skills/gptomics/bioskills/bio-format-conversion

bio-format-conversion

SKILL.md

Format Conversion

Convert sequence files between formats using Biopython's Bio.SeqIO module.

Required Import

from Bio import SeqIO

Core Function

SeqIO.convert() - Direct Conversion

Convert between formats in a single call. Most efficient method.

count = SeqIO.convert('input.gb', 'genbank', 'output.fasta', 'fasta')
print(f'Converted {count} records')

Parameters:

  • in_file - Input filename or handle
  • in_format - Input format string
  • out_file - Output filename or handle
  • out_format - Output format string

Returns: Number of records converted

Common Conversions

From To Notes
GenBank FASTA Loses annotations, keeps sequence
FASTA GenBank Need to add molecule_type
FASTQ FASTA Loses quality scores
FASTA FASTQ Need to add quality scores
GenBank EMBL Usually works directly
Stockholm FASTA Alignment to sequences

Code Patterns

Simple Conversion

SeqIO.convert('input.gb', 'genbank', 'output.fasta', 'fasta')

GenBank to FASTA

SeqIO.convert('sequence.gb', 'genbank', 'sequence.fasta', 'fasta')

FASTQ to FASTA (drop quality)

SeqIO.convert('reads.fastq', 'fastq', 'reads.fasta', 'fasta')

FASTA to GenBank (requires molecule_type)

records = SeqIO.parse('input.fasta', 'fasta')
def add_molecule_type(records):
    for record in records:
        record.annotations['molecule_type'] = 'DNA'
        yield record

SeqIO.write(add_molecule_type(records), 'output.gb', 'genbank')

FASTA to FASTQ (add dummy quality)

def add_quality(records, quality=30):
    for record in records:
        record.letter_annotations['phred_quality'] = [quality] * len(record.seq)
        yield record

records = SeqIO.parse('input.fasta', 'fasta')
SeqIO.write(add_quality(records), 'output.fastq', 'fastq')

Batch Convert Multiple Files

from pathlib import Path

for gb_file in Path('.').glob('*.gb'):
    fasta_file = gb_file.with_suffix('.fasta')
    count = SeqIO.convert(str(gb_file), 'genbank', str(fasta_file), 'fasta')
    print(f'{gb_file.name}: {count} records')

Convert with Modifications

from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord

def uppercase_record(rec):
    return SeqRecord(rec.seq.upper(), id=rec.id, description=rec.description)

records = SeqIO.parse('input.fasta', 'fasta')
modified = (uppercase_record(rec) for rec in records)
SeqIO.write(modified, 'output.fasta', 'fasta')

Alignment Format Conversion

from Bio import AlignIO

AlignIO.convert('alignment.sto', 'stockholm', 'alignment.phy', 'phylip')

Format Compatibility Matrix

Can convert directly (no modifications needed):

  • GenBank <-> EMBL
  • FASTA -> any format (may need annotations added)
  • Any format -> FASTA (always works, may lose data)
  • FASTQ -> FASTA

Requires adding data:

  • FASTA -> FASTQ (need quality scores)
  • FASTA -> GenBank (need molecule_type)

May lose data:

  • GenBank -> FASTA (loses features, annotations)
  • FASTQ -> FASTA (loses quality scores)
  • Any rich format -> FASTA

Common Errors

Error Cause Solution
ValueError: missing molecule_type FASTA to GenBank Add molecule_type annotation
ValueError: missing quality scores FASTA to FASTQ Add phred_quality to letter_annotations
KeyError: 'phred_quality' Wrong FASTQ variant Try 'fastq-sanger', 'fastq-illumina'

Decision Tree

Converting formats?
├── Simple conversion (no data changes)?
│   └── Use SeqIO.convert() directly
├── Need to add annotations?
│   └── Parse, modify records, then write
├── Need to transform sequences?
│   └── Parse, apply transformation, then write
└── Multiple files?
    └── Loop with SeqIO.convert() or batch generator

Related Skills

  • read-sequences - Parse sequences for custom conversion logic
  • write-sequences - Write converted sequences with modifications
  • batch-processing - Convert multiple files at once
  • compressed-files - Handle compressed input/output during conversion
  • alignment-files - For SAM/BAM/CRAM conversion, use samtools view
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