GFF Parsing

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(Writing GFF3)
(Writing GFF3)
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== Writing GFF3 ==
 
== Writing GFF3 ==
 +
The <code>GFF3Writer</code> 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.
 +
 
<python>
 
<python>
 
from BCBio.GFF import GFF3Writer
 
from BCBio.GFF import GFF3Writer

Revision as of 12:50, 28 September 2009

Contents

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

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()
{('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')]}
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

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

Providing initial sequence records

from BCBio.GFF import GFFParser
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"
parser = GFFParser()
in_handle = open(in_file)
for rec in parser.parse(in_handle, base_dict=seq_dict):
    print rec
in_handle.close()

Limiting parsed lines

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

Iterating over portions of a file

from BCBio.GFF import GFFParser
 
in_file = "your_file.gff"
parser = GFFParser()
 
in_handle = open(in_file)
for rec in parser.parse_in_parts(in_handle, target_lines=1000):
    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.GFF import GFF3Writer
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")
writer = GFF3Writer()
writer.write(SeqIO.parse(in_handle, "genbank"), out_handle)
 
in_handle.close()
out_handle.close()
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