def main():
sameformat = False
options, args = doc_optparse.parse( __doc__ )
try:
chr_col_1, start_col_1, end_col_1, strand_col_1 = parse_cols_arg( options.cols1 )
chr_col_2, start_col_2, end_col_2, strand_col_2 = parse_cols_arg( options.cols2 )
if options.sameformat:
sameformat = True
in_file_1, in_file_2, out_fname = args
except:
doc_optparse.exception()
g1 = NiceReaderWrapper( fileinput.FileInput( in_file_1 ),
chrom_col=chr_col_1,
start_col=start_col_1,
end_col=end_col_1,
strand_col=strand_col_1,
fix_strand=True )
g2 = NiceReaderWrapper( fileinput.FileInput( in_file_2 ),
chrom_col=chr_col_2,
start_col=start_col_2,
end_col=end_col_2,
strand_col=strand_col_2,
fix_strand=True )
out_file = open( out_fname, "w" )
try:
for line in concat( [g1, g2], sameformat=sameformat ):
if type( line ) is GenomicInterval:
out_file.write( "%s\n" % "\t".join( line.fields ) )
else:
out_file.write( "%s\n" % line )
except ParseError as exc:
out_file.close()
fail( "Invalid file format: %s" % str( exc ) )
out_file.close()
if g1.skipped > 0:
print(skipped( g1, filedesc=" of 1st dataset" ))
if g2.skipped > 0:
print(skipped( g2, filedesc=" of 2nd dataset" ))
def main():
options, args = doc_optparse.parse( __doc__ )
try:
chr_col_1, start_col_1, end_col_1, strand_col_1 = parse_cols_arg( options.cols1 )
chr_col_2, start_col_2, end_col_2, strand_col_2 = parse_cols_arg( options.cols2 )
in_fname, in2_fname, out_fname = args
except:
doc_optparse.exception()
g1 = NiceReaderWrapper( fileinput.FileInput( in_fname ),
chrom_col=chr_col_1,
start_col=start_col_1,
end_col=end_col_1,
strand_col=strand_col_1,
fix_strand=True )
g2 = NiceReaderWrapper( fileinput.FileInput( in2_fname ),
chrom_col=chr_col_2,
start_col=start_col_2,
end_col=end_col_2,
strand_col=strand_col_2,
fix_strand=True )
out_file = open( out_fname, "w" )
try:
for line in coverage( [g1, g2] ):
if type( line ) is GenomicInterval:
out_file.write( "%s\n" % "\t".join( line.fields ) )
else:
out_file.write( "%s\n" % line )
except ParseError as exc:
out_file.close()
fail( "Invalid file format: %s" % str( exc ) )
out_file.close()
if g1.skipped > 0:
print(skipped( g1, filedesc=" of 1st dataset" ))
if g2.skipped > 0:
print(skipped( g2, filedesc=" of 2nd dataset" ))
def main():
mincols = 1
options, args = doc_optparse.parse(__doc__)
try:
chr_col_1, start_col_1, end_col_1, strand_col_1 = parse_cols_arg(
options.cols1)
if options.mincols:
mincols = int(options.mincols)
in_fname, out_fname = args
except:
doc_optparse.exception()
g1 = NiceReaderWrapper(fileinput.FileInput(in_fname),
chrom_col=chr_col_1,
start_col=start_col_1,
end_col=end_col_1,
strand_col=strand_col_1,
fix_strand=True)
out_file = open(out_fname, "w")
try:
for line in merge(g1, mincols=mincols):
if options.threecol:
if type(line) is GenomicInterval:
out_file.write(
"%s\t%s\t%s\n" %
(line.chrom, str(line.startCol), str(line.endCol)))
elif type(line) is list:
out_file.write("%s\t%s\t%s\n" %
(line[chr_col_1], str(line[start_col_1]),
str(line[end_col_1])))
else:
out_file.write("%s\n" % line)
else:
if type(line) is GenomicInterval:
out_file.write("%s\n" % "\t".join(line.fields))
elif type(line) is list:
out_file.write("%s\n" % "\t".join(line))
else:
out_file.write("%s\n" % line)
except ParseError as exc:
out_file.close()
fail("Invalid file format: %s" % str(exc))
out_file.close()
if g1.skipped > 0:
print(skipped(g1, filedesc=" of 1st dataset"))
def main():
mincols = 1
options, args = doc_optparse.parse( __doc__ )
try:
chr_col_1, start_col_1, end_col_1, strand_col_1 = parse_cols_arg( options.cols1 )
if options.mincols:
mincols = int( options.mincols )
