def models_to_compare():
from pita.io import read_bed_transcripts
f1 = open("tests/data/annotation1.bed")
f2 = open("tests/data/annotation2.bed")
t1 = read_bed_transcripts(f1)
t2 = read_bed_transcripts(f2)
result = {}
f = open("tests/data/ann1_vs_ann2.txt")
f.readline() # header
for line in f.readlines():
vals = line.strip().split("\t")
result.setdefault(vals[0], {})
result[vals[0]][vals[1]] = [float(x) for x in vals[2:]]
return t1, t2, result
def test_long_exon_filter(db, t1, t2):
from pita.dbcollection import DbCollection
from pita.io import read_bed_transcripts
for tname, source, exons in read_bed_transcripts(open(t1)):
db.add_transcript("{0}{1}{2}".format("t1", "|", tname), source, exons)
for tname, source, exons in read_bed_transcripts(open(t2)):
db.add_transcript("{0}{1}{2}".format("t2", "|", tname), source, exons)
c = DbCollection(db, [], chrom="chr1")
c.filter_long(l=500, evidence=1)
models = []
for cluster in c.get_best_variants([]):
models.append(cluster)
assert [3, 5] == sorted([len(m) for m in models])
def models_to_compare():
from pita.io import read_bed_transcripts
f1 = open("tests/data/annotation1.bed")
f2 = open("tests/data/annotation2.bed")
t1 = read_bed_transcripts(f1)
t2 = read_bed_transcripts(f2)
result = {}
f = open("tests/data/ann1_vs_ann2.txt")
f.readline() # header
for line in f.readlines():
vals = line.strip().split("\t")
result.setdefault(vals[0], {})
result[vals[0]][vals[1]] = [float(x) for x in vals[2:]]
return t1, t2, result
def test_long_exon_filter(db, t1, t2):
from pita.dbcollection import DbCollection
from pita.io import read_bed_transcripts
for tname, source, exons in read_bed_transcripts(open(t1)):
db.add_transcript("{0}{1}{2}".format("t1", "|", tname), source, exons)
for tname, source, exons in read_bed_transcripts(open(t2)):
db.add_transcript("{0}{1}{2}".format("t2", "|", tname), source, exons)
c = DbCollection(db, chrom="chr1")
c.filter_long(evidence=1)
models = []
for cluster in c.get_connected_models():
for m in cluster:
models.append(m)
assert [1,3,5] == sorted([len(m) for m in models])
def collection(db):
from pita.dbcollection import DbCollection
from pita.io import read_bed_transcripts
bed = "tests/data/scaffold_54_genes.bed"
for tname, source, exons in read_bed_transcripts(open(bed), "test", 0):
db.add_transcript("{0}{1}{2}".format("test", ":::", tname), source, exons)
mc = DbCollection(db)
return mc
def test_get_overlapping_models(db, bedfile):
from pita.io import read_bed_transcripts
from pita.util import get_overlapping_models
for tname, source, exons in read_bed_transcripts(open(bedfile), "test", 0):
db.add_transcript("{0}{1}{2}".format("test", ":::", tname), source, exons)
exons = db.get_exons("JGIv7b.000000226")
assert 72 == len(get_overlapping_models(exons))
def collection(db):
from pita.dbcollection import DbCollection
from pita.io import read_bed_transcripts
bed = "tests/data/scaffold_54_genes.bed"
for tname, source, exons in read_bed_transcripts(open(bed), "test", 0):
db.add_transcript("{0}{1}{2}".format("test", ":::", tname), source,
exons)
mc = DbCollection(db, [])
return mc
def test_get_overlapping_models(db, bedfile):
from pita.io import read_bed_transcripts
from pita.util import get_overlapping_models
for tname, source, exons in read_bed_transcripts(open(bedfile), "test", 0):
db.add_transcript("{0}{1}{2}".format("test", ":::", tname), source,
exons)
exons = db.get_exons("JGIv7b.000000226")
assert 72 == len(get_overlapping_models(exons))
