fastafile,
params["background"],
self.tmpdir,
params["strand"],
wildcard,
)
p = Popen(cmd, shell=True, stdout=PIPE, stderr=PIPE)
out,err = p.communicate()
stdout += out.decode()
stderr += err.decode()
os.chdir(current_path)
pwmfiles = glob.glob("{}/tmp*/result/*pwm".format(self.tmpdir))
if len(pwmfiles) > 0:
out_file = pwmfiles[0]
stdout += "\nOutfile: {}".format(out_file)
my_motifs = []
if os.path.exists(out_file):
my_motifs = read_motifs(out_file, fmt="pwm")
for m in motifs:
m.id = "{}_{}".format(self.name, m.id)
stdout += "\nTrawler: {} motifs".format(len(motifs))
# remove temporary files
if os.path.exists(tmp.name):
os.unlink(tmp.name)
for motif in my_motifs:
motif.id = "{}_{}_{}".format(self.name, wildcard, motif.id)
motifs += my_motifs
else:
stderr += "\nNo outfile found"
return motifs, stdout, stderr |
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def _run_program(self, bin,fastafile, params=None):
""" Run Weeder and predict motifs from a FASTA file. Parameters bin : str Command used to run the tool. fastafile : str Name of the FASTA input file. params : dict, optional Optional parameters. For some of the tools required parameters are passed using this dictionary. Returns ------- motifs : list of Motif instances The predicted motifs. stdout : str Standard out of the tool. stderr : str Standard error of the tool. """ |
params = self._parse_params(params)
organism = params["organism"]
weeder_organisms = {
"hg18":"HS",
"hg19":"HS",
"hg38":"HS",
"mm9":"MM",
"mm10":"MM",
"dm3":"DM",
"dm5":"DM",
"dm6":"DM",
"yeast":"SC",
"sacCer2":"SC",
"sacCer3":"SC",
"TAIR10":"AT",
"TAIR11":"AT",
}
weeder_organism = weeder_organisms.get(organism, "HS")
tmp = NamedTemporaryFile(dir=self.tmpdir)
name = tmp.name
tmp.close()
shutil.copy(fastafile, name)
fastafile = name
cmd = "{} -f {} -O".format(
self.cmd,
fastafile,
weeder_organism,
)
if params["single"]:
cmd += " -ss"
#print cmd
stdout, stderr = "", ""
p = Popen(cmd, shell=True, stdout=PIPE, stderr=PIPE, cwd=self.tmpdir)
out,err = p.communicate()
stdout += out.decode()
stderr += err.decode()
motifs = []
if os.path.exists(fastafile + ".matrix.w2"):
f = open(fastafile + ".matrix.w2")
motifs = self.parse(f)
f.close()
for m in motifs:
m.id = "{}_{}".format(self.name, m.id.split("\t")[0])
for ext in [".w2", ".matrix.w2" ]:
if os.path.exists(fastafile + ext):
os.unlink(fastafile + ext)
return motifs, stdout, stderr |
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def _run_program(self, bin, fastafile, params=None):
""" Run MotifSampler and predict motifs from a FASTA file. Parameters bin : str Command used to run the tool. fastafile : str Name of the FASTA input file. params : dict, optional Optional parameters. For some of the tools required parameters are passed using this dictionary. Returns ------- motifs : list of Motif instances The predicted motifs. stdout : str Standard out of the tool. stderr : str Standard error of the tool. """ |
params = self._parse_params(params)
# TODO: test organism
#cmd = "%s -f %s -b %s -m %s -w %s -n %s -o %s -s %s > /dev/null 2>&1" % (
cmd = "%s -f %s -b %s -m %s -w %s -n %s -o %s -s %s" % (
bin,
fastafile,
params["background_model"],
params["pwmfile"],
params["width"],
params["number"],
params["outfile"],
params["strand"],
)
#print cmd
p = Popen(cmd, shell=True, stdout=PIPE, stderr=PIPE)
stdout, stderr = p.communicate()
#stdout,stderr = "",""
#p = Popen(cmd, shell=True)
#p.wait()
motifs = []
if os.path.exists(params["outfile"]):
with open(params["outfile"]) as f:
motifs = self.parse_out(f)
for motif in motifs:
motif.id = "%s_%s" % (self.name, motif.id)
return motifs, stdout, stderr |
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def _run_program(self, bin, fastafile, params=None):
""" Run MDmodule and predict motifs from a FASTA file. Parameters bin : str Command used to run the tool. fastafile : str Name of the FASTA input file. params : dict, optional Optional parameters. For some of the tools required parameters are passed using this dictionary. Returns ------- motifs : list of Motif instances The predicted motifs. stdout : str Standard out of the tool. stderr : str Standard error of the tool. """ |
default_params = {"width":10, "number":10}