in_fname, out_fname = args
except:
doc_optparse.exception()
g1 = NiceReaderWrapper( fileinput.FileInput( in_fname ),
chrom_col=chr_col_1,
start_col=start_col_1,
end_col=end_col_1,
strand_col=strand_col_1,
fix_strand=True )
out_file = open( out_fname, "w" )
try:
for line in merge(g1, mincols=mincols):
if options.threecol:
if type( line ) is GenomicInterval:
out_file.write( "%s\t%s\t%s\n" % ( line.chrom, str( line.startCol ), str( line.endCol ) ) )
elif type( line ) is list:
out_file.write( "%s\t%s\t%s\n" % ( line[chr_col_1], str( line[start_col_1] ), str( line[end_col_1] ) ) )
else:
out_file.write( "%s\n" % line )
else:
if type( line ) is GenomicInterval:
out_file.write( "%s\n" % "\t".join( line.fields ) )
elif type( line ) is list:
out_file.write( "%s\n" % "\t".join( line ) )
else:
out_file.write( "%s\n" % line )
except ParseError as exc:
out_file.close()
fail( "Invalid file format: %s" % str( exc ) )
out_file.close()
if g1.skipped > 0:
print(skipped( g1, filedesc=" of 1st dataset" ))
def main():
options, args = doc_optparse.parse( __doc__ )
try:
chr_col_1, start_col_1, end_col_1, strand_col_1 = parse_cols_arg( options.cols1 )
in_fname, out_fname = args
except:
doc_optparse.exception()
g1 = NiceReaderWrapper( fileinput.FileInput( in_fname ),
chrom_col=chr_col_1,
start_col=start_col_1,
end_col=end_col_1,
strand_col=strand_col_1,
fix_strand=True )
try:
bases = base_coverage(g1)
except ParseError as exc:
fail( "Invalid file format: %s" % str( exc ) )
out_file = open( out_fname, "w" )
out_file.write( "%s\n" % str( bases ) )
out_file.close()
if g1.skipped > 0:
print(skipped( g1, filedesc="" ))
# should only execute this code once per line
fileline = fileLines[line].rstrip("\n\r")
try:
cluster_interval = GenomicInterval( g1, fileline.split("\t"),
g1.chrom_col,
g1.start_col,
g1.end_col,
g1.strand_col,
g1.default_strand,
g1.fix_strand )
except Exception, exc:
print >> sys.stderr, str( exc )
f1.close()
sys.exit()
interval_size = cluster_interval.end - cluster_interval.start
if outsize == -1 or \
( outsize > interval_size and output == 4 ) or \
( outsize < interval_size and output == 5 ):
outinterval = cluster_interval
outsize = interval_size
out_file.write( "%s\n" % outinterval )
f1.close()
out_file.close()
if g1.skipped > 0:
print skipped( g1, filedesc="" )
if __name__ == "__main__":
main()
def main():
options, args = doc_optparse.parse( __doc__ )
try:
chr_col_1, start_col_1, end_col_1, strand_col_1 = parse_cols_arg( options.cols1 )
chr_col_2, start_col_2, end_col_2, strand_col_2 = parse_cols_arg( options.cols2 )
in1_gff_format = bool( options.gff1 )
in2_gff_format = bool( options.gff2 )
in_fname, in2_fname, out_fname, direction = args
except:
doc_optparse.exception()
# Set readers to handle either GFF or default format.
if in1_gff_format:
in1_reader_wrapper = GFFIntervalToBEDReaderWrapper
else:
in1_reader_wrapper = NiceReaderWrapper
if in2_gff_format:
in2_reader_wrapper = GFFIntervalToBEDReaderWrapper
else:
in2_reader_wrapper = NiceReaderWrapper
g1 = in1_reader_wrapper( fileinput.FileInput( in_fname ),
chrom_col=chr_col_1,
start_col=start_col_1,
end_col=end_col_1,
strand_col=strand_col_1,
fix_strand=True )
g2 = in2_reader_wrapper( fileinput.FileInput( in2_fname ),
chrom_col=chr_col_2,
start_col=start_col_2,
end_col=end_col_2,
strand_col=strand_col_2,
fix_strand=True )
# Find flanking features.
out_file = open( out_fname, "w" )
try:
for result in proximal_region_finder([g1, g2], direction):
if type( result ) is list:
line, closest_feature = result
# Need to join outputs differently depending on file types.
if in1_gff_format:
# Output is GFF with added attribute 'closest feature.'
# Invervals are in BED coordinates; need to convert to GFF.
line = convert_bed_coords_to_gff( line )
closest_feature = convert_bed_coords_to_gff( closest_feature )
# Replace double quotes with single quotes in closest feature's attributes.
out_file.write( "%s closest_feature \"%s\" \n" %
( "\t".join( line.fields ),
"\t".join( closest_feature.fields ).replace( "\"", "\\\"" )
) )
else:
# Output is BED + closest feature fields.
output_line_fields = []
output_line_fields.extend( line.fields )
output_line_fields.extend( closest_feature.fields )
out_file.write( "%s\n" % ( "\t".join( output_line_fields ) ) )
else:
out_file.write( "%s\n" % result )
except ParseError as exc:
fail( "Invalid file format: %s" % str( exc ) )
print("Direction: %s" % (direction))
if g1.skipped > 0:
print(skipped( g1, filedesc=" of 1st dataset" ))
if g2.skipped > 0:
print(skipped( g2, filedesc=" of 2nd dataset" ))
def main():
mincols = 1
options, args = doc_optparse.parse(__doc__)
try:
chr_col_1, start_col_1, end_col_1, strand_col_1 = parse_cols_arg(
options.cols1)
chr_col_2, start_col_2, end_col_2, strand_col_2 = parse_cols_arg(
options.cols2)
if options.mincols:
mincols = int(options.mincols)
pieces = bool(options.pieces)
in1_gff_format = bool(options.gff1)
in2_gff_format = bool(options.gff2)
in_fname, in2_fname, out_fname = args
except:
doc_optparse.exception()
# Set readers to handle either GFF or default format.
if in1_gff_format:
in1_reader_wrapper = GFFReaderWrapper
else:
in1_reader_wrapper = NiceReaderWrapper
if in2_gff_format:
in2_reader_wrapper = GFFReaderWrapper
else:
in2_reader_wrapper = NiceReaderWrapper
g1 = in1_reader_wrapper(fileinput.FileInput(in_fname),
chrom_col=chr_col_1,
start_col=start_col_1,
end_col=end_col_1,
strand_col=strand_col_1,
fix_strand=True)
if in1_gff_format:
# Subtract requires coordinates in BED format.
g1.convert_to_bed_coord = True
g2 = in2_reader_wrapper(fileinput.FileInput(in2_fname),
chrom_col=chr_col_2,
start_col=start_col_2,
end_col=end_col_2,
strand_col=strand_col_2,
fix_strand=True)
if in2_gff_format:
# Subtract requires coordinates in BED format.
g2.convert_to_bed_coord = True
out_file = open(out_fname, "w")
try:
for feature in subtract([g1, g2], pieces=pieces, mincols=mincols):
if isinstance(feature, GFFFeature):
# Convert back to GFF coordinates since reader converted automatically.
convert_bed_coords_to_gff(feature)
for interval in feature.intervals:
out_file.write("%s\n" % "\t".join(interval.fields))
elif isinstance(feature, GenomicInterval):
out_file.write("%s\n" % "\t".join(feature.fields))
else:
out_file.write("%s\n" % feature)
except ParseError as exc:
out_file.close()
fail("Invalid file format: %s" % str(exc))
out_file.close()
if g1.skipped > 0:
print(skipped(g1, filedesc=" of 2nd dataset"))
if g2.skipped > 0:
print(skipped(g2, filedesc=" of 1st dataset"))
def main():
options, args = doc_optparse.parse(__doc__)
try:
chr_col_1, start_col_1, end_col_1, strand_col_1 = parse_cols_arg(
options.cols1)
chr_col_2, start_col_2, end_col_2, strand_col_2 = parse_cols_arg(
options.cols2)
in1_gff_format = bool(options.gff1)
in2_gff_format = bool(options.gff2)
in_fname, in2_fname, out_fname, direction = args
except:
doc_optparse.exception()
# Set readers to handle either GFF or default format.
if in1_gff_format:
in1_reader_wrapper = GFFIntervalToBEDReaderWrapper
else:
in1_reader_wrapper = NiceReaderWrapper
if in2_gff_format:
in2_reader_wrapper = GFFIntervalToBEDReaderWrapper
else:
in2_reader_wrapper = NiceReaderWrapper
g1 = in1_reader_wrapper(fileinput.FileInput(in_fname),
chrom_col=chr_col_1,
start_col=start_col_1,
end_col=end_col_1,
strand_col=strand_col_1,
fix_strand=True)
g2 = in2_reader_wrapper(fileinput.FileInput(in2_fname),
chrom_col=chr_col_2,
start_col=start_col_2,
end_col=end_col_2,
strand_col=strand_col_2,
fix_strand=True)
# Find flanking features.
out_file = open(out_fname, "w")
try:
for result in proximal_region_finder([g1, g2], direction):
if type(result) is list:
line, closest_feature = result
# Need to join outputs differently depending on file types.
if in1_gff_format:
# Output is GFF with added attribute 'closest feature.'
# Invervals are in BED coordinates; need to convert to GFF.
line = convert_bed_coords_to_gff(line)
closest_feature = convert_bed_coords_to_gff(
closest_feature)
# Replace double quotes with single quotes in closest feature's attributes.
out_file.write(
"%s closest_feature \"%s\" \n" %
("\t".join(line.fields), "\t".join(
closest_feature.fields).replace("\"", "\\\"")))
else:
# Output is BED + closest feature fields.
output_line_fields = []
output_line_fields.extend(line.fields)
output_line_fields.extend(closest_feature.fields)
out_file.write("%s\n" % ("\t".join(output_line_fields)))
else:
out_file.write("%s\n" % result)
except ParseError as exc:
fail("Invalid file format: %s" % str(exc))
print("Direction: %s" % (direction))
if g1.skipped > 0:
print(skipped(g1, filedesc=" of 1st dataset"))
if g2.skipped > 0:
print(skipped(g2, filedesc=" of 2nd dataset"))
def main():
mincols = 1
leftfill = False
rightfill = False
options, args = doc_optparse.parse(__doc__)
try:
chr_col_1, start_col_1, end_col_1, strand_col_1 = parse_cols_arg(
options.cols1)
chr_col_2, start_col_2, end_col_2, strand_col_2 = parse_cols_arg(
options.cols2)
if options.mincols:
mincols = int(options.mincols)
if options.fill:
if options.fill == "both":
rightfill = leftfill = True
else:
rightfill = options.fill == "right"
leftfill = options.fill == "left"
in_fname, in2_fname, out_fname = args
except:
doc_optparse.exception()
g1 = NiceReaderWrapper(fileinput.FileInput(in_fname),
chrom_col=chr_col_1,
start_col=start_col_1,
end_col=end_col_1,
strand_col=strand_col_1,
fix_strand=True)
g2 = NiceReaderWrapper(fileinput.FileInput(in2_fname),
chrom_col=chr_col_2,
start_col=start_col_2,
end_col=end_col_2,
strand_col=strand_col_2,
fix_strand=True)
out_file = open(out_fname, "w")
try:
for outfields in join(g1,
g2,
mincols=mincols,
rightfill=rightfill,
leftfill=leftfill):
if type(outfields) is list:
out_file.write("%s\n" % "\t".join(outfields))
else:
out_file.write("%s\n" % outfields)
except ParseError as exc:
out_file.close()
fail("Invalid file format: %s" % str(exc))
except MemoryError:
out_file.close()
fail("Input datasets were too large to complete the join operation.")
out_file.close()
if g1.skipped > 0:
print(skipped(g1, filedesc=" of 1st dataset"))
if g2.skipped > 0:
print(skipped(g2, filedesc=" of 2nd dataset"))
def main():
allchroms = False
options, args = doc_optparse.parse( __doc__ )
try:
chr_col_1, start_col_1, end_col_1, strand_col_1 = parse_cols_arg( options.cols1 )
lengths = options.lengths
if options.all:
allchroms = True
in_fname, out_fname = args
except:
doc_optparse.exception()
g1 = NiceReaderWrapper( fileinput.FileInput( in_fname ),
chrom_col=chr_col_1,
start_col=start_col_1,
end_col=end_col_1,
strand_col=strand_col_1,
fix_strand=True )
lens = dict()
chroms = list()
# dbfile is used to determine the length of each chromosome. The lengths
# are added to the lens dict and passed copmlement operation code in bx.
dbfile = fileinput.FileInput( lengths )
if dbfile:
if not allchroms:
try:
for line in dbfile:
fields = line.split("\t")
lens[fields[0]] = int(fields[1])
except:
# assume LEN doesn't exist or is corrupt somehow
pass
elif allchroms:
try:
for line in dbfile:
fields = line.split("\t")
end = int(fields[1])
chroms.append("\t".join([fields[0], "0", str(end)]))
except:
pass
# Safety...if the dbfile didn't exist and we're on allchroms, then
# default to generic complement
if allchroms and len(chroms) == 0:
allchroms = False
if allchroms:
chromReader = GenomicIntervalReader(chroms)
generator = subtract([chromReader, g1])
else:
generator = complement(g1, lens)
out_file = open( out_fname, "w" )
try:
for interval in generator:
if type( interval ) is GenomicInterval:
out_file.write( "%s\n" % "\t".join( interval ) )
else:
out_file.write( "%s\n" % interval )
except ParseError as exc:
out_file.close()
fail( "Invalid file format: %s" % str( exc ) )
out_file.close()
if g1.skipped > 0:
print(skipped( g1, filedesc="" ))
def main():
distance = 0
minregions = 2
output = 1
options, args = doc_optparse.parse(__doc__)
try:
chr_col_1, start_col_1, end_col_1, strand_col_1 = parse_cols_arg(
options.cols1)
if options.distance:
distance = int(options.distance)
if options.overlap:
distance = -1 * int(options.overlap)
if options.output:
output = int(options.output)
if options.minregions:
minregions = int(options.minregions)
in_fname, out_fname = args
except:
doc_optparse.exception()
g1 = NiceReaderWrapper(fileinput.FileInput(in_fname),
chrom_col=chr_col_1,
start_col=start_col_1,
end_col=end_col_1,
strand_col=strand_col_1,
fix_strand=True)
# Get the cluster tree
try:
clusters, extra = find_clusters(g1,
mincols=distance,
minregions=minregions)
except ParseError as exc:
fail("Invalid file format: %s" % str(exc))
f1 = open(in_fname, "r")
out_file = open(out_fname, "w")
# If "merge"
if output == 1:
fields = [
"."
for x in range(max(g1.chrom_col, g1.start_col, g1.end_col) + 1)
]
for chrom, tree in clusters.items():
for start, end, lines in tree.getregions():
fields[g1.chrom_col] = chrom
fields[g1.start_col] = str(start)
fields[g1.end_col] = str(end)
out_file.write("%s\n" % "\t".join(fields))
# If "filtered" we preserve order of file and comments, etc.
if output == 2:
linenums = dict()
for chrom, tree in clusters.items():
for linenum in tree.getlines():
linenums[linenum] = 0
linenum = -1
f1.seek(0)
for line in f1.readlines():
linenum += 1
if linenum in linenums or linenum in extra:
out_file.write("%s\n" % line.rstrip("\n\r"))
# If "clustered" we output original intervals, but near each other (i.e. clustered)
if output == 3:
linenums = list()
f1.seek(0)
fileLines = f1.readlines()
for chrom, tree in clusters.items():
for linenum in tree.getlines():
out_file.write("%s\n" % fileLines[linenum].rstrip("\n\r"))
# If "minimum" we output the smallest interval in each cluster
if output == 4 or output == 5:
linenums = list()
f1.seek(0)
fileLines = f1.readlines()
for chrom, tree in clusters.items():
for start, end, lines in tree.getregions():
outsize = -1
outinterval = None
for line in lines:
# three nested for loops?
# should only execute this code once per line
fileline = fileLines[line].rstrip("\n\r")
try:
cluster_interval = GenomicInterval(
g1, fileline.split("\t"), g1.chrom_col,
g1.start_col, g1.end_col, g1.strand_col,
g1.default_strand, g1.fix_strand)
except Exception as exc:
print(str(exc), file=sys.stderr)
f1.close()
sys.exit()
interval_size = cluster_interval.end - cluster_interval.start
if outsize == -1 or \
( outsize > interval_size and output == 4 ) or \
( outsize < interval_size and output == 5 ):
outinterval = cluster_interval
outsize = interval_size
out_file.write("%s\n" % outinterval)
f1.close()
out_file.close()
if g1.skipped > 0:
print(skipped(g1, filedesc=""))
class CreateCaseControlTrack(GeneralGuiTool):
@staticmethod
def getToolName():
'''
Specifies a header of the tool, which is displayed at the top of the
page.
'''
return "Combine two BED files into single case-control track"
@staticmethod
def getInputBoxNames():
'''
Specifies a list of headers for the input boxes, and implicitly also the
number of input boxes to display on the page. The returned list can have
two syntaxes:
1) A list of strings denoting the headers for the input boxes in
numerical order.
2) A list of tuples of strings, where each tuple has
two items: a header and a key.
The contents of each input box must be defined by the function
getOptionsBoxK, where K is either a number in the range of 1 to the
number of boxes (case 1), or the specified key (case 2).
'''
return [('Select genome build: ', 'genome'), \
('Select track to be used as case: ', 'case'), \
('Select track to be used as control: ', 'control'), \
('Shared regions should be: ', 'shared')] #Alternatively: [ ('box1','key1'), ('box2','key2') ]
#@staticmethod
#def getInputBoxOrder():
# '''
# Specifies the order in which the input boxes should be displayed, as a
# list. The input boxes are specified by index (starting with 1) or by
# key. If None, the order of the input boxes is in the order specified by
# getInputBoxNames.
# '''
# return None
@staticmethod
def getOptionsBoxGenome(): # Alternatively: getOptionsBoxKey1()
'''
Defines the type and contents of the input box. User selections are
returned to the tools in the prevChoices and choices attributes to other
methods. These are lists of results, one for each input box (in the
order specified by getInputBoxOrder()).
The input box is defined according to the following syntax:
Selection box: ['choice1','choice2']
- Returns: string
Text area: 'textbox' | ('textbox',1) | ('textbox',1,False)
- Tuple syntax: (contents, height (#lines) = 1, read only flag = False)
- Returns: string
Password field: '__password__'
- Returns: string
Genome selection box: '__genome__'
- Returns: string
Track selection box: '__track__'
- Requires genome selection box.
- Returns: colon-separated string denoting track name
History selection box: ('__history__',) | ('__history__', 'bed', 'wig')
- Only history items of specified types are shown.
- Returns: colon-separated string denoting galaxy track name, as
specified in ExternalTrackManager.py.
History check box list: ('__multihistory__', ) | ('__multihistory__', 'bed', 'wig')
- Only history items of specified types are shown.
- Returns: OrderedDict with galaxy id as key and galaxy track name
as value if checked, else None.
Hidden field: ('__hidden__', 'Hidden value')
- Returns: string
Table: [['header1','header2'], ['cell1_1','cell1_2'], ['cell2_1','cell2_2']]
- Returns: None
Check box list: OrderedDict([('key1', True), ('key2', False), ('key3', False)])
- Returns: OrderedDict from key to selection status (bool).
'''
return '__genome__'
@staticmethod
def getOptionsBoxCase(prevChoices): # Alternatively: getOptionsBoxKey2()
'''
See getOptionsBox1().
prevChoices is a namedtuple of selections made by the user in the
previous input boxes (that is, a namedtuple containing only one element
in this case). The elements can accessed either by index, e.g.
prevChoices[0] for the result of input box 1, or by key, e.g.
prevChoices.key (case 2).
'''
return '__history__', 'bed', 'point.bed', 'category.bed', 'valued.bed'
@staticmethod
def getOptionsBoxControl(
prevChoices): # Alternatively: getOptionsBoxKey2()
'''
See getOptionsBox1().
prevChoices is a namedtuple of selections made by the user in the
previous input boxes (that is, a namedtuple containing only one element
in this case). The elements can accessed either by index, e.g.
prevChoices[0] for the result of input box 1, or by key, e.g.
prevChoices.key (case 2).
'''
return '__history__', 'bed', 'point.bed', 'category.bed', 'valued.bed'
@staticmethod
def getOptionsBoxShared(prevChoices):
return [
'removed', 'returned as case regions',
'returned as control regions',
'returned as they are (possibly overlapping)'
]
#@staticmethod
#def getOptionsBox4(prevChoices):
# return ['']
#@staticmethod
#def getDemoSelections():
# return ['testChoice1','..']
@classmethod
def subtract_files(cls, fn1, fn2, out_fn):
g1 = NiceReaderWrapper(fileinput.FileInput(fn1), fix_strand=True)
g2 = NiceReaderWrapper(fileinput.FileInput(fn2), fix_strand=True)
out_file = open(out_fn, "w")
try:
for feature in subtract([g1, g2], pieces=True, mincols=1):
out_file.write("%s\n" % feature)
except ParseError, exc:
out_file.close()
fail("Invalid file format: %s" % str(exc))
out_file.close()
if g1.skipped > 0:
print skipped(g1, filedesc=" of 2nd dataset")
if g2.skipped > 0:
print skipped(g2, filedesc=" of 1st dataset")
def main():
mincols = 1
options, args = doc_optparse.parse( __doc__ )
try:
chr_col_1, start_col_1, end_col_1, strand_col_1 = parse_cols_arg( options.cols1 )
chr_col_2, start_col_2, end_col_2, strand_col_2 = parse_cols_arg( options.cols2 )
if options.mincols:
mincols = int( options.mincols )
pieces = bool( options.pieces )
in1_gff_format = bool( options.gff1 )
in2_gff_format = bool( options.gff2 )
in_fname, in2_fname, out_fname = args
except:
doc_optparse.exception()
# Set readers to handle either GFF or default format.
if in1_gff_format:
in1_reader_wrapper = GFFReaderWrapper
else:
in1_reader_wrapper = NiceReaderWrapper
if in2_gff_format:
in2_reader_wrapper = GFFReaderWrapper
else:
in2_reader_wrapper = NiceReaderWrapper
g1 = in1_reader_wrapper( fileinput.FileInput( in_fname ),
chrom_col=chr_col_1,
start_col=start_col_1,
end_col=end_col_1,
strand_col=strand_col_1,
fix_strand=True )
if in1_gff_format:
# Subtract requires coordinates in BED format.
g1.convert_to_bed_coord = True
g2 = in2_reader_wrapper( fileinput.FileInput( in2_fname ),
chrom_col=chr_col_2,
start_col=start_col_2,
end_col=end_col_2,
strand_col=strand_col_2,
fix_strand=True )
if in2_gff_format:
# Subtract requires coordinates in BED format.
g2.convert_to_bed_coord = True
out_file = open( out_fname, "w" )
try:
for feature in subtract( [g1, g2], pieces=pieces, mincols=mincols ):
if isinstance( feature, GFFFeature ):
# Convert back to GFF coordinates since reader converted automatically.
convert_bed_coords_to_gff( feature )
for interval in feature.intervals:
out_file.write( "%s\n" % "\t".join( interval.fields ) )
elif isinstance( feature, GenomicInterval ):
out_file.write( "%s\n" % "\t".join( feature.fields ) )
else:
out_file.write( "%s\n" % feature )
except ParseError as exc:
out_file.close()
fail( "Invalid file format: %s" % str( exc ) )
out_file.close()
if g1.skipped > 0:
print(skipped( g1, filedesc=" of 2nd dataset" ))
if g2.skipped > 0:
print(skipped( g2, filedesc=" of 1st dataset" ))
chrom_col=chr_col_2,
start_col=start_col_2,
end_col=end_col_2,
strand_col=strand_col_2,
fix_strand=True )
out_file = open( out_fname, "w" )
try:
for outfields in join(g1, g2, mincols=mincols, rightfill=rightfill, leftfill=leftfill):
if type( outfields ) is list:
out_file.write( "%s\n" % "\t".join( outfields ) )
else:
out_file.write( "%s\n" % outfields )
except ParseError, exc:
out_file.close()
fail( "Invalid file format: %s" % str( exc ) )
except MemoryError:
out_file.close()
fail( "Input datasets were too large to complete the join operation." )
out_file.close()
if g1.skipped > 0:
print skipped( g1, filedesc=" of 1st dataset" )
if g2.skipped > 0:
print skipped( g2, filedesc=" of 2nd dataset" )
if __name__ == "__main__":
main()
for line in lines:
# three nested for loops?
# should only execute this code once per line
fileline = fileLines[line].rstrip("\n\r")
try:
cluster_interval = GenomicInterval(
g1, fileline.split("\t"), g1.chrom_col,
g1.start_col, g1.end_col, g1.strand_col,
g1.default_strand, g1.fix_strand)
except Exception, exc:
print >> sys.stderr, str(exc)
f1.close()
sys.exit()
interval_size = cluster_interval.end - cluster_interval.start
if outsize == -1 or \
( outsize > interval_size and output == 4 ) or \
( outsize < interval_size and output == 5 ):
outinterval = cluster_interval
outsize = interval_size
out_file.write("%s\n" % outinterval)
f1.close()
out_file.close()
if g1.skipped > 0:
print skipped(g1, filedesc="")
if __name__ == "__main__":
main()
def main():
distance = 0
minregions = 2
output = 1
options, args = doc_optparse.parse( __doc__ )
try:
chr_col_1, start_col_1, end_col_1, strand_col_1 = parse_cols_arg( options.cols1 )
if options.distance:
distance = int( options.distance )
if options.overlap:
distance = -1 * int( options.overlap )
if options.output:
output = int( options.output )
if options.minregions:
minregions = int( options.minregions )
in_fname, out_fname = args
except:
doc_optparse.exception()
g1 = NiceReaderWrapper( fileinput.FileInput( in_fname ),
chrom_col=chr_col_1,
start_col=start_col_1,
end_col=end_col_1,
strand_col=strand_col_1,
fix_strand=True )
# Get the cluster tree
try:
clusters, extra = find_clusters( g1, mincols=distance, minregions=minregions)
except ParseError as exc:
fail( "Invalid file format: %s" % str( exc ) )
f1 = open( in_fname, "r" )
out_file = open( out_fname, "w" )
# If "merge"
if output == 1:
fields = ["." for x in range(max(g1.chrom_col, g1.start_col, g1.end_col) + 1)]
for chrom, tree in clusters.items():
for start, end, lines in tree.getregions():
fields[g1.chrom_col] = chrom
fields[g1.start_col] = str(start)
fields[g1.end_col] = str(end)
out_file.write( "%s\n" % "\t".join( fields ) )
# If "filtered" we preserve order of file and comments, etc.
if output == 2:
linenums = dict()
for chrom, tree in clusters.items():
for linenum in tree.getlines():
linenums[linenum] = 0
linenum = -1
f1.seek(0)
for line in f1.readlines():
linenum += 1
if linenum in linenums or linenum in extra:
out_file.write( "%s\n" % line.rstrip( "\n\r" ) )
# If "clustered" we output original intervals, but near each other (i.e. clustered)
if output == 3:
linenums = list()
f1.seek(0)
fileLines = f1.readlines()
for chrom, tree in clusters.items():
for linenum in tree.getlines():
out_file.write( "%s\n" % fileLines[linenum].rstrip( "\n\r" ) )
# If "minimum" we output the smallest interval in each cluster
if output == 4 or output == 5:
linenums = list()
f1.seek(0)
fileLines = f1.readlines()
for chrom, tree in clusters.items():
for start, end, lines in tree.getregions():
outsize = -1
outinterval = None
for line in lines:
# three nested for loops?
# should only execute this code once per line
fileline = fileLines[line].rstrip("\n\r")
try:
cluster_interval = GenomicInterval( g1, fileline.split("\t"),
g1.chrom_col,
g1.start_col,
g1.end_col,
g1.strand_col,
g1.default_strand,
g1.fix_strand )
except Exception as exc:
print(str( exc ), file=sys.stderr)
f1.close()
sys.exit()
interval_size = cluster_interval.end - cluster_interval.start
if outsize == -1 or \
( outsize > interval_size and output == 4 ) or \
( outsize < interval_size and output == 5 ):
outinterval = cluster_interval
outsize = interval_size
out_file.write( "%s\n" % outinterval )
f1.close()
out_file.close()
if g1.skipped > 0:
print(skipped( g1, filedesc="" ))
if options.threecol:
if type(line) is GenomicInterval:
out_file.write(
"%s\t%s\t%s\n" %
(line.chrom, str(line.startCol), str(line.endCol)))
elif type(line) is list:
out_file.write("%s\t%s\t%s\n" %
(line[chr_col_1], str(line[start_col_1]),
str(line[end_col_1])))
else:
out_file.write("%s\n" % line)
else:
if type(line) is GenomicInterval:
out_file.write("%s\n" % "\t".join(line.fields))
elif type(line) is list:
out_file.write("%s\n" % "\t".join(line))
else:
out_file.write("%s\n" % line)
except ParseError, exc:
out_file.close()
fail("Invalid file format: %s" % str(exc))
out_file.close()
if g1.skipped > 0:
print skipped(g1, filedesc=" of 1st dataset")
if __name__ == "__main__":
main()
def main():
allchroms = False
options, args = doc_optparse.parse(__doc__)
try:
chr_col_1, start_col_1, end_col_1, strand_col_1 = parse_cols_arg(
options.cols1)
lengths = options.lengths
if options.all:
allchroms = True
in_fname, out_fname = args
except:
doc_optparse.exception()
g1 = NiceReaderWrapper(fileinput.FileInput(in_fname),
chrom_col=chr_col_1,
start_col=start_col_1,
end_col=end_col_1,
strand_col=strand_col_1,
fix_strand=True)
lens = dict()
chroms = list()
# dbfile is used to determine the length of each chromosome. The lengths
# are added to the lens dict and passed copmlement operation code in bx.
dbfile = fileinput.FileInput(lengths)
if dbfile:
if not allchroms:
try:
for line in dbfile:
fields = line.split("\t")
lens[fields[0]] = int(fields[1])
except:
# assume LEN doesn't exist or is corrupt somehow
pass
elif allchroms:
try:
for line in dbfile:
fields = line.split("\t")
end = int(fields[1])
chroms.append("\t".join([fields[0], "0", str(end)]))
except:
pass
# Safety...if the dbfile didn't exist and we're on allchroms, then
# default to generic complement
if allchroms and len(chroms) == 0:
allchroms = False
if allchroms:
chromReader = GenomicIntervalReader(chroms)
generator = subtract([chromReader, g1])
else:
generator = complement(g1, lens)
out_file = open(out_fname, "w")
try:
for interval in generator:
if type(interval) is GenomicInterval:
out_file.write("%s\n" % "\t".join(interval))
else:
out_file.write("%s\n" % interval)
except ParseError as exc:
out_file.close()
fail("Invalid file format: %s" % str(exc))
out_file.close()
if g1.skipped > 0:
print(skipped(g1, filedesc=""))
