def main():
# Functions for jtools:
# Get sequence
# Detect tandem acceptors NAGNAG
# Annotate with genes
# Jiggle
# Bed to juncid
# Guess frame
# Find stops in intron + in frame
# SVM recomputes
# Splice site strength? ppt?
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Load a fasta file
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
global f
# Opening fasta filehandle
print >> sys.stderr, "[%s] Opening fasta file" % (spanki_utils.timestamp())
f = Fasta(fastafile)
fastachr = set(sorted(f.keys()))
#print fastachr
########################################################
### Parsing a juncbed file
########################################################
if (juncbedfile):
print "juncid\toriginal_id\tdastring"
print >> sys.stderr, "Loading", juncbedfile
lines = csv.reader(open(juncbedfile, 'rb'), delimiter='\t')
z = []
for line in lines:
pattern = re.compile('track')
track = pattern.search(line[0])
if not track:
values = line
blocksizes = values[10].split(",")
blockstarts = values[11].split(",")
chr = values[0]
rangestart = int(values[1]) - 1
rangeend = int(values[2])
strand = values[5]
id = values[3]
intronstart = rangestart + int(blocksizes[0]) + 2
intronend = rangeend - int(blocksizes[1])
# Or..
#intronend = rangestart + int(blocksizes[0]) + int(blockstarts[1])
#chrXHet 800 1767 JUNC00000001 2 + 800 1767 255,0,0 2 20,63 0,904
intronsize = intronend - intronstart;
juncid = chr + ":" + str(intronstart) + "_" + str(intronend) + ":" + strand
dastring = intron_sequence_single(juncid,f)
z.append(str(dastring))
print juncid, values[3], dastring
print >> sys.stderr, "Distribution of detected motifs:\n",Counter(z)
quit("Done")
########################################################
### Parsing a intronbed file
########################################################
#scaffold_12916 13833982 13834044 10
#scaffold_12916 13838614 13838676 67
#scaffold_12916 13839119 13839204 75
if (intronbedfile):
print "juncid\tid\tdastring"
lines = csv.reader(open(intronbedfile, 'rb'), delimiter='\t')
for line in lines:
pattern = re.compile('track')
track = pattern.search(line[0])
values = line
if not track:
chr = values[0]
intronstart = int(values[1]) + 1
intronend = int(values[2]) - 1
strand = "+"
id = values[0]
intronsize = intronend - intronstart;
juncid = chr + ":" + str(intronstart) + "_" + str(intronend) + ":" + strand
dastring = intron_sequence_single(juncid,f)
print juncid, values[3], dastring
quit("Done")
########################################################
########################################################
### Converting from another format
########################################################
if gfffile:
#reflist = tab_to_dict(gff)
results = collections.defaultdict(lambda : collections.defaultdict(dict))
gffdict = gff_to_dict(gfffile)
for x in gffdict:
#print x
#print gffdict[x]
if (gffdict[x]['feature_type'] == "exon_junction"):
juncid = gffdict[x]['chr'] + ":" + str(int(gffdict[x]['start']) + 1) + "_" + str(int(gffdict[x]['end']) - 1) + ":" + gffdict[x]['strand']
elif (gffdict[x]['feature_type'] == "intron"):
juncid = gffdict[x]['chr'] + ":" + gffdict[x]['start'] + "_" + gffdict[x]['end'] + ":" + gffdict[x]['strand']
dastring = intron_sequence_single(juncid,f)
#print dastring
results[x]['juncid'] = juncid
results[x]['dastring'] = dastring
print "ID\tjuncid\tdastring"
for x in sorted(results.iterkeys()):
