annotate glimmerHMM/glimmerhmm_tabular_to_sequence.py @ 0:c9699375fcf6 draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/glimmer_hmm commit 0dc67759bcbdf5a8a285ded9ba751340d741fe63

#!/usr/bin/env python
"""Convert GlimmerHMM gene predictions into protein sequences.

This works with the GlimmerHMM specific output format:

  12    1  +  Initial       10748      10762       15
  12    2  +  Internal      10940      10971       32
  12    3  +  Internal      11035      11039        5
  12    4  +  Internal      11072      11110       39
  12    5  +  Internal      11146      11221       76
  12    6  +  Terminal      11265      11388      124

http://www.cbcb.umd.edu/software/GlimmerHMM/

Modified version of the converter from Brad Chapman: https://github.com/chapmanb/bcbb/blob/master/biopython/glimmer_to_proteins.py

Usage:
    glimmer_to_proteins.py <glimmer output> <ref fasta> <output file> <convert to protein ... False|True>
"""

import sys
import os
import operator

from Bio import SeqIO
from Bio.SeqRecord import SeqRecord

def main(glimmer_file, ref_file, out_file, to_protein):
    with open(ref_file) as in_handle:
        ref_rec = SeqIO.read(in_handle, "fasta")

    base, ext = os.path.splitext(glimmer_file)

    with open(out_file, "w") as out_handle:
        SeqIO.write(protein_recs(glimmer_file, ref_rec, to_protein), out_handle, "fasta")

def protein_recs(glimmer_file, ref_rec, to_protein):
    """Generate protein records
    """
    with open(glimmer_file) as in_handle:
        for gene_num, exons, strand in glimmer_predictions(in_handle):
            seq_exons = []
            for start, end in exons:
                seq_exons.append(ref_rec.seq[start:end])
            gene_seq = reduce(operator.add, seq_exons)
            if strand == '-':
                gene_seq = gene_seq.reverse_complement()
            if to_protein:
                yield SeqRecord(gene_seq.translate(), gene_num, "", "")
            else:
                yield SeqRecord(gene_seq, gene_num, "", "")

def glimmer_predictions(in_handle):
    """Parse Glimmer output, generating a exons and strand for each prediction.
    """
    # read the header
    while 1:
        line = in_handle.readline()
        if line.startswith("   #    #"):
            break
    in_handle.readline()
    # read gene predictions one at a time
    cur_exons, cur_gene_num, cur_strand = ([], None, None)
    while 1:
        line = in_handle.readline()
        if not line:
            break
        parts = line.strip().split()
        # new exon
        if len(parts) == 0:
            yield cur_gene_num, cur_exons, cur_strand
            cur_exons, cur_gene_num, cur_strand = ([], None, None)
        else:
            this_gene_num = parts[0]
            this_strand = parts[2]
            this_start = int(parts[4]) - 1 # 1 based
            this_end = int(parts[5])
            if cur_gene_num is None:
                cur_gene_num = this_gene_num
                cur_strand = this_strand
            else:
                assert cur_gene_num == this_gene_num
                assert cur_strand == this_strand
            cur_exons.append((this_start, this_end))
    if len(cur_exons) > 0:
        yield cur_gene_num, cur_exons, cur_strand

if __name__ == "__main__":
    if len(sys.argv) != 5:
        print __doc__
        sys.exit()
    main(*sys.argv[1:])