def load_chrom_data(conn, new, chrom, anno_files, data, index=None):
logger = logging.getLogger("pita")
try:
# Read annotation files
db = AnnotationDb(index=index, conn=conn, new=new)
logger.debug("%s %s", chrom, id(db.session))
logger.info("Reading annotation for %s", chrom)
for name, fname, tabix_file, ftype, min_exons in anno_files:
logger.info("Reading annotation from %s", fname)
tabixfile = pysam.Tabixfile(tabix_file)
#tabixfile = fname
if chrom in tabixfile.contigs:
fobj = TabixIteratorAsFile(tabixfile.fetch(chrom))
if ftype == "bed":
it = read_bed_transcripts(fobj,
fname,
min_exons=min_exons,
merge=10)
elif ftype in ["gff", "gtf", "gff3"]:
it = read_gff_transcripts(fobj,
fname,
min_exons=min_exons,
merge=10)
for tname, source, exons in it:
db.add_transcript("{0}{1}{2}".format(name, SEP, tname),
source, exons)
del fobj
tabixfile.close()
del tabixfile
logger.info("Loading data for %s", chrom)
for name, fname, span, extend in data:
if span == "splice":
logger.info("Reading splice data %s from %s", name, fname)
db.get_splice_statistics(chrom, fname, name)
else:
logger.info("Reading BAM data %s from %s", name, fname)
db.get_read_statistics(chrom,
fname,
name=name,
span=span,
extend=extend,
nreads=None)
except:
logger.exception("Error on %s", chrom)
raise
def test_variants(db, variant_track):
from pita.dbcollection import DbCollection
from pita.io import read_bed_transcripts
from pita.util import model_to_bed
for tname, source, exons in read_bed_transcripts(open(variant_track)):
db.add_transcript("{0}{1}{2}".format("t1", "|", tname), source, exons)
c = DbCollection(db)
best_model = [m for m in c.get_connected_models()][0][0]
cuts = [str(e) for e in c.get_node_cuts(best_model)]
assert ["chr1:800+900", "chr1:1400+1500"] == cuts
best_variant = c.get_best_variant(best_model, [{"weight":1,"type":"length","name":"length"}])
s = [str(s) for s in best_variant]
assert ["chr1:100+200", "chr1:400+700", "chr1:800+900", "chr1:1000+1300", "chr1:1400+1500", "chr1:1600+1900", "chr1:2000+2100"] == s
def load_chrom_data(conn, new, chrom, anno_files, data, index=None):
logger = logging.getLogger("pita")
try:
# Read annotation files
db = AnnotationDb(index=index, conn=conn, new=new)
logger.debug("%s %s", chrom, id(db.session))
logger.info("Reading annotation for %s", chrom)
for name, fname, tabix_file, ftype, min_exons in anno_files:
logger.info("Reading annotation from %s", fname)
tabixfile = pysam.Tabixfile(tabix_file)
#tabixfile = fname
if chrom in tabixfile.contigs:
fobj = TabixIteratorAsFile(tabixfile.fetch(chrom))
if ftype == "bed":
it = read_bed_transcripts(fobj, fname,
min_exons=min_exons, merge=10)
elif ftype in ["gff", "gtf", "gff3"]:
it = read_gff_transcripts(fobj, fname,
min_exons=min_exons, merge=10)
for tname, source, exons in it:
db.add_transcript(
"{0}{1}{2}".format(name, SEP, tname),
source, exons)
del fobj
tabixfile.close()
del tabixfile
logger.info("Loading data for %s", chrom)
for name, fname, span, extend in data:
if span == "splice":
logger.info("Reading splice data %s from %s", name, fname)
db.get_splice_statistics(chrom, fname, name)
else:
logger.info("Reading BAM data %s from %s", name, fname)
db.get_read_statistics(chrom, fname, name=name, span=span, extend=extend, nreads=None)
except:
logger.exception("Error on %s", chrom)
raise
def call_utr(inbed, bamfiles, utr5=False, utr3=True):
"""
Call 3' UTR for all genes in a BED12 file based on RNA-seq reads
in BAM files.
"""
# Load genes in BED file
transcripts = read_bed_transcripts(open(inbed))
# No genes
if len(transcripts) == 0:
return
td = dict([(t[0].split("|")[1] + "_", t[2]) for t in transcripts])
#Trying to fix the scaffold struggles
#td = {}
#for t in transcripts:
# if "scaffold" not in t[0]:
# td[t[0].split("_")[1]+"_"] = t[2]
# else:
# inter = t[0].split(":")
# scafName = "_".join(inter[0].split("_")[2:4])
# pos = inter[1].split("_")[0]
# td[scafName+":"+pos+"_"] = t[2]
# Create a BED6 file with exons, used to determine UTR boundaries
sys.stderr.write("Preparing temporary BED files\n")
exonbed = NamedTemporaryFile(prefix="pita.", suffix=".bed")
bed2exonbed(inbed, exonbed.name)
# Determine boundaries using bedtools
genes = pybedtools.BedTool(inbed)
exons = pybedtools.BedTool(exonbed.name)
tmp = NamedTemporaryFile(prefix="pita.", suffix=".bed")
EXTEND = 10000
sys.stderr.write("Determining gene boundaries determined by closest gene\n")
for x in genes.closest(exons, D="a", io=True, iu=True):
transcript = td[x[3]]
# Extend to closest exon or EXTEND, whichever is closer
extend = EXTEND
if (int(x[-1]) >= 0) and (int(x[-1]) < extend):
extend = int(x[-1])
if transcript[0][-1] == "+":
first = transcript[-1]
first[2] += extend
else:
first = transcript[-0]
first[1] -= extend
if first[1] < 0:
first[1] = 0
tmp.write("{}\t{}\t{}\t{}\t{}\t{}\n".format(
first[0],
first[1],
first[2],
x[3],
0,
first[3]
))
tmp.flush()
tmpsam = NamedTemporaryFile(prefix="pita.", suffix=".sam")
tmpbam = NamedTemporaryFile(prefix="pita.")
# Retrieve header from first BAM file
sp.call("samtools view -H {} > {}".format(bamfiles[0], tmpsam.name), shell=True)
# Filter all BAM files for the specific regions. This runs much faster
# then running bedtools coverage on all individual BAM files
tmp_check = NamedTemporaryFile(prefix="pita.", suffix=".bam")
cmd = "samtools view -L {} {} > {}"
sys.stderr.write("Merging bam files\n")
for bamfile in bamfiles:
try:
sp.check_call(cmd.format(tmp.name, bamfile, tmp_check.name), shell=True)
sp.call("cat {} >> {}".format(tmp_check.name, tmpsam.name), shell=True)
except sp.CalledProcessError as e:
sys.stderr.write("Error in file {}, skipping:\n".format(bamfile))
sys.stderr.write("{}\n".format(e))
tmp_check.close()
def compare_annotation_files(fname1, fname2):
file1 = open(fname1)
file2 = open(fname2)
data1 = read_bed_transcripts(file1)
data2 = read_bed_transcripts(file2)
result = compare_annotation(data1, data2)
for t2, x, exons in data2:
if not result.has_key(t2) or result[t2] == {}:
print "{0}\t{1}\t{2}\t{3}".format(exons[0][0], exons[0][1],
exons[-1][2], 1)
#print result
cluster = {}
t2_cluster = {}
for transcript2, d in result.items():
for transcript1 in d.keys():
if not cluster.has_key(transcript1):
cluster[transcript1] = d.keys()
else:
for t in d.keys():
if not t in cluster[transcript1]:
cluster[transcript1].append(t)
for t1 in cluster[transcript1]:
t2_cluster.setdefault(t1, []).append(transcript2)
clusters = set([tuple(x) for x in cluster.values()])
loc = {}
for name, x, exons in data1:
loc[name] = [exons[0][0], exons[0][1], exons[-1][2]]
for cluster in clusters:
t2s = set(t2_cluster[cluster[0]])
min_acs = []
for transcript1 in cluster:
acs = []
for transcript2 in t2s:
if result[transcript2].has_key(transcript1):
sn, sp, ac = result[transcript2][transcript1]
#print transcript1, transcript2, sn, sp, ac
acs.append(1 - sp)
min_acs.append(np.min(acs))
#print transcript1, np.min(acs)
#print "###"
#print cluster, min_acs
chrom = loc[cluster[0]][0]
start = np.min([loc[t][1] for t in cluster])
end = np.max([loc[t][2] for t in cluster])
print "{0}\t{1}\t{2}\t{3}".format(chrom, start, end, np.mean(min_acs))
#print loc
#for t in cluster:
sys.exit()
for k, v in result.items():
if len(v.keys()) > 0:
print "{0}\t{1}\t{2}".format(loc[k], k, 1 - np.max(v.values()))
else:
print "{0}\t{1}\t{2}".format(loc[k], k, 1.0)
def compare_annotation_files(fname1, fname2):
file1 = open(fname1)
file2 = open(fname2)
data1 = read_bed_transcripts(file1)
data2 = read_bed_transcripts(file2)
result = compare_annotation(data1, data2)
for t2, x, exons in data2:
if not result.has_key(t2) or result[t2] == {}:
print "{0}\t{1}\t{2}\t{3}".format(exons[0][0], exons[0][1], exons[-1][2], 1)
#print result
cluster = {}
t2_cluster = {}
for transcript2,d in result.items():
for transcript1 in d.keys():
if not cluster.has_key(transcript1):
cluster[transcript1] = d.keys()
else:
for t in d.keys():
if not t in cluster[transcript1]:
cluster[transcript1].append(t)
for t1 in cluster[transcript1]:
t2_cluster.setdefault(t1, []).append(transcript2)
clusters = set([tuple(x) for x in cluster.values()])
loc = {}
for name,x,exons in data1:
loc[name] = [exons[0][0], exons[0][1], exons[-1][2]]
for cluster in clusters:
t2s = set(t2_cluster[cluster[0]])
min_acs = []
for transcript1 in cluster:
acs = []
for transcript2 in t2s:
if result[transcript2].has_key(transcript1):
sn, sp, ac = result[transcript2][transcript1]
#print transcript1, transcript2, sn, sp, ac
acs.append(1 - sp)
min_acs.append(np.min(acs))
#print transcript1, np.min(acs)
#print "###"
#print cluster, min_acs
chrom = loc[cluster[0]][0]
start = np.min([loc[t][1] for t in cluster])
end = np.max([loc[t][2] for t in cluster])
print "{0}\t{1}\t{2}\t{3}".format(chrom, start, end, np.mean(min_acs))
#print loc
#for t in cluster:
sys.exit()
for k,v in result.items():
if len(v.keys()) > 0:
print "{0}\t{1}\t{2}".format(loc[k], k, 1 - np.max(v.values()))
else:
print "{0}\t{1}\t{2}".format(loc[k], k, 1.0)
def call_utr(inbed, bamfiles, utr5=False, utr3=True):
"""
Call 3' UTR for all genes in a BED12 file based on RNA-seq reads
in BAM files.
