Bio Seq Objects

Version Compatibility

Reference examples tested with: BioPython 1.83+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Seq Objects

Create and manipulate biological sequence objects using Biopython.

"Create a sequence object" → Wrap a raw string in a typed sequence container for biological operations.

• Immutable: Seq('ATGC') (BioPython) — string-like, supports complement/translate
• Mutable: MutableSeq('ATGC') (BioPython) — supports in-place edits
• Annotated: SeqRecord(Seq(...), id=...) (BioPython) — adds metadata for file I/O

Required Imports

from Bio.Seq import Seq, MutableSeq
from Bio.SeqRecord import SeqRecord

Core Objects

Seq - Immutable Sequence

The basic sequence object. Immutable like Python strings.

seq = Seq('ATGCGATCGATCG')

Seq objects support string-like operations:

len(seq)           # Length
seq[0]             # First base
seq[-1]            # Last base
seq[0:10]          # Slice (returns Seq)
str(seq)           # Convert to string
'ATG' in seq       # Membership test
seq.count('G')     # Count occurrences
seq.find('ATG')    # Find position (-1 if not found)
seq.upper()        # Uppercase
seq.lower()        # Lowercase
seq * 3            # Repeat sequence
seq.strip()        # Remove leading/trailing whitespace

MutableSeq - Mutable Sequence

For in-place modifications when performance matters.

mut_seq = MutableSeq('ATGCGATCG')
mut_seq[0] = 'C'              # Modify single position
mut_seq[0:3] = 'GGG'          # Replace slice
mut_seq.append('A')           # Add to end
mut_seq.insert(0, 'G')        # Insert at position
mut_seq.pop()                 # Remove and return last
mut_seq.remove('G')           # Remove first occurrence
mut_seq.reverse()             # Reverse in place

Convert between types:

seq = Seq(mut_seq)            # MutableSeq to Seq
mut_seq = MutableSeq(seq)     # Seq to MutableSeq

SeqRecord - Annotated Sequence

Sequence with metadata for file I/O and analysis.

record = SeqRecord(
    Seq('ATGCGATCG'),
    id='gene1',
    name='example_gene',
    description='An example gene sequence'
)

SeqRecord attributes:

record.seq           # The Seq object
record.id            # Identifier string
record.name          # Name string
record.description   # Description string
record.features      # List of SeqFeature objects
record.annotations   # Dict of annotations
record.letter_annotations  # Per-letter annotations (quality scores)
record.dbxrefs       # Database cross-references

SeqRecord Methods

Goal: Transform entire records while preserving metadata.

Approach: Use SeqRecord methods that return new records with features remapped to new coordinates.

# Reverse complement (preserves ID, updates features)
rc_record = record.reverse_complement(id='gene1_rc', description='reverse complement')

# Translate to protein (creates new SeqRecord with protein)
protein_record = record.translate(id='gene1_protein')

# Quick format output (returns string in file format)
fasta_str = record.format('fasta')
genbank_str = record.format('genbank')

Slicing preserves features (adjusted to new coordinates):

# Slice SeqRecord - features are clipped/adjusted automatically
subset = record[10:50]  # Features outside range are dropped

Code Patterns

Create Seq from String

dna = Seq('ATGCGATCGATCG')
rna = Seq('AUGCGAUCGAUCG')
protein = Seq('MRCRS')

Create SeqRecord for File Output

record = SeqRecord(Seq('ATGCGATCG'), id='seq1', description='My sequence')

Create SeqRecord with Annotations

record = SeqRecord(Seq('ATGCGATCG'), id='gene1', description='Example')
record.annotations['organism'] = 'Homo sapiens'
record.annotations['molecule_type'] = 'DNA'

Build SeqRecord from Parsed Data

from Bio.SeqFeature import SeqFeature, FeatureLocation

record = SeqRecord(Seq('ATGCGATCGATCG'), id='gene1')
feature = SeqFeature(FeatureLocation(0, 9), type='CDS', qualifiers={'product': ['Example protein']})
record.features.append(feature)

Batch Create SeqRecords

sequences = ['ATGC', 'GCTA', 'TTAA']
records = [SeqRecord(Seq(s), id=f'seq_{i}') for i, s in enumerate(sequences)]

Copy a SeqRecord

from copy import deepcopy
new_record = deepcopy(record)
new_record.id = 'modified_copy'

Modify SeqRecord Sequence

record = SeqRecord(Seq('ATGCGATCG'), id='seq1')
record.seq = Seq('GGGGGATCG')  # Replace entire sequence

Join Sequences into One SeqRecord

combined_seq = seq1 + Seq('NNNN') + seq2  # With linker
combined_record = SeqRecord(combined_seq, id='combined')

Transform SeqRecord with reverse_complement

# Reverse complement a gene sequence
record = SeqRecord(Seq('ATGCGATCGATCG'), id='gene1', description='Forward strand')
rc_record = record.reverse_complement(id=f'{record.id}_rc', description='Reverse complement')
# Features are remapped to new coordinates

Translate SeqRecord to Protein

# Translate coding sequence
cds_record = SeqRecord(Seq('ATGCGATCGATCGTAA'), id='cds1', description='Coding sequence')
protein_record = cds_record.translate(id=f'{cds_record.id}_protein', to_stop=True)

Quick Output with format()

record = SeqRecord(Seq('ATGCGATCG'), id='seq1', description='Example sequence')
print(record.format('fasta'))
# >seq1 Example sequence
# ATGCGATCG

Common Errors

Decision Tree

Need to work with sequence data?
├── Just doing string-like operations?
│   └── Use Seq
├── Need to modify sequence in-place?
│   └── Use MutableSeq
├── Need metadata (ID, description, features)?
│   └── Use SeqRecord
└── Need to write to file?
    └── Use SeqRecord with appropriate annotations

Related Skills

• sequence-io/read-sequences - Parse files to get SeqRecord objects
• sequence-io/write-sequences - Write SeqRecord objects to files
• transcription-translation - Transform Seq objects (DNA to protein)
• reverse-complement - Get reverse complement of Seq
• sequence-slicing - Slice and extract from Seq/SeqRecord
• database-access/entrez-fetch - Fetch sequences from NCBI as SeqRecords