print x, "\t", results[x]['juncid'], "\t", results[x]['dastring']
quit()
########################################################
### Converting from another format
########################################################
if gtffile:
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Intializing the reference
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# You need the gtf file, and the fasta file
lookup = spanki_utils.prep_ref(gtffile,fastafile,output_dir)
## Note that you now have a reference called ref.bam, and a lookup dict
#tmp_dir = output_dir + "/tmp/"
#reffile = tmp_dir + "/ref.bam"
reffile = "tmp/ref.bam"
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Load an annotation, flattened as bam
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
print >> sys.stderr, "[%s] Trying to load annotation as bam" % (spanki_utils.timestamp())
reffh = pysam.Samfile( reffile, "rb" )
edgedict, refjuncs = spanki_parse_utils.parseRefAsBam(reffh)
reffh.close()
print >> sys.stderr, "[%s] Done loading annotation as bam" % (spanki_utils.timestamp())
for junc in refjuncs:
print junc
quit()
### Below are functions that operate on a junction list
########################################################
if jlist:
#~~~~~~~~~~~~~~~~~~~
# Load reference junction list
#~~~~~~~~~~~~~~~~~~~
reflist = tab_to_dict(jlist)
# Find the junctions in jlist that are not in jtab
myjuncs = reflist.keys()
print >> sys.stderr, len(myjuncs), "in junction list"
updonor = 20
downdonor = 2
upacceptor = 2
downacceptor = 20
for x in myjuncs:
print x
j1 = Junctionid(x)
j1.display()
if j1.strand == "+":
#print Seq(f[j1.chr][j1.donor-updonor:j1.donor], IUPAC.unambiguous_dna)
tempseq = Seq(f[j1.chr][j1.donor-updonor:j1.donor], IUPAC.unambiguous_dna)
#print "***", tempseq.translate()
#print Seq(f[j1.chr][j1.donor:j1.donor + downdonor], IUPAC.unambiguous_dna)
#print Seq(f[j1.chr][j1.acceptor-upacceptor:j1.acceptor], IUPAC.unambiguous_dna)
#print Seq(f[j1.chr][j1.acceptor:j1.acceptor + downacceptor], IUPAC.unambiguous_dna)
nagstring = find_nag(Seq(f[j1.chr][j1.acceptor:j1.acceptor + downacceptor], IUPAC.unambiguous_dna))
print nagstring
elif j1.strand == "-":
pass
#print Seq(f[j1.chr][j1.donor:j1.donor + updonor], IUPAC.unambiguous_dna).reverse_complement()
#print Seq(f[j1.chr][j1.donor - downdonor:j1.donor], IUPAC.unambiguous_dna).reverse_complement()
#print Seq(f[j1.chr][j1.acceptor:j1.acceptor + upacceptor], IUPAC.unambiguous_dna).reverse_complement()
#print Seq(f[j1.chr][j1.acceptor-downacceptor:j1.acceptor], IUPAC.unambiguous_dna).reverse_complement()
else:
quit("Don't recognize strand")
#fiveprimeflank = fiveprimeflank.reverse_complement()
quit("Done")
quit()
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Older code that's not used yet
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# IRT
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
bamfh = pysam.Samfile( bamfile, "rb" )
#for alignedread in samfile:
# Need some kind of iterator to getread length from first alignment in sam
print >> sys.stderr, "[%s] Getting intron read-though (IRT), may take awhile" % (spanki_utils.timestamp())
IRT = intron_readthrough(myjuncs,bamfh)
bamfh.close()
print >> sys.stderr, "[%s] Done getting IRT" % (spanki_utils.timestamp())
#for edgeid in covbyedge.keys():
# print edgeid, covbyedge[edgeid]
# These are the fields you end up with after merging:
#juncid geneassign cov lirt rirt irt dncov ancov numsamps
