Cookbook

From gene sequence to predicted protein with the GFF module.

Contributed by Brad Chapman

Note: This requires the proposed Biopython GFF module, which has not yet been integrated in Biopython. You will need to install it first to run the examples

Problem

Starting with a GlimmerHMM output file in GFF3 format, produce a FASTA file of predicted protein sequences.

Solution

Setting this up, we import the required modules and parse our input FASTA file into a standard python dictionary, using SeqIO. SeqIO is also used for writing the output file. We pass the output function a python generator, which SeqIO will convert into FASTA files one at a time. This allows the script to be generalized to arbitrarily large data sets without running into memory issues.

from __future__ import with_statement
import sys
import os
import operator

from Bio import SeqIO
from Bio.SeqRecord import SeqRecord

from BCBio import GFF


def main(glimmer_file, ref_file):
    with open(ref_file) as in_handle:
        ref_recs = SeqIO.to_dict(SeqIO.parse(in_handle, "fasta"))

    base, ext = os.path.splitext(glimmer_file)
    out_file = "%s-proteins.fa" % base
    with open(out_file, "w") as out_handle:
        SeqIO.write(protein_recs(glimmer_file, ref_recs), out_handle, "fasta")

Next, we provide a function to parse the GlimmerHMM output. Thanks to the GFF library, this is very easy. We iterate over a group of lines at a time to handle very large output files, and provide as input the reference records we parsed earlier with SeqIO:

def glimmer_predictions(in_handle, ref_recs):
    """Parse Glimmer output, generating SeqRecord and SeqFeatures for predictions
    """
    for rec in GFF.parse(in_handle, target_lines=1000, base_dict=ref_recs):
        yield rec

This is the meat of the program. We loop over each of the records in the GlimmerHMM output, then over each of the gene features in that record. Each of these SeqFeatures contains a gene prediction with exons present as sub_features of the top level feature. Using the location of these exons, we extract the nucleotide sequence for the current gene and append it to a list. We then add together the list to get the full sequence. If the gene is on the reverse strand, the sequence is reverse complemented. Finally, we translate the sequence to protein. A SeqRecord with the sequence and prediction identifier is yielded, providing the generator we used earlier to write the output file:

def protein_recs(glimmer_file, ref_recs):
    """Generate protein records from GlimmerHMM gene predictions.
    """
    with open(glimmer_file) as in_handle:
        for rec in glimmer_predictions(in_handle, ref_recs):
            for feature in rec.features:
                seq_exons = []
                for cds in feature.sub_features:
                    seq_exons.append(
                        rec.seq[cds.location.nofuzzy_start : cds.location.nofuzzy_end]
                    )
                gene_seq = reduce(operator.add, seq_exons)
                if feature.strand == -1:
                    gene_seq = gene_seq.reverse_complement()
                protein_seq = gene_seq.translate()
                yield SeqRecord(protein_seq, feature.qualifiers["ID"][0], "", "")

Discussion

Compare the full scripts for:

This highlights how using standard libraries with standardized formats can save coding versus rolling your own. The GFF3 version is more general since it allows multiple contigs to be processed concurrently, and requires less custom code to solve the problem. This allows you to focus on the biological problem, and avoid bugs in the parsing code.

Example files to run the scripts are available thanks to Michael Kuhn: