def cluster(args):
outdir = os.path.abspath(args.outdir)
if not os.path.exists(outdir):
os.mkdir(outdir)
ncpus = args.ncpus
clusters = []
motifs = read_motifs(args.inputfile)
if len(motifs) == 1:
clusters = [[motifs[0], motifs]]
else:
tree = cluster_motifs(
args.inputfile,
"total",
"wic",
"mean",
True,
threshold=args.threshold,
include_bg=True,
ncpus=ncpus,
)
clusters = tree.getResult()
ids = _create_images(outdir, clusters)
_write_report(outdir, ids, tree, clusters)
def test1_cluster_motifs(self):
""" cluster a pwm file with motifs """
# Run clustering
tree = cluster_motifs(self.pwm,
"total",
"wic",
"mean",
True,
threshold=0.95,
include_bg=True)
clusters = tree.getResult()
self.assertEquals(2, len(clusters))
self.assertEquals([3,2], [len(c[1]) for c
in sorted(clusters, cmp=lambda x,y: cmp(len(x), len(y)))])
def test1_cluster_motifs(self):
""" cluster a pwm file with motifs """
# Run clustering
tree = cluster_motifs(self.pwm,
"total",
"wic",
"mean",
True,
threshold=0.95,
include_bg=True)
clusters = tree.getResult()
self.assertEqual(2, len(clusters))
self.assertEqual([3,2], [len(c[1]) for c
in sorted(clusters, key=lambda x: len(x))])
def cluster(args):
outdir = os.path.abspath(args.outdir)
if not os.path.exists(outdir):
os.mkdir(outdir)
ncpus = args.ncpus
clusters = []
motifs = pwmfile_to_motifs(args.inputfile)
if len(motifs) == 1:
clusters = [[motifs[0], motifs]]
else:
tree = cluster_motifs(args.inputfile, "total", "wic", "mean", True, threshold=args.threshold, include_bg=True, ncpus=ncpus)
clusters = tree.getResult()
ids = _create_images(outdir, clusters)
_write_report(outdir, ids, tree, clusters)
def cluster(args):
revcomp = not args.single
outdir = os.path.abspath(args.outdir)
if not os.path.exists(outdir):
os.mkdir(outdir)
trim_ic = 0.2
clusters = []
motifs = pwmfile_to_motifs(args.inputfile)
if len(motifs) == 1:
clusters = [[motifs[0], motifs]]
else:
tree = cluster_motifs(args.inputfile, "total", "wic", "mean", True, threshold=args.threshold, include_bg=True)
clusters = tree.getResult()
ids = []
mc = MotifComparer()
sys.stderr.write("Creating images\n")
for cluster,members in clusters:
cluster.trim(trim_ic)
cluster.to_img(os.path.join(outdir,"%s.png" % cluster.id), format="PNG")
ids.append([cluster.id, {"src":"%s.png" % cluster.id},[]])
if len(members) > 1:
scores = {}
for motif in members:
scores[motif] = mc.compare_motifs(cluster, motif, "total", "wic", "mean", pval=True)
add_pos = sorted(scores.values(),cmp=lambda x,y: cmp(x[1], y[1]))[0][1]
for motif in members:
score, pos, strand = scores[motif]
add = pos - add_pos
if strand in [1,"+"]:
pass
else:
#print "RC %s" % motif.id
rc = motif.rc()
rc.id = motif.id
motif = rc
#print "%s\t%s" % (motif.id, add)
motif.to_img(os.path.join(outdir, "%s.png" % motif.id.replace(" ", "_")), format="PNG", add_left=add)
ids[-1][2] = [dict([("src", "%s.png" % motif.id.replace(" ", "_")), ("alt", motif.id.replace(" ", "_"))]) for motif in members]
config = MotifConfig()
env = jinja2.Environment(loader=jinja2.FileSystemLoader([config.get_template_dir()]))
template = env.get_template("cluster_template.jinja.html")
result = template.render(motifs=ids)
with open(os.path.join(outdir, "cluster_report.html"), "w") as f:
f.write(result.encode('utf-8'))
f = open(os.path.join(outdir, "cluster_key.txt"), "w")
for id in ids:
f.write("%s\t%s\n" % (id[0], ",".join([x["alt"] for x in id[2]])))
f.close()
f = open(os.path.join(outdir, "clustered_motifs.pwm"), "w")
if len(clusters) == 1 and len(clusters[0][1]) == 1:
f.write("%s\n" % clusters[0][0].to_pwm())
else:
for motif in tree.get_clustered_motifs():
f.write("%s\n" % motif.to_pwm())
f.close()
def _cluster_motifs(self, pfm_file, cluster_pwm, dir, threshold):
self.logger.info("clustering significant motifs.")
trim_ic = 0.2
clusters = []
motifs = read_motifs(open(pfm_file), fmt="pwm")
if len(motifs) == 1:
clusters = [[motifs[0], motifs]]
else:
tree = cluster_motifs(pfm_file,
"total",
"wic",
"mean",
True,
threshold=float(threshold),
include_bg=True,
progress=False)
clusters = tree.getResult()
ids = []
mc = MotifComparer()
for cluster, members in clusters:
cluster.trim(trim_ic)
cluster.to_img(os.path.join(self.imgdir, "%s.png" % cluster.id),
format="PNG")
ids.append([cluster.id, {"src": "images/%s.png" % cluster.id}, []])
if len(members) > 1:
scores = {}
for motif in members:
scores[motif] = mc.compare_motifs(cluster,
motif,
"total",
"wic",
"mean",
pval=True)
add_pos = sorted(scores.values(),
cmp=lambda x, y: cmp(x[1], y[1]))[0][1]
for motif in members:
score, pos, strand = scores[motif]
add = pos - add_pos
if strand in [1, "+"]:
pass
else:
#print "RC %s" % motif.id
rc = motif.rc()
rc.id = motif.id
motif = rc
#print "%s\t%s" % (motif.id, add)
motif.to_img(os.path.join(
self.imgdir, "%s.png" % motif.id.replace(" ", "_")),
format="PNG",
add_left=add)
ids[-1][2] = [
dict([("src", "images/%s.png" % motif.id.replace(" ", "_")),
("alt", motif.id.replace(" ", "_"))])
for motif in members
]
env = jinja2.Environment(
loader=jinja2.FileSystemLoader([self.config.get_template_dir()]))
template = env.get_template("cluster_template.jinja.html")
result = template.render(expname=self.basename,
motifs=ids,
inputfile=self.inputfile,
date=datetime.today().strftime("%d/%m/%Y"),
version=GM_VERSION)
f = open(self.cluster_report, "w")
f.write(result.encode('utf-8'))
f.close()
f = open(cluster_pwm, "w")
if len(clusters) == 1 and len(clusters[0][1]) == 1:
f.write("%s\n" % clusters[0][0].to_pwm())
else:
for motif in tree.get_clustered_motifs():
f.write("%s\n" % motif.to_pwm())
f.close()
self.logger.debug("Clustering done. See the result in %s",
self.cluster_report)
return clusters
def nmer_predict(fastafile):
from tempfile import NamedTemporaryFile,mkdtemp
from gimmemotifs.fasta import Fasta
from numpy import sum,histogram
from subprocess import Popen,PIPE
from gimmemotifs.motif import Motif,motif_from_align
from gimmemotifs.cluster import cluster_motifs
from string import maketrans
def rc(seq):
t = maketrans("ATCG", "TAGC")
return seq[::-1].translate(t)
f = Fasta(fastafile)
nmer = {}
N = {6:4, 8:3,10:2,12:1}
tmp = NamedTemporaryFile()
abs_cutoff = len(f.items()) / 100.0 * 2
for check_n,cutoff in N.items():
for id,seq in f.items():
for i in range(len(seq) - check_n):
n = seq[i: i + check_n]
nmer.setdefault(n.upper(), []).append(i)
for n,pos in nmer.items():
if len(pos) > abs_cutoff:
hist = histogram(pos, bins=9, range=(0,200))[0]
if sum(hist[3:6]) > sum(hist[0:3] * N[len(n)]) and sum(hist[3:6]) > sum(hist[7:]) * N[len(n)]:
tmp.write(">%s\n" % n)
for char in n:
w = []
for x in ["A", "C", "G", "T"]:
if x == char:
w.append(len(pos))
else:
w.append(0)
tmp.write("\t".join([str(x) for x in w]) + "\n")
tmp.flush()
tmpname = tmp.name