"""
# Load genes in BED file
transcripts = read_bed_transcripts(open(inbed))
# No genes
if len(transcripts) == 0:
return
td = dict([(t[0].split("|")[1] + "_", t[2]) for t in transcripts])
#Trying to fix the scaffold struggles
#td = {}
#for t in transcripts:
# if "scaffold" not in t[0]:
# td[t[0].split("_")[1]+"_"] = t[2]
# else:
# inter = t[0].split(":")
# scafName = "_".join(inter[0].split("_")[2:4])
# pos = inter[1].split("_")[0]
# td[scafName+":"+pos+"_"] = t[2]
# Create a BED6 file with exons, used to determine UTR boundaries
sys.stderr.write("Preparing temporary BED files\n")
exonbed = NamedTemporaryFile(prefix="pita.", suffix=".bed")
bed2exonbed(inbed, exonbed.name)
# Determine boundaries using bedtools
genes = pybedtools.BedTool(inbed)
exons = pybedtools.BedTool(exonbed.name)
tmp = NamedTemporaryFile(prefix="pita.", suffix=".bed")
EXTEND = 10000
sys.stderr.write(
"Determining gene boundaries determined by closest gene\n")
for x in genes.closest(exons, D="a", io=True, iu=True):
transcript = td[x[3]]
# Extend to closest exon or EXTEND, whichever is closer
extend = EXTEND
if (int(x[-1]) >= 0) and (int(x[-1]) < extend):
extend = int(x[-1])
if transcript[0][-1] == "+":
first = transcript[-1]
first[2] += extend
else:
first = transcript[-0]
first[1] -= extend
if first[1] < 0:
first[1] = 0
tmp.write("{}\t{}\t{}\t{}\t{}\t{}\n".format(first[0], first[1],
first[2], x[3], 0,
first[3]))
tmp.flush()
tmpsam = NamedTemporaryFile(prefix="pita.", suffix=".sam")
tmpbam = NamedTemporaryFile(prefix="pita.")
# Retrieve header from first BAM file
sp.call("samtools view -H {} > {}".format(bamfiles[0], tmpsam.name),
shell=True)
# Filter all BAM files for the specific regions. This runs much faster
# then running bedtools coverage on all individual BAM files
tmp_check = NamedTemporaryFile(prefix="pita.", suffix=".bam")
cmd = "samtools view -L {} {} > {}"
sys.stderr.write("Merging bam files\n")
for bamfile in bamfiles:
try:
sp.check_call(cmd.format(tmp.name, bamfile, tmp_check.name),
shell=True)
sp.call("cat {} >> {}".format(tmp_check.name, tmpsam.name),
shell=True)
except sp.CalledProcessError as e:
sys.stderr.write("Error in file {}, skipping:\n".format(bamfile))
sys.stderr.write("{}\n".format(e))
tmp_check.close()
# Created sorted and index bam
cmd = "samtools view -Sb {} | samtools sort -m 4G - {}"
sp.call(cmd.format(tmpsam.name, tmpbam.name), shell=True)
sp.call("samtools index {}.bam".format(tmpbam.name), shell=True)
# Close and remove temporary SAM file
tmpsam.close()
sys.stderr.write("Calculating coverage\n")
cmd = "bedtools coverage -abam {} -b {} -d -split "
p = sp.Popen(cmd.format(tmpbam.name + ".bam", tmp.name),
shell=True,
stdout=sp.PIPE,
bufsize=1)
sys.stderr.write("Calling UTRs\n")
data = []
current = [None]
utr = {}
for line in iter(p.stdout.readline, b''):
vals = line.strip().split("\t")
if vals[3] != current[0]:
if len(data) > 0:
result = call_cpt(current[1], current[2], current[3], data,
len(bamfile))
#print result
if result:
utr[current[0]] = result
data = []
current = [vals[3], int(vals[1]), int(vals[2]), vals[5]]
data.append(int(vals[7]))
if current[0]:
result = call_cpt(current[1], current[2], current[3], data,
len(bamfiles))
if result:
utr[current[0]] = result
for fname in [tmpbam.name + ".bam", tmpbam.name + ".bam.bai"]:
if os.path.exists(fname):
os.unlink(fname)
tmpbam.close()
tmp.close()
return utr
def call_utr(inbed, bamfiles, utr5=False, utr3=True):
"""
Call 3' UTR for all genes in a BED12 file based on RNA-seq reads
in BAM files.
"""
# Load genes in BED file
transcripts = read_bed_transcripts(open(inbed))
# No genes
if len(transcripts) == 0:
return
td = dict([(t[0].split("|")[1] + "_", t[2]) for t in transcripts])
#Trying to fix the scaffold struggles
#td = {}
#for t in transcripts:
# if "scaffold" not in t[0]:
# td[t[0].split("_")[1]+"_"] = t[2]
# else:
# inter = t[0].split(":")
# scafName = "_".join(inter[0].split("_")[2:4])
# pos = inter[1].split("_")[0]
# td[scafName+":"+pos+"_"] = t[2]
# Create a BED6 file with exons, used to determine UTR boundaries
sys.stderr.write("Preparing temporary BED files\n")
exonbed = NamedTemporaryFile(prefix="pita.", suffix=".bed")
bed2exonbed(inbed, exonbed.name)
# Determine boundaries using bedtools
genes = pybedtools.BedTool(inbed)
exons = pybedtools.BedTool(exonbed.name)
tmp = NamedTemporaryFile(prefix="pita.", suffix=".bed")
EXTEND = 10000
sys.stderr.write("Determining gene boundaries determined by closest gene\n")
for x in genes.closest(exons, D="a", io=True, iu=True):
transcript = td[x[3]]
# Extend to closest exon or EXTEND, whichever is closer
extend = EXTEND
if (int(x[-1]) >= 0) and (int(x[-1]) < extend):
extend = int(x[-1])
if transcript[0][-1] == "+":
first = transcript[-1]
first[2] += extend
else:
first = transcript[-0]
first[1] -= extend
if first[1] < 0:
first[1] = 0
tmp.write("{}\t{}\t{}\t{}\t{}\t{}\n".format(
first[0],
first[1],
first[2],
x[3],
0,
first[3]
))
tmp.flush()
tmpsam = NamedTemporaryFile(prefix="pita.", suffix=".sam")
tmpbam = NamedTemporaryFile(prefix="pita.")
# Retrieve header from first BAM file
sp.call("samtools view -H {} > {}".format(bamfiles[0], tmpsam.name), shell=True)
# Filter all BAM files for the specific regions. This runs much faster
# then running bedtools coverage on all individual BAM files
tmp_check = NamedTemporaryFile(prefix="pita.", suffix=".bam")
cmd = "samtools view -L {} {} > {}"
sys.stderr.write("Merging bam files\n")
for bamfile in bamfiles:
try:
sp.check_call(cmd.format(tmp.name, bamfile, tmp_check.name), shell=True)
sp.call("cat {} >> {}".format(tmp_check.name, tmpsam.name), shell=True)
except sp.CalledProcessError as e:
sys.stderr.write("Error in file {}, skipping:\n".format(bamfile))
sys.stderr.write("{}\n".format(e))
tmp_check.close()
# Created sorted and index bam
cmd = "samtools view -Sb {} | samtools sort -m 4G - {}"
sp.call(cmd.format(tmpsam.name, tmpbam.name), shell=True)
sp.call("samtools index {}.bam".format(tmpbam.name), shell=True)
# Close and remove temporary SAM file
tmpsam.close()
sys.stderr.write("Calculating coverage\n")
cmd = "bedtools coverage -abam {} -b {} -d -split "
p = sp.Popen(cmd.format(tmpbam.name + ".bam", tmp.name), shell=True, stdout=sp.PIPE, bufsize=1)
sys.stderr.write("Calling UTRs\n")
data = []
current = [None]
utr = {}
for line in iter(p.stdout.readline, b''):
vals = line.strip().split("\t")
if vals[3] != current[0]:
if len(data) > 0:
result = call_cpt(current[1], current[2], current[3], data, len(bamfile))
#print result
if result:
utr[current[0]] = result
data = []
current = [vals[3], int(vals[1]), int(vals[2]), vals[5]]
data.append(int(vals[7]))
if current[0]:
result = call_cpt(current[1], current[2], current[3], data, len(bamfiles))
if result:
utr[current[0]] = result
for fname in [tmpbam.name + ".bam", tmpbam.name + ".bam.bai"]:
if os.path.exists(fname):
os.unlink(fname)
tmpbam.close()
tmp.close()
return utr