if params is not None:
default_params.update(params)
new_file = os.path.join(self.tmpdir, "mdmodule_in.fa")
shutil.copy(fastafile, new_file)
fastafile = new_file
pwmfile = fastafile + ".out"
width = default_params['width']
number = default_params['number']
current_path = os.getcwd()
os.chdir(self.tmpdir)
cmd = "%s -i %s -a 1 -o %s -w %s -t 100 -r %s" % (bin, fastafile, pwmfile, width, number)
p = Popen(cmd, shell=True, stdout=PIPE, stderr=PIPE)
stdout,stderr = p.communicate()
stdout = "cmd: {}\n".format(cmd) + stdout.decode()
motifs = []
if os.path.exists(pwmfile):
with open(pwmfile) as f:
motifs = self.parse(f)
os.chdir(current_path)
for motif in motifs:
motif.id = "%s_%s" % (self.name, motif.id)
return motifs, stdout, stderr |
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def _run_program(self, bin, fastafile, params=None):
""" Run ChIPMunk and predict motifs from a FASTA file. Parameters bin : str Command used to run the tool. fastafile : str Name of the FASTA input file. params : dict, optional Optional parameters. For some of the tools required parameters are passed using this dictionary. Returns ------- motifs : list of Motif instances The predicted motifs. stdout : str Standard out of the tool. stderr : str Standard error of the tool. """ |
params = self._parse_params(params)
basename = "munk_in.fa"
new_file = os.path.join(self.tmpdir, basename)
out = open(new_file, "w")
f = Fasta(fastafile)
for seq in f.seqs:
header = len(seq) // 2
out.write(">%s\n" % header)
out.write("%s\n" % seq)
out.close()
fastafile = new_file
outfile = fastafile + ".out"
current_path = os.getcwd()
os.chdir(self.dir())
motifs = []
# Max recommended by ChIPMunk userguide
ncpus = 4
stdout = ""
stderr = ""
for zoops_factor in ["oops", 0.0, 0.5, 1.0]:
cmd = "{} {} {} y {} m:{} 100 10 1 {} 1>{}".format(
bin,
params.get("width", 8),
params.get("width", 20),
zoops_factor,
fastafile,
ncpus,
outfile
)
#print("command: ", cmd)
p = Popen(cmd, shell=True, stdout=PIPE, stderr=PIPE)
std = p.communicate()
stdout = stdout + std[0].decode()
stderr = stderr + std[1].decode()
if "RuntimeException" in stderr:
return [], stdout, stderr
if os.path.exists(outfile):
with open(outfile) as f:
motifs += self.parse(f)
os.chdir(current_path)
return motifs, stdout, stderr |
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def _run_program(self, bin, fastafile, params=None):
""" Run Posmo and predict motifs from a FASTA file. Parameters bin : str Command used to run the tool. fastafile : str Name of the FASTA input file. params : dict, optional Optional parameters. For some of the tools required parameters are passed using this dictionary. Returns ------- motifs : list of Motif instances The predicted motifs. stdout : str Standard out of the tool. stderr : str Standard error of the tool. """ |
default_params = {}
if params is not None:
default_params.update(params)
width = params.get("width", 8)
basename = "posmo_in.fa"
new_file = os.path.join(self.tmpdir, basename)
shutil.copy(fastafile, new_file)
fastafile = new_file
#pwmfile = fastafile + ".pwm"
motifs = []
current_path = os.getcwd()
os.chdir(self.tmpdir)
for n_ones in range(4, min(width, 11), 2):
x = "1" * n_ones
outfile = "%s.%s.out" % (fastafile, x)
cmd = "%s 5000 %s %s 1.6 2.5 %s 200" % (bin, x, fastafile, width)
p = Popen(cmd, shell=True, stdout=PIPE, stderr=PIPE)
stdout, stderr = p.communicate()
stdout = stdout.decode()
stderr = stderr.decode()
context_file = fastafile.replace(basename, "context.%s.%s.txt" % (basename, x))
cmd = "%s %s %s simi.txt 0.88 10 2 10" % (bin.replace("posmo","clusterwd"), context_file, outfile)
p = Popen(cmd, shell=True, stdout=PIPE, stderr=PIPE)
out, err = p.communicate()
stdout += out.decode()
stderr += err.decode()
if os.path.exists(outfile):
with open(outfile) as f:
motifs += self.parse(f, width, n_ones)
os.chdir(current_path)
return motifs, stdout, stderr |
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def _run_program(self, bin, fastafile, params=None):
""" Get enriched JASPAR motifs in a FASTA file. Parameters bin : str Command used to run the tool. fastafile : str Name of the FASTA input file. params : dict, optional Optional parameters. For some of the tools required parameters are passed using this dictionary. Returns ------- motifs : list of Motif instances The predicted motifs. stdout : str Standard out of the tool. stderr : str Standard error of the tool. """ |