tree = cluster_motifs(tmpname, "subtotal", "ed", "mean", False, threshold=-0.1, include_bg=False)
clusters = tree.getResult()
def refine_by_scanning(motifs, fastafile):
tmp_gff = NamedTemporaryFile()
file_in = NamedTemporaryFile()
for m in motifs:
file_in.write("%s\n" % m.to_pfm())
file_in.flush()
cmd = "pwmscan.py -i %s -p %s -c 0.8 > %s" % (fastafile, file_in.name, tmp_gff.name)
p = Popen(cmd, shell=True)
stdout,stderr = p.communicate()
aligns = {}
for line in open(tmp_gff.name):
vals = line.strip().split("\t")
motif,instance = [x.split(" ")[1].replace('"', "") for x in vals[8].split(" ; ")]
if vals[6] == "+":
aligns.setdefault(motif,[]).append(instance.upper())
else:
aligns.setdefault(motif,[]).append(rc(instance.upper()))
tmp_out = NamedTemporaryFile()
refined_motifs = []
for id,align in aligns.items():
if len(align) > 10:
motif = motif_from_align(align)
refined_motifs.append(motif)
return refined_motifs
motifs = refine_by_scanning([x[0] for x in clusters], fastafile)
tmp4 = NamedTemporaryFile()
for m in motifs:
tmp4.write("%s\n" % m.to_pfm())
tmp4.flush()
motifs = []
tree = cluster_motifs(tmp4.name, "total", "wic", "mean", True, threshold=0.95, include_bg=True)
clusters = tree.getResult()
for i, (cluster,members) in enumerate(clusters):
cluster.id = "Nmer_%s" % (i + 1)
motifs.append(cluster)
refined_motifs = refine_by_scanning(motifs, fastafile)
for i,m in enumerate(refined_motifs):
m.id = "WannaMotif_%s" % (i + 1)
return refined_motifs, "", ""
def _cluster_motifs(self, pfm_file, cluster_pwm, dir, threshold):
self.logger.info("clustering significant motifs.")
trim_ic = 0.2
clusters = []
motifs = read_motifs(open(pfm_file), fmt="pwm")
if len(motifs) == 1:
clusters = [[motifs[0], motifs]]
else:
tree = cluster_motifs(
pfm_file,
"total",
"wic",
"mean",
True,
threshold=float(threshold),
include_bg=True,
progress=False
)
clusters = tree.getResult()
ids = []
mc = MotifComparer()
for cluster,members in clusters:
cluster.trim(trim_ic)
cluster.to_img(os.path.join(self.imgdir,"%s.png" % cluster.id), format="PNG")
ids.append([cluster.id, {"src":"images/%s.png" % cluster.id},[]])
if len(members) > 1:
scores = {}
for motif in members:
scores[motif] = mc.compare_motifs(cluster, motif, "total", "wic", "mean", pval=True)
add_pos = sorted(scores.values(),cmp=lambda x,y: cmp(x[1], y[1]))[0][1]
for motif in members:
score, pos, strand = scores[motif]
add = pos - add_pos
if strand in [1,"+"]:
pass
else:
#print "RC %s" % motif.id
rc = motif.rc()
rc.id = motif.id
motif = rc
#print "%s\t%s" % (motif.id, add)
motif.to_img(os.path.join(self.imgdir, "%s.png" % motif.id.replace(" ", "_")), format="PNG", add_left=add)
ids[-1][2] = [dict([("src", "images/%s.png" % motif.id.replace(" ", "_")), ("alt", motif.id.replace(" ", "_"))]) for motif in members]
env = jinja2.Environment(loader=jinja2.FileSystemLoader([self.config.get_template_dir()]))
template = env.get_template("cluster_template.jinja.html")
result = template.render(expname=self.basename, motifs=ids, inputfile=self.inputfile, date=datetime.today().strftime("%d/%m/%Y"), version=GM_VERSION)
