Source code for galaxy.datatypes.converters.interval_to_fli

'''
Creates a feature location index (FLI) for a given BED/GFF file.
FLI index has the form::

    [line_length]
    <symbol1_in_lowercase><tab><symbol1><tab><location>
    <symbol2_in_lowercase><tab><symbol2><tab><location>
    ...

where location is formatted as:

    contig:start-end

and symbols are sorted in lexigraphical order.
'''
import optparse

from bx.tabular.io import Comment, Header

from galaxy.datatypes.util.gff_util import convert_gff_coords_to_bed, GFFReaderWrapper, read_unordered_gtf


[docs]def main():
    # Process arguments.
    parser = optparse.OptionParser()
    parser.add_option('-F', '--format', dest="input_format")
    (options, args) = parser.parse_args()
    in_fname, out_fname = args
    input_format = options.input_format.lower()

    # Create dict of name-location pairings.
    name_loc_dict = {}
    if input_format in ['gff', 'gtf']:
        # GTF/GFF format

        # Create reader.
        if input_format == 'gff':
            in_reader = GFFReaderWrapper(open(in_fname, 'r'))
        else:  # input_format == 'gtf'
            in_reader = read_unordered_gtf(open(in_fname, 'r'))

        for feature in in_reader:
            if isinstance(feature, (Header, Comment)):
                continue

            for name in feature.attributes:
                val = feature.attributes[name]
                try:
                    float(val)
                    continue
                except ValueError:
                    convert_gff_coords_to_bed(feature)
                    # Value is not a number, so it can be indexed.
                    if val not in name_loc_dict:
                        # Value is not in dictionary.
                        name_loc_dict[val] = {
                            'contig': feature.chrom,
                            'start': feature.start,
                            'end': feature.end
                        }
                    else:
                        # Value already in dictionary, so update dictionary.
                        loc = name_loc_dict[val]
                        if feature.start < loc['start']:
                            loc['start'] = feature.start
                        if feature.end > loc['end']:
                            loc['end'] = feature.end
    elif input_format == 'bed':
        # BED format.
        for line in open(in_fname, 'r'):
            # Ignore track lines.
            if line.startswith("track"):
                continue

            fields = line.split()

            # Ignore lines with no feature name.
            if len(fields) < 4:
                continue

            # Process line
            name_loc_dict[fields[3]] = {
                'contig': fields[0],
                'start': int(fields[1]),
                'end': int(fields[2])
            }

    # Create sorted list of entries.
    max_len = 0
    entries = []
    for name in sorted(name_loc_dict.keys()):
        loc = name_loc_dict[name]
        entry = '%s\t%s\t%s' % (name.lower(), name, '%s:%i-%i' % (loc['contig'], loc['start'], loc['end']))
        if len(entry) > max_len:
            max_len = len(entry)
        entries.append(entry)

    # Write padded entries.
    with open(out_fname, 'w') as out:
        out.write(str(max_len + 1).ljust(max_len) + '\n')
        for entry in entries:
            out.write(entry.ljust(max_len) + '\n')


if __name__ == '__main__':
    main()