#chr2L:22427471_22427525:- none 2 57 28 85 0 0 1
#chr2R:5702257_5702656:+ FBgn0040092 13 0 0 0 0 0 2
#chr2L:11436293_11436415:- FBgn0261648 23 0 0 0 0 0 2
#chr2R:9334834_9336812:- FBgn0013765 6 0 0 0 0 0 2
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Now compile the results
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# First show how you can get in hte myjuncs list
print >> sys.stderr, "Printing results table"
print >> juncs_out, "juncid\tgeneassign\tannostatus\tintron_size\tgmcode\tregcode\tcov\tlirt\trirt\tirt\tdncov\tancov"
for juncid in sorted(keys2):
try:
results = [juncid, jdict[juncid]['geneassign'], jdict[juncid]['annostatus'], jdict[juncid]['intron_size'], jdict[juncid]['gmcode'], jdict[juncid]['regcode'], jdict[juncid]['cov'], jdict[juncid]['lirt'], jdict[juncid]['rirt'], jdict[juncid]['irt'], jdict[juncid]['dncov'], jdict[juncid]['ancov']]
print >> juncs_out, ('\t'.join(map(str,results)))
except KeyError:
#myjuncs.append(juncid)
j1 = Junctionid(juncid)
donid = j1.donid
accid = j1.accid
if covbyedge[donid]: dncov = covbyedge[donid]
else: dncov = 0
if covbyedge[accid]: ancov = covbyedge[accid]
else: ancov = 0
results = [juncid, reflist[juncid]['geneassign'], reflist[juncid]['annostatus'], reflist[juncid]['intron_size'], reflist[juncid]['gmcode'], reflist[juncid]['regcode'], 0, IRT[juncid]['lirt'], IRT[juncid]['rirt'], IRT[juncid]['irt'], dncov, ancov]
#print(results, sep='\t')
print >> juncs_out, ('\t'.join(map(str,results)))
quit("done")
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Parse the read alignments
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Parse the bam file
## Get a table of junctions, table of donors etc.
bamfh = pysam.Samfile( bamfile, "rb" )
#JTAB,UNFILT_JTAB,STAB,NEWDTAB,MMES = parse_aligns_detailed(bamfh)
JTAB,UNFILT_JTAB = quickcov(bamfh,anchorsize)
bamfh.close()
myjuncs = JTAB.keys()
myjuncs.sort()
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Print junction list to the output directory
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
print "juncid\tunfilt_cov\tcov"
for juncid in myjuncs:
print juncid, UNFILT_JTAB[juncid], JTAB[juncid]
def main():
# Functions for jtools:
# Get sequence
# Detect tandem acceptors NAGNAG
# Annotate with genes
# Jiggle
# Bed to juncid
# Guess frame
# Find stops in intron + in frame
# SVM recomputes
# Splice site strength? ppt?
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Load a fasta file
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
global f
# Opening fasta filehandle
print >> sys.stderr, "[%s] Opening fasta file" % (spanki_utils.timestamp())
f = Fasta(fastafile)
fastachr = set(sorted(f.keys()))
#print fastachr
########################################################
### Parsing a juncbed file
########################################################
if (juncbedfile):
print "juncid\toriginal_id\tdastring"
print >> sys.stderr, "Loading", juncbedfile
lines = csv.reader(open(juncbedfile, 'rb'), delimiter='\t')
z = []
for line in lines:
pattern = re.compile('track')
track = pattern.search(line[0])
if not track:
values = line
blocksizes = values[10].split(",")
blockstarts = values[11].split(",")
chr = values[0]
rangestart = int(values[1]) - 1
rangeend = int(values[2])
strand = values[5]
id = values[3]
intronstart = rangestart + int(blocksizes[0]) + 2
intronend = rangeend - int(blocksizes[1])