f = open(self.cluster_report, "w")
f.write(result.encode('utf-8'))
f.close()
f = open(cluster_pwm, "w")
if len(clusters) == 1 and len(clusters[0][1]) == 1:
f.write("%s\n" % clusters[0][0].to_pwm())
else:
for motif in tree.get_clustered_motifs():
f.write("%s\n" % motif.to_pwm())
f.close()
self.logger.debug("Clustering done. See the result in %s",
self.cluster_report)
return clusters
def nmer_predict(fastafile):
from tempfile import NamedTemporaryFile, mkdtemp
from gimmemotifs.fasta import Fasta
from numpy import sum, histogram
from subprocess import Popen, PIPE
from gimmemotifs.motif import Motif, motif_from_align
from gimmemotifs.cluster import cluster_motifs
from string import maketrans
def rc(seq):
t = maketrans("ATCG", "TAGC")
return seq[::-1].translate(t)
f = Fasta(fastafile)
nmer = {}
N = {6: 4, 8: 3, 10: 2, 12: 1}
tmp = NamedTemporaryFile()
abs_cutoff = len(f.items()) / 100.0 * 2
for check_n, cutoff in N.items():
for id, seq in f.items():
for i in range(len(seq) - check_n):
n = seq[i:i + check_n]
nmer.setdefault(n.upper(), []).append(i)
for n, pos in nmer.items():
if len(pos) > abs_cutoff:
hist = histogram(pos, bins=9, range=(0, 200))[0]
if sum(hist[3:6]) > sum(hist[0:3] * N[len(n)]) and sum(
hist[3:6]) > sum(hist[7:]) * N[len(n)]:
tmp.write(">%s\n" % n)
for char in n:
w = []
for x in ["A", "C", "G", "T"]:
if x == char:
w.append(len(pos))
else:
w.append(0)
tmp.write("\t".join([str(x) for x in w]) + "\n")
tmp.flush()
tmpname = tmp.name
tree = cluster_motifs(tmpname,
"subtotal",
"ed",
"mean",
False,
threshold=-0.1,
include_bg=False)
clusters = tree.getResult()
def refine_by_scanning(motifs, fastafile):
tmp_gff = NamedTemporaryFile()
file_in = NamedTemporaryFile()
for m in motifs:
file_in.write("%s\n" % m.to_pfm())
file_in.flush()
cmd = "pwmscan.py -i %s -p %s -c 0.8 > %s" % (fastafile, file_in.name,
tmp_gff.name)
p = Popen(cmd, shell=True)
stdout, stderr = p.communicate()
aligns = {}
for line in open(tmp_gff.name):
vals = line.strip().split("\t")
motif, instance = [
x.split(" ")[1].replace('"', "") for x in vals[8].split(" ; ")
]
if vals[6] == "+":
aligns.setdefault(motif, []).append(instance.upper())
else:
aligns.setdefault(motif, []).append(rc(instance.upper()))
tmp_out = NamedTemporaryFile()
refined_motifs = []
for id, align in aligns.items():
if len(align) > 10:
motif = motif_from_align(align)
refined_motifs.append(motif)
return refined_motifs
motifs = refine_by_scanning([x[0] for x in clusters], fastafile)
tmp4 = NamedTemporaryFile()
for m in motifs:
tmp4.write("%s\n" % m.to_pfm())
tmp4.flush()
motifs = []
tree = cluster_motifs(tmp4.name,
"total",
"wic",
"mean",
True,
threshold=0.95,
include_bg=True)
clusters = tree.getResult()
for i, (cluster, members) in enumerate(clusters):
cluster.id = "Nmer_%s" % (i + 1)
motifs.append(cluster)
refined_motifs = refine_by_scanning(motifs, fastafile)
for i, m in enumerate(refined_motifs):
m.id = "WannaMotif_%s" % (i + 1)
return refined_motifs, "", ""