GFF Parsing

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GFF Parsing

Note: GFF parsing is not yet integrated into Biopython. This documentation is work towards making it ready for inclusion. You can retrieve the current version of the GFF parser from: http://github.com/chapmanb/bcbb/tree/master/gff. Comments are very welcome.

Generic Feature Format (GFF) is a biological sequence file format for representing features and annotations on sequences. It is a tab delimited format, making it accessible to biologists and editable in text editors and spreadsheet programs. It is also well defined and can be parsed via automated programs. GFF files are available from many of the large sequencing and annotation centers. The specification provides full details on the format and its uses.

Biopython provides a full featured GFF parser which will handle several versions of GFF: GFF3, GFF2, and GTF. It supports writing GFF3, the latest version.

GFF parsing differs from parsing other file formats like GenBank or PDB in that it is not record oriented. In a GenBank file, sequences are broken into discrete parts which can be parsed as a whole. In contrast, GFF is a line oriented format with support for nesting features. GFF is also commonly used to store only biological features, and not the primary sequence.

These differences have some consequences in how you will deal with GFF:

  • Files are first examined to determine available annotations and define items of interest.
  • Sequences will often be parsed separately, from an associated FASTA file.
  • Parsing needs to consider available memory, which can be quickly used up on files with many annotations. This problem can be solved via two methods:
    • Limiting parsing to features of interest.
    • Iterating over portions of the file.

The documentation below provides a practical guide to examining, parsing and writing GFF files in Python.

Examining your GFF file

Since GFF is a very general format, it is extremely useful to start by getting a sense of the type of data in the file and how it is structured. GFFExaminer provides an interface to examine and query the file. To examine relationships between features, examine a dictionary mapping parent to child features:

import pprint
from BCBio.GFF import GFFExaminer
 
in_file = "your_file.gff"
examiner = GFFExaminer()
in_handle = open(in_file)
pprint.pprint(examiner.parent_child_map(in_handle))
in_handle.close()

This file contains a flexible three level description of coding sequences: genes have mRNA trascripts; those mRNA transcripts each contain common features of coding sequence, the CDS itself, exon, intron and 5' and 3' untranslated regions. This is a common GFF structure allowing representation of multiple transcripts:

{('Coding_transcript', 'gene'): [('Coding_transcript', 'mRNA')],
 ('Coding_transcript', 'mRNA'): [('Coding_transcript', 'CDS'),
                                 ('Coding_transcript', 'exon'),
                                 ('Coding_transcript', 'five_prime_UTR'),
                                 ('Coding_transcript', 'intron'),
                                 ('Coding_transcript', 'three_prime_UTR')]}

Another item of interest for designing your parse strategy is understanding the various tags used to label the features. These consist of:

  • gff_id -- The record identifier being described. This will often refer to a chromosome or other scaffold sequence.
  • gff_source -- The source description from the second column of the GFF file, which specifies how a feature was generated.
  • gff_type -- The type of the feature, pulled from the 3rd column of the GFF file.
  • gff_source_type -- All combinations of sources and types in the file.

The available_limits function in the examiner gives you a high level summary of these feature attributes, along with counts for the number of times they appear in the file:

import pprint
from BCBio.GFF import GFFExaminer
 
in_file = "your_file.gff"
examiner = GFFExaminer()
in_handle = open(in_file)
pprint.pprint(examiner.available_limits(in_handle))
in_handle.close()
{'gff_id': {('I',): 159,
            ('II',): 3,
            ('III',): 2,
            ('IV',): 5,
            ('V',): 2,
            ('X',): 6},
 'gff_source': {('Allele',): 1,
                ('Coding_transcript',): 102,
                ('Expr_profile',): 1,
                ('GenePair_STS',): 8,
                ('Oligo_set',): 1,
                ('Orfeome',): 8,
                ('Promoterome',): 5,
                ('SAGE_tag',): 1,
                ('SAGE_tag_most_three_prime',): 1,
                ('SAGE_tag_unambiguously_mapped',): 12,
                ('history',): 30,
                ('mass_spec_genome',): 7},
 'gff_source_type': {('Allele', 'SNP'): 1,
                     ('Coding_transcript', 'CDS'): 27,
                     ('Coding_transcript', 'exon'): 33,
                     ('Coding_transcript', 'five_prime_UTR'): 4,
                     ('Coding_transcript', 'gene'): 2,
                     ('Coding_transcript', 'intron'): 29,
                     ('Coding_transcript', 'mRNA'): 4,
                     ('Coding_transcript', 'three_prime_UTR'): 3,
                     ('Expr_profile', 'experimental_result_region'): 1,
                     ('GenePair_STS', 'PCR_product'): 8,
                     ('Oligo_set', 'reagent'): 1,
                     ('Orfeome', 'PCR_product'): 8,
                     ('Promoterome', 'PCR_product'): 5,
                     ('SAGE_tag', 'SAGE_tag'): 1,
                     ('SAGE_tag_most_three_prime', 'SAGE_tag'): 1,
                     ('SAGE_tag_unambiguously_mapped', 'SAGE_tag'): 12,
                     ('history', 'CDS'): 30,
                     ('mass_spec_genome', 'translated_nucleotide_match'): 7},
 'gff_type': {('CDS',): 57,
              ('PCR_product',): 21,
              ('SAGE_tag',): 14,
              ('SNP',): 1,
              ('exon',): 33,
              ('experimental_result_region',): 1,
              ('five_prime_UTR',): 4,
              ('gene',): 2,
              ('intron',): 29,
              ('mRNA',): 4,
              ('reagent',): 1,
              ('three_prime_UTR',): 3,
              ('translated_nucleotide_match',): 7}}

GFF Parsing

Basic GFF parsing

Generally, the GFF parser works similar to other parsers in Biopython. Calling parse with a handle to a GFF file returns a set of SeqRecord objects corresponding to the various IDs referenced in the file:

from BCBio import GFF
 
in_file = "your_file.gff"
 
in_handle = open(in_file)
for rec in GFF.parse(in_handle):
    print rec
in_handle.close()

Since a GFF file is not broken down into an explicit record structure, this requires reading the entire file, parsing all of the features, and then returning those as records. This will be fine for small files, but for most real life cases you will want to restrict parsing to a set of features of interest or a section of lines at once to conserve memory.

Limiting parsed lines

from BCBio import GFF
 
in_file = "your_file.gff"
 
limit_info = dict(
        gff_source = ["Coding_transcript"])
 
in_handle = open(in_file)
for rec in GFF.parse(in_handle, limit_info=limit_info):
    print rec.features[0]
in_handle.close()

Iterating over portions of a file

from BCBio import GFF
 
in_file = "your_file.gff"
 
in_handle = open(in_file)
for rec in GFF.parse(in_handle, target_lines=1000):
    print rec
in_handle.close()

Providing initial sequence records

from BCBio import GFF
from Bio import SeqIO
 
in_seq_file = "seqs.fa"
in_seq_handle = open(in_seq_file)
seq_dict = SeqIO.to_dict(SeqIO.parse(in_seq_handle, "fasta"))
in_seq_handle.close()
 
in_file = "your_file.gff"
in_handle = open(in_file)
for rec in GFF.parse(in_handle, base_dict=seq_dict):
    print rec
in_handle.close()

Writing GFF3

The GFF3Writer takes an iterator of SeqRecord objects, and writes each SeqFeature as a GFF3 line:

  • seqid -- SeqRecord ID
  • source -- Feature qualifier with key "source"
  • type -- Feature type attribute
  • start, end -- The Feature Location
  • score -- Feature qualifier with key "score"
  • strand -- Feature strand attribute
  • phase -- Feature qualifier with key "phase"

The remaining qualifiers are the final key/value pairs of the attribute.

A feature hierarchy is represented as sub_features of the parent feature. This handles any arbitrarily deep nesting of parent and child features.

from BCBio import GFF
from Bio import SeqIO
 
in_file = "your_file.gb"
out_file = "your_file.gff"
in_handle = open(in_file)
out_handle = open(out_file, "w")
 
GFF.write(SeqIO.parse(in_handle, "genbank"), out_handle)
 
in_handle.close()
out_handle.close()
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