# Or..
#intronend = rangestart + int(blocksizes[0]) + int(blockstarts[1])
#chrXHet 800 1767 JUNC00000001 2 + 800 1767 255,0,0 2 20,63 0,904
intronsize = intronend - intronstart
juncid = chr + ":" + str(intronstart) + "_" + str(
intronend) + ":" + strand
dastring = intron_sequence_single(juncid, f)
z.append(str(dastring))
print juncid, values[3], dastring
print >> sys.stderr, "Distribution of detected motifs:\n", Counter(z)
quit("Done")
########################################################
### Parsing a intronbed file
########################################################
#scaffold_12916 13833982 13834044 10
#scaffold_12916 13838614 13838676 67
#scaffold_12916 13839119 13839204 75
if (intronbedfile):
print "juncid\tid\tdastring"
lines = csv.reader(open(intronbedfile, 'rb'), delimiter='\t')
for line in lines:
pattern = re.compile('track')
track = pattern.search(line[0])
values = line
if not track:
chr = values[0]
intronstart = int(values[1]) + 1
intronend = int(values[2]) - 1
strand = "+"
id = values[0]
intronsize = intronend - intronstart
juncid = chr + ":" + str(intronstart) + "_" + str(
intronend) + ":" + strand
dastring = intron_sequence_single(juncid, f)
print juncid, values[3], dastring
quit("Done")
########################################################
########################################################
### Converting from another format
########################################################
if gfffile:
#reflist = tab_to_dict(gff)
results = collections.defaultdict(
lambda: collections.defaultdict(dict))
gffdict = gff_to_dict(gfffile)
for x in gffdict:
#print x
#print gffdict[x]
if (gffdict[x]['feature_type'] == "exon_junction"):
juncid = gffdict[x]['chr'] + ":" + str(
int(gffdict[x]['start']) +
1) + "_" + str(int(gffdict[x]['end']) -
1) + ":" + gffdict[x]['strand']
elif (gffdict[x]['feature_type'] == "intron"):
juncid = gffdict[x]['chr'] + ":" + gffdict[x][
'start'] + "_" + gffdict[x]['end'] + ":" + gffdict[x][
'strand']
dastring = intron_sequence_single(juncid, f)
#print dastring
results[x]['juncid'] = juncid
results[x]['dastring'] = dastring
print "ID\tjuncid\tdastring"
for x in sorted(results.iterkeys()):
print x, "\t", results[x]['juncid'], "\t", results[x]['dastring']
quit()
########################################################
### Converting from another format
########################################################
if gtffile:
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Intializing the reference
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# You need the gtf file, and the fasta file
lookup = spanki_utils.prep_ref(gtffile, fastafile, output_dir)
## Note that you now have a reference called ref.bam, and a lookup dict
#tmp_dir = output_dir + "/tmp/"
#reffile = tmp_dir + "/ref.bam"
reffile = "tmp/ref.bam"
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Load an annotation, flattened as bam
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
print >> sys.stderr, "[%s] Trying to load annotation as bam" % (
spanki_utils.timestamp())
reffh = pysam.Samfile(reffile, "rb")
edgedict, refjuncs = spanki_parse_utils.parseRefAsBam(reffh)
reffh.close()
print >> sys.stderr, "[%s] Done loading annotation as bam" % (
spanki_utils.timestamp())
for junc in refjuncs:
print junc
quit()
### Below are functions that operate on a junction list
########################################################
if jlist:
#~~~~~~~~~~~~~~~~~~~
# Load reference junction list
#~~~~~~~~~~~~~~~~~~~
reflist = tab_to_dict(jlist)
# Find the junctions in jlist that are not in jtab
myjuncs = reflist.keys()
print >> sys.stderr, len(myjuncs), "in junction list"
updonor = 20
downdonor = 2
upacceptor = 2
downacceptor = 20
for x in myjuncs:
print x
j1 = Junctionid(x)
j1.display()
if j1.strand == "+":
#print Seq(f[j1.chr][j1.donor-updonor:j1.donor], IUPAC.unambiguous_dna)
tempseq = Seq(f[j1.chr][j1.donor - updonor:j1.donor],
IUPAC.unambiguous_dna)
#print "***", tempseq.translate()
#print Seq(f[j1.chr][j1.donor:j1.donor + downdonor], IUPAC.unambiguous_dna)
#print Seq(f[j1.chr][j1.acceptor-upacceptor:j1.acceptor], IUPAC.unambiguous_dna)
#print Seq(f[j1.chr][j1.acceptor:j1.acceptor + downacceptor], IUPAC.unambiguous_dna)
nagstring = find_nag(
Seq(f[j1.chr][j1.acceptor:j1.acceptor + downacceptor],
IUPAC.unambiguous_dna))
print nagstring
elif j1.strand == "-":
pass
#print Seq(f[j1.chr][j1.donor:j1.donor + updonor], IUPAC.unambiguous_dna).reverse_complement()
#print Seq(f[j1.chr][j1.donor - downdonor:j1.donor], IUPAC.unambiguous_dna).reverse_complement()
#print Seq(f[j1.chr][j1.acceptor:j1.acceptor + upacceptor], IUPAC.unambiguous_dna).reverse_complement()
#print Seq(f[j1.chr][j1.acceptor-downacceptor:j1.acceptor], IUPAC.unambiguous_dna).reverse_complement()
else:
quit("Don't recognize strand")
#fiveprimeflank = fiveprimeflank.reverse_complement()
quit("Done")
quit()
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Older code that's not used yet
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# IRT
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
bamfh = pysam.Samfile(bamfile, "rb")
#for alignedread in samfile:
# Need some kind of iterator to getread length from first alignment in sam
print >> sys.stderr, "[%s] Getting intron read-though (IRT), may take awhile" % (
spanki_utils.timestamp())
IRT = intron_readthrough(myjuncs, bamfh)
bamfh.close()
print >> sys.stderr, "[%s] Done getting IRT" % (spanki_utils.timestamp())
#for edgeid in covbyedge.keys():
# print edgeid, covbyedge[edgeid]
# These are the fields you end up with after merging:
#juncid geneassign cov lirt rirt irt dncov ancov numsamps
#chr2L:22427471_22427525:- none 2 57 28 85 0 0 1
#chr2R:5702257_5702656:+ FBgn0040092 13 0 0 0 0 0 2
#chr2L:11436293_11436415:- FBgn0261648 23 0 0 0 0 0 2
#chr2R:9334834_9336812:- FBgn0013765 6 0 0 0 0 0 2
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Now compile the results
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# First show how you can get in hte myjuncs list
print >> sys.stderr, "Printing results table"
print >> juncs_out, "juncid\tgeneassign\tannostatus\tintron_size\tgmcode\tregcode\tcov\tlirt\trirt\tirt\tdncov\tancov"
for juncid in sorted(keys2):
try:
results = [
juncid, jdict[juncid]['geneassign'],
jdict[juncid]['annostatus'], jdict[juncid]['intron_size'],
jdict[juncid]['gmcode'], jdict[juncid]['regcode'],
jdict[juncid]['cov'], jdict[juncid]['lirt'],
jdict[juncid]['rirt'], jdict[juncid]['irt'],
jdict[juncid]['dncov'], jdict[juncid]['ancov']
]
print >> juncs_out, ('\t'.join(map(str, results)))
except KeyError:
#myjuncs.append(juncid)
j1 = Junctionid(juncid)
donid = j1.donid
accid = j1.accid
if covbyedge[donid]: dncov = covbyedge[donid]
else: dncov = 0
if covbyedge[accid]: ancov = covbyedge[accid]
else: ancov = 0
results = [
juncid, reflist[juncid]['geneassign'],
reflist[juncid]['annostatus'], reflist[juncid]['intron_size'],
reflist[juncid]['gmcode'], reflist[juncid]['regcode'], 0,
IRT[juncid]['lirt'], IRT[juncid]['rirt'], IRT[juncid]['irt'],
dncov, ancov
]
#print(results, sep='\t')
print >> juncs_out, ('\t'.join(map(str, results)))
quit("done")
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Parse the read alignments
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Parse the bam file
## Get a table of junctions, table of donors etc.
bamfh = pysam.Samfile(bamfile, "rb")
#JTAB,UNFILT_JTAB,STAB,NEWDTAB,MMES = parse_aligns_detailed(bamfh)
JTAB, UNFILT_JTAB = quickcov(bamfh, anchorsize)
bamfh.close()
myjuncs = JTAB.keys()
myjuncs.sort()
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Print junction list to the output directory
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
print "juncid\tunfilt_cov\tcov"
for juncid in myjuncs:
print juncid, UNFILT_JTAB[juncid], JTAB[juncid]
