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:
# Intersect 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:
# Intersect requires coordinates in BED format.
g2.convert_to_bed_coord = True
out_file = open( out_fname, "w" )
try:
for feature in intersect( [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, e:
out_file.close()
fail( "Invalid file format: %s" % str( e ) )
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():
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"))