fname = os.path.join(self.config.get_motif_dir(), "JASPAR2010_vertebrate.pwm")
motifs = read_motifs(fname, fmt="pwm")
for motif in motifs:
motif.id = "JASPAR_%s" % motif.id
return motifs, "", "" |
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def _run_program(self, bin, fastafile, params=None):
""" Run MEME and predict motifs from a FASTA file. Parameters bin : str Command used to run the tool. fastafile : str Name of the FASTA input file. params : dict, optional Optional parameters. For some of the tools required parameters are passed using this dictionary. Returns ------- motifs : list of Motif instances The predicted motifs. stdout : str Standard out of the tool. stderr : str Standard error of the tool. """ |
default_params = {"width":10, "single":False, "number":10}
if params is not None:
default_params.update(params)
tmp = NamedTemporaryFile(dir=self.tmpdir)
tmpname = tmp.name
strand = "-revcomp"
width = default_params["width"]
number = default_params["number"]
cmd = [bin, fastafile, "-text","-dna","-nostatus","-mod", "zoops","-nmotifs", "%s" % number, "-w","%s" % width, "-maxsize", "10000000"]
if not default_params["single"]:
cmd.append(strand)
#sys.stderr.write(" ".join(cmd) + "\n")
p = Popen(cmd, bufsize=1, stderr=PIPE, stdout=PIPE)
stdout,stderr = p.communicate()
motifs = []
motifs = self.parse(io.StringIO(stdout.decode()))
# Delete temporary files
tmp.close()
return motifs, stdout, stderr |
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Description:
def scan_to_table(input_table, genome, scoring, pwmfile=None, ncpus=None):
"""Scan regions in input table with motifs. Parameters input_table : str Filename of input table. Can be either a text-separated tab file or a feather file. genome : str Genome name. Can be either the name of a FASTA-formatted file or a genomepy genome name. scoring : str "count" or "score" pwmfile : str, optional Specify a PFM file for scanning. ncpus : int, optional If defined this specifies the number of cores to use. Returns ------- table : pandas.DataFrame DataFrame with motif ids as column names and regions as index. Values are either counts or scores depending on the 'scoring' parameter.s """ |
config = MotifConfig()
if pwmfile is None:
pwmfile = config.get_default_params().get("motif_db", None)
if pwmfile is not None:
pwmfile = os.path.join(config.get_motif_dir(), pwmfile)
if pwmfile is None:
raise ValueError("no pwmfile given and no default database specified")
logger.info("reading table")
if input_table.endswith("feather"):
df = pd.read_feather(input_table)
idx = df.iloc[:,0].values
else:
df = pd.read_table(input_table, index_col=0, comment="#")
idx = df.index
regions = list(idx)
s = Scanner(ncpus=ncpus)
s.set_motifs(pwmfile)
s.set_genome(genome)
s.set_background(genome=genome)
nregions = len(regions)
scores = []
if scoring == "count":
logger.info("setting threshold")
s.set_threshold(fpr=FPR)
logger.info("creating count table")
for row in s.count(regions):
scores.append(row)
logger.info("done")
else:
s.set_threshold(threshold=0.0)
logger.info("creating score table")
for row in s.best_score(regions, normalize=True):
scores.append(row)
logger.info("done")
motif_names = [m.id for m in read_motifs(pwmfile)]
logger.info("creating dataframe")
return pd.DataFrame(scores, index=idx, columns=motif_names) |
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Solve the following problem using Python, implementing the functions described below, one line at a time
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<USER_TASK:>
Description:
def get_args(parser):
""" Converts arguments extracted from a parser to a dict, and will dismiss arguments which default to NOT_SET. :param parser: an ``argparse.ArgumentParser`` instance. :type parser: argparse.ArgumentParser :return: Dictionary with the configs found in the parsed CLI arguments. :rtype: dict """ |
args = vars(parser.parse_args()).items()
return {key: val for key, val in args if not isinstance(val, NotSet)